Deep Learning Study
A curated list of Deep Learning, Reinforcement Learning, Machine Learning, Data Science, Recommendation, Chatbot
Deep Learning
- Tutorial & Lecture
- 홍콩 과기대 김성훈 교수님의 모두의 딥러닝
- Deep Learning Tutorial from Tensorflow Blog
- Andrew Ng's Coursera Machine Learning
- Stanford - CS231n: Convolutional Neural Networks for Visual Recognition : [Video], [Korean], [Video - Korean], [Korean - KNU]
- Stanford - CS224n: Deep Learning for Natural Language Processing : [Video]
- Stanford - Unsupervised Feature Learning and Deep Learning Tutorial
- Stanford - Tensorflow for Deep Learning Research
- Stanford - Theories of Deep Learning [STATS 385]
- MIT - 6.S191: Introduction to Deep Learning
- MIT - 6.S094: Deep Learning for Self-Driving Cars
- Oxford - Deep NLP 2017 course
- Deep learning courses at UC Berkeley
- T81-558:Applications of Deep Neural Networks
- MILA - DEEP LEARNING AND REINFORCEMENT LEARNING SUMMER SCHOOL 2017 : [Video]
- CS 598 LAZ: Cutting-Edge Trends in Deep Learning and Recognition
- KAIST Machine Learning Lecture
- Udacity - Deep Learning by Google
- Python Deep Learning with Keras - Machine Learning Mastery
- Practical Deep Learning For Coders—18 hours of lessons for free
- Deep Learning for Speech and Language
- 동국대 홍정모 교수님의 C++로 배우는 딥러닝
- Enjoy DL
- Laon People 머신러닝/딥러닝 블로그
- TensorFlow Slim 실습
- TensorFlow Workshop
- TensorFlow Tutorials
- TensorFlow Tutorial : [Video]
- Machine Learning & Deep Learning
- T아카데미 인공지능을 위한 머신러닝 알고리즘 강의
- Deep Learning course: lecture slides and lab notebooks - Master Datascience Paris Saclay
- Learning Tensorflow - Beginner-level tutorials for a powerful framework
- Tensorflow for Deep Learning : [Video]
- 텐서플로우 기초 이해하기
- Effective Tensorflow
- Introduction to Deep Neural Networks with Keras and Tensorflow
- PyTorch로 시작하는 딥러닝 입문 CAMP 1기 강의자료
- 패스트캠퍼스 Deep Learning 강의 자료
- 딥러닝 교육 자료
- Keras 강의 - CodeOnWeb
- DeepSchool.io - Deep Learning tutorials in jupyter notebooks
- Deep Learning Course - PyTorch
- TensorFlow Tutorial and Examples for Beginners with Latest APIs
- PyTorch Zero To All
- FastCampus Deep Learning NLP Chatbot
- 최신 논문으로 시작하는 딥러닝 - 최성준님 : [Code]
- Everybody Tensorflow
- 이찬우님의 패스트 캠퍼스 TensorFlow 딥러닝 강의자료
- 1. Machine Learning Basic, Linear Regression, Logistic Regression
- 2. Feed Forward Neural Network
- 3. Pipeline, TFRecord, Queue Runners, Dataset Framework
- 4. Convolutional Neural Network
- 5. Recurrent Neural Network
- 6. RNN Cells, Advanced RNNs
- 7. High Level APIs, Estimator, Experiment
- 8. Word2vec, GAN Basic
- 딥러닝 이론에서 실습까지 - 엑셈
- Easy-deep-learning-with-Keras
- AI Student Kits - Intel Academy
- Kaggle - Hands-On Data Science Education
- Google - SuperComputing 2017 Deep Learning Tutorial
- Google - Machine Learning Crash Course with TensorFlow APIs
- Google - Machine Learning Practica
- Lecture Slides for Deeplearning book
- Microsoft Professional Program for Data Science track
- Microsoft Professional Program for Artificial Intelligence track
- Edwith - 인공지능을 위한 선형대수
- Edwith - 머신러닝을 위한 Python
- Edwith - Bayesian Deep Learning
- 딥러닝 퀵스타트 : 파이토치편
- Open Machine Learning Course
- 텐서플로 강의 - 이찬우님
- Machine learning in Python with scikit-learn : [Code]
- Natural Language Processing with PyTorch
- PyTorch-Deep-Learning-Minicourse : [Video]
- Community
- TensorFlow KR Facebook Group
- AI Korea Facebook Group
- AI Korea
- AI Korea Reddit
- 텐서플로우 블로그
- Machine Learning Reddit
- Deep Learning Facebook Group
- Deep AI Facebook Group
- 모두의 연구소 커뮤니티 Facebook Group
- 모두의 연구소
- KERAS.AI Facebook Group
- Bigdata Machine Learning Facebook Group
- Big Data Korea Facebook Group
- 딥러닝 솔루션 그룹 Facebook Group
- AI DEV 인공지능 개발자 모임
- Distill - Machine Learning Research Journal
- ArxivSanityKr
- Towards Data Science - Sharing concepts, ideas, and codes.
- INSIGHT - Your bridge to careers in Data Science and Data Engineering
- 카카오 AI 매거진
- HillClimber.ai - a curated machine learning mashup
- MyBridge - Machine Learning Top 10 Articles For the Past Month
- Datascience+ - An online community for showcasing R & Python tutorials
- Data School - Launch a data science career!
- Papers with Code
- explained.ai - Deep explanations of machine learning and related topics
- Article
- Andrej Karpathy's Deep Learning Blog
- 머신러닝 딥러닝 입문 시 도움 되는 강좌
- 딥러닝 입문자용 글 모음
- 딥러닝 공부 방법
- 딥러닝 공부를 처음 시작 하는 초심자가 꼭 공부 해야 하는 것이 아닌 것
- Practical seq2seq
- New York University Deep Learning Natural Language Processing Lecture Note
- Intro into Keras and Image Classification : [Video]
- The Black Magic of Deep Learning - Tips and Tricks for the practitioner
- How a Japanese cucumber farmer is using deep learning and TensorFlow
- [개앞맵시] 스카이넷도 딥러닝부터
- Keras 강좌
- Coding a Deep Neural Network to Steer a Car: Step By Step
- Torch와 OpenCV를 활용한 실시간 이미지 분류 데모
- Variational Autoencoders Explained
- Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)
- 이슈카님의 딥러닝 블로그 : CS231n
- Hama님의 딥러닝 블로그
- A Machine Learning Craftsmanship Blog
- DeepLAB - [머신러닝레볼루션] RNN과 LSTM - 쫄지말자 딥러닝
- DeepMind just published a mind blowing paper: PathNet
- Deep Learning for Noobs [Part 2] – Hacker Noon
- MNIST Generative Adversarial Model in Keras
- Image Recognition in Python with Keras
- 유재준님의 딥러닝 블로그
- Food Classification with Deep Learning in Keras / Tensorflow
- Accelerating Deep Learning with Multiprocess Image Augmentation in Keras
- Introduction to deep learning for machine vision tasks using Keras
- The AWS Deep Learning AMI, Now with Ubuntu
- Intel’s BigDL on Databricks Distributed deep learning on Apache Spark
- Deep Learning Research Review: Natural Language Processing
- Getting Started with Tensorflow
- 최근우님의 딥러닝 블로그
- 전상혁님의 머신러닝/딥러닝 블로그
- Gunho Choi님의 딥러닝 큐레이션 리스트
- nthought님의 딥러닝/데이터마이닝 블로그
- KH님의 딥러닝 블로그
- Deep Learning and Machine Learning Guide: Part I
- Deep Learning and Machine Learning Guide: Part II
- Deep Learning and Machine Learning Guide: Part III
- Deep Learning 학습 자료 정리
- Deep Learning with Keras
- Activation Function
- Deep Learning Conference 후기
- Building an Image Classification Web Application Using VGG-16
- PREPARING A LARGE-SCALE IMAGE DATASET WITH TENSORFLOW'S TFRECORD FILES
- Distributed Deep Learning with Apache Spark and Keras
- 내가 찾은 Deep Learning 공부 최단경로
- PyTorch MNIST Example
- CNN 역전파를 이해하는 가장 쉬운 방법
- Recurrent Neural Network(RNN)과 LSTM
- Data Science와 TensorFlow Study 정리 : Data Science와 TensorFlow Study Blog
- Learn TensorFlow and deep learning, without a Ph.D
- Visualizing parts of Convolutional Neural Networks using Keras and Cats
- Machine Learning is Fun!
- Machine Learning is Fun! The world’s easiest introduction to Machine Learning : [Korean]
- Machine Learning is Fun! Part 2 Using Machine Learning to generate Super Mario Maker levels : [Korean]
- Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks : [Korean]
- Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning : [Korean]
- Machine Learning is Fun Part 5: Language Translation with Deep Learning and the Magic of Sequences : [Korean]
- Machine Learning is Fun Part 6: How to do Speech Recognition with Deep Learning
- Machine Learning is Fun Part 7: Abusing Generative Adversarial Networks to Make 8-bit Pixel Art
- 딥러닝을 이용한 주가 예측
- 솔라리스의 인공지능 연구실
- Directory of tutorials and open-source code repositories for working with Keras, the Python deep learning library
- Using Caffe with your own dataset
- Sang-Kil Park님의 딥러닝 블로그
- Image Classification and Segmentation with Tensorflow and TF-Slim
- Reuters-21578 text classification with Gensim and Keras
- How to Set Up a Deep Learning Environment on AWS with Keras/Thean
- Bumjun Kim님의 딥러닝 블로그
- Generative Adversarial Networks – Hot Topic in Machine Learning
- 조대협님의 머신러닝/딥러닝 블로그
- RNN(Recurrent Neural Network)과 Torch로 발라드곡 작사하기
- 모두의 딥러닝 강의 정리
- Arthur Juliani's Deep Learning Blog
- Tutorial: Optimizing Neural Networks using Keras (Image recognition)
- A curated list of resources related to NLP (Natural Language Processing) for Korean + NLP resources in Korean
- 딥러닝과 에스프레소북 그리고 이것저것들
- Adit Deshpande's Deep Learning Blos
- Keras Tutorial: The Ultimate Beginner's Guide to Deep Learning in Python
- LSTM(RNN) 소개
- 엑소사랑하자 - OpenFace로 우리 오빠들 얼굴 인식하기
- Deep Learning Papers Reading Roadmap
- [번역] A Beginner's Guide To Understanding Convolutional Neural Networks
- RNNS IN TENSORFLOW, A PRACTICAL GUIDE AND UNDOCUMENTED FEATURES
- Image Completion with Deep Learning in TensorFlow
- DeepLearning Ninja001 - Hello Tensorflow
- 딥러닝을 처음 시작하는 분들을 위해
- List of Pycon2016 session related with ML
- Awesome - Most Cited Deep Learning Papers
- 테리님의 딥러닝 블로그
- Machine Learning & Deep Learning Tutorials
- Deep Learning for Dummies, Carey Nachenberg
- TensorFlow-v1.0.0 + Keras 설치 (Windows/Linux/macOS)
- Deep Learning based Detection
- LSTM 과 ResNet
- TensorFlow: How to optimise your input pipeline with queues and multi-threading
- Image denoising with Autoencoder in Keras
- How to Build an Image Classification Web App With VGG-16
- Deep Learning Project Workflow
- [AI기획]경쟁 통해 배우는 인공지능 기술 GAN
- How these researchers tried something unconventional to come out with a smaller yet better Image Recognition
- Understanding Neural Networks Through Deep Visualization
- Picking an optimizer for Style Transfer
- Deep Learning with Keras on Google Compute Engine
- Clickbaits Revisited: Deep Learning on Title + Content Features to Tackle Clickbaits
- 텐서플로우 시작하기
- Baidu released PaddlePaddle Jupyter notebook
- ratsgo님의 블로그
- Faster R-CNN
- TensorFlow RNN Tutorial
- Build Your Own Text-to-Speech Applications with Amazon Polly
- Five video classification methods implemented in Keras and TensorFlow
- Build a talking, face-recognizing doorbell for about $100
- Deep Learning for Vision Guided Language And Image Generation
- 텐서보드 - TensorBoard 시작하기
- Classifying White Blood Cells With Deep Learning
- Diving Into Natural Language Processing
- Deep Learning with Emojis - not Math
- 겐[GANs]이 혁신할 인공지능 번역 기술
- 고려대학교 Deep Learning 세미나
- Awesome-Pytorch-list
- Artificial Intelligence GitBook
- Deploy Deep Learning Models on Amazon ECS
- DeepLAB : [논문반/논문세미나] SEGAN : Speech Enhancement Generative Adversarial Network
- awesome-deep-vision-web-demo
- Introducing tf-seq2seq: An Open Source Sequence-to-Sequence Framework in TensorFlow
- Kaggle DSTL Competition
- 14 DESIGN PATTERNS TO IMPROVE YOUR CONVOLUTIONAL NEURAL NETWORKS
- MXNet을 활용한 이미지 분류 앱 개발하기
- Tensorflow Tutorial 2: image classifier using convolutional neural network
- Rohan & Lenny #3: Recurrent Neural Networks & LSTMs
- Awesome-korean-nlp
- Deep learning for satellite imagery via image segmentation
- 지능형 한국어 형태소 분석기 - Korean Intelligent Word Identifier
- Transfer Learning using Keras
- Agustinus Kristiadi's Blog [GAN]
- Everything about Self Driving Cars Explained for Non-Engineers
- Kaggle Data Science Bown 2017 참가기[지능정보기술연구원]
- The GAN Zoo
- THE NEURAL NETWORK ZOO
- Classification datasets results
- Deeplunch팀의 Kaggle Data Science Bowl 도전기[1] - 케글 도전 팁
- A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN
- Running BigDL, Deep Learning for Apache Spark, on AWS
- ImageNet: VGGNet, ResNet, Inception, and Xception with Keras
- TensorFlow: A proposal of good practices for files, folders and models architecture
- The Modern History of Object Recognition — Infographic
- Learning Deep Learning with Keras
- Deep Learning: Language identification using Keras & TensorFlow
- Deep Learning Papers by task
- Deep Learning Tutorials for 10 Weeks
- Keras Tutorial: Deep Learning in Python
- 2nd place solution for the 2017 national datascience bowl
- Deep learning for complete beginners: convolutional neural networks with keras
- Deep Learning으로 학습된 Object Detection Model 에 대해 정리한 Archive
- Face recognition with Keras and OpenCV
- Image segmentation with Neural Net
- GANs - Generative Adversarial Networks
- Neural networks for algorithmic trading 1.2 — Correct time series forecasting + backtesting
- 22 must watch talks on Python for Deep Learning, Machine Learning & Data Science - from PyData 2017, Amsterdam
- 라즈베리파이기반 TensorFlow 사물인식 로봇
- 라즈베리파이기반 YOLO 사물인식 로봇
- Deep Learning #3: More on CNNs & Handling Overfitting
- pyTorch Tutorials
- fast.ai: How I built a deep learning application to detect invasive species in just 1 day and for $12.60
- Picasso: A free open-source visualizer for Convolutional Neural Networks
- Using Machine Learning to Explore Neural Network Architecture
- Convolutional Methods for Text
- Applying deep learning to real-world problems
- Using TensorFlow to build image-to-text application
- Your tl;dr by an ai: a deep reinforced model for abstractive summarization
- Practical UseCases of Deep Learning Techniques… Part II
- Caption this, with TensorFlow
- Image Segmentation using deconvolution layer in Tensorflow
- Exploring LSTMs
- [YOLO DARKNET] 구성 및 설치, 사용방법
- You can probably use deep learning even if your data isn't that big
- TensorFlow for Hackers
- TensorFlow Basics — TensorFlow for Hackers Part I
- Building a Simple Neural Network — TensorFlow for Hackers Part II
- Building a Cat Detector using Convolutional Neural Networks — TensorFlow for Hackers Part III
- Neural Network from Scratch — TensorFlow for Hackers Part IV
- Making a Predictive Keyboard using Recurrent Neural Networks — TensorFlow for Hackers Part V
- Human Activity Recognition using LSTMs on Android — TensorFlow for Hackers Part VI
- Visualizing TensorFlow Graphs in Jupyter Notebooks
- Safe Crime Prediction
- A neural approach to relational reasoning
- Neural Translation of Musical Style
- RNN을 이용한 한글 자동 띄어쓰기
- Object detection with neural networks — a simple tutorial using keras
- GAN by Example using Keras on Tensorflow Backend
- Supercharge your Computer Vision models with the TensorFlow Object Detection API
- Stacking Made Easy: An Introduction to StackNet by Competitions Grandmaster Marios Michailidis - KazAnova
- Generative Adversarial Networks for Beginners
- Accelerating Deep Learning Research with the Tensor2Tensor Library
- Building a Real-Time Object Recognition App with Tensorflow and OpenCV
- How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow, Keras & React Native
- How to Visualize Your Recurrent Neural Network with Attention in Keras
- Interpreting neurons in an LSTM network
- 머신러닝 실습 with Tensorflow
- Pytorch를 사용한 단 50줄로 코드로 짜보는 GAN
- DeepMind’s Relational Reasoning Networks — Demystified
- Artificial Inteligence
- How to deploy Machine Learning models with TensorFlow. Part 2— containerize it!
- Predicting the Success of a Reddit Submission with Deep Learning and Keras
- CycleGAN : Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks - 컨셉
- Find Distinct People in a Video with Amazon Rekognition
- TensorFlow Neural Machine Translation Tutorial
- Galaxy Zoo classification with Keras
- 김태희의 닮은 꼴도 머신러닝으로 구분할 수 있을까?
- An end to end implementation of a Machine Learning pipeline
- Debugging & Visualising training of Neural Network with TensorBoard
- Deploy Tensorflow Docker Image to AWS ECS
- Perform sentiment analysis with LSTMs, using TensorFlow
- Textboxes - 2016 : Image Text Detection 논문 리뷰
- 37 Reasons why your Neural Network is not working
- 37 Reasons why your Neural Network is not working 번역
- A Step-by-Step Guide to Synthesizing Adversarial Examples
- Deep Learning for NLP Best Practices
- Exploiting the Unique Features of the Apache MXNet Deep Learning Framework with a Cheat Sheet
- How to train your own Object Detector with TensorFlow’s Object Detector API
- Classifying traffic signs with Apache MXNet: An introduction to computer vision with neural networks
- Towards Next Generation Deep Learning Framework - An Introduction to MXNet/Gluon
- A gentle introduction to Doc2Vec
- A non-NLP application of Word2Vec
- Deep Learning #4: Why You Need to Start Using Embedding Layers
- Apache MXNet에 대한 모든 것!
- MXNet 기반 추천 오픈 소스 딥러닝 프로젝트 모음
- 클라우드에 딱 맞는 MXNet의 5가지 딥러닝 학습 기능
- Applying Deep Learning to Time Series Forecasting with TensorFlow
- Classifying e-commerce products based on images and text
- Autoencoders — Bits and Bytes of Deep Learning
- TensorFlow Photo x-Ray Object Detection with App Engine
- Seq2Seq - ICML17 Tutorial
- Jamie Kang님의 머신러닝 블로그
- Seamlessly Scale Predictions with AWS Lambda and MXNet
- Deep Learning on AWS Batch
- Twitter Sentiment Analysis with Gensim Word2Vec and Keras Convolutional Networks
- Using AI to Super Compress Images
- Where’s Waldo : Terminator Edition
- Vanishing Gradient Problem
- Estimating the Location of Images Using MXNet and Multimedia Commons Dataset on AWS EC2
- Captioning Novel Objects in Images
- Training MXNet
- Image Augmentation for Deep Learning using Keras and Histogram Equalization
- Learn.AI님의 GAN 정리
- 옹쿠님의 Deep Learning 블로그
- Getting Up and Running with PyTorch on Amazon Cloud
- Credit Card Fraud Detection using Autoencoders in Keras — TensorFlow for Hackers Part VII
- Building a Facial Recognition Pipeline with Deep Learning in Tensorflow
- Generative Adversarial Networks [GANs]: Engine and Applications
- Machine Learning for Humans
- 이찬우님의 Deep Learning Blog
- [Lecture] How to build a recognition system - Part 1: best practices
- [Lecture] Evolution: from vanilla RNN to GRU & LSTMs
- Connecting the dots for a Deep Learning App
- An Intuitive Guide to Deep Network Architectures
- Secret Sauce behind the beauty of Deep Learning: Beginners guide to Activation Functions
- Tensorflow Object Detection API Tutorial
- A Deep Learning Based AI for Path of Exile: A Series
- Deploying your Keras model using Keras.JS
- Learning GAN
- A Word2Vec Keras tutorial
- Neural Networks Part 2: Implementing a Neural Network function in python using Keras
- Tutorial - What is a variational autoencoder?
- 2017 beginner's review of GAN architectures
- My Neural Network isn't working! What should I do?
- Keras shoot-out: TensorFlow vs MXNet
- Applied Deep Learning
- BigData와 결합한, 분산 Deep Learning 그 의미와 접근 방법에 대하여
- Deep Learning with Intel’s BigDL and Apache Spark
- My Workflow of Supervised Learning - 지도학습의 자세한 나만의 워크플로우
- Python gensim Word2Vec tutorial with TensorFlow and Keras
- Time Series Prediction Using Recurrent Neural Networks [LSTMs]
- GCP ML 엔진 튜토리얼: 텐서플로우 고수준 API로 작성된 CIFAR-10 모델의 초모수 최적화 하기
- Familiarization of Sequence to Sequence model in Deep Learning
- Understanding LSTM in Tensorflow[MNIST dataset]
- Deep Learning for Object Detection: A Comprehensive Review
- Detecting Malicious Requests with Keras & Tensorflow
- Recognizing Game Genres From Screenshots using CNNs
- Deep Learning with Intel’s BigDL and Apache Spark
- Keras Tutorial: Content Based Image Retrieval Using a Convolutional Denoising Autoencoder
- How to write distributed TensorFlow code — with an example on TensorPort
- Build your own Machine Learning Visualizations with the new TensorBoard API
- Gradient Trader Part 1: The Surprising Usefulness of Autoencoders
- Create self-driving trucks inside Euro Truck Simulator 2
- Dealing with Unbalanced Classes in Machine Learning
- Introduction to TensorFlow Datasets and Estimators
- Higher-Level APIs in TensorFlow
- Building a Toy Detector with Tensorflow Object Detection API
- 딥러닝 기반 자연어처리 기법의 최근 연구 동향
- Recurrent Neural Network [RNN] 이해하기
- Wasserstein GAN in Keras
- PyTorch tutorial distilled
- Tensorpack과 Multigpu를 활용한 빠른 트레이닝 코드 작성하기
- ‘Image Classification’ Outline
- A ten-minute introduction to sequence-to-sequence learning in Keras
- A new kind of pooling layer for faster and sharper convergence
- Understanding emotions — from Keras to pyTorch
- TensorFlow Datasets 및 Estimators를 소개합니다.
- Visualizing your model using TensorBoard
- Towards data set augmentation with GANs
- TensorFlow in a Nutshell
- Introducing NNVM Compiler: A New Open End-to-End Compiler for AI Frameworks
- Vanilla LSTM with numpy
- Sentiment analysis with Apache MXNet
- Question answering with TensorFlow
- Recurrent neural networks and LSTM Tutorial in Python and TensorFlow
- Serving PyTorch Models on AWS Lambda with Caffe2 & ONNX
- Behind the Magic: How we built the ARKit Sudoku Solver
- TensorFlow Lattice: Flexibility Empowered by Prior Knowledge
- 딥러닝과 OpenCV를 활용해 사진 속 글자 검출하기
- 옥수별님의 머신러닝/딥러닝 블로그
- Neural Networks for Advertisers
- Recurrent Neural Networks for Email List Churn Prediction
- Tensorflow Text Classification – Python Deep Learning
- D.Voice: 딥러닝 음성 합성 엔진
- Video Analysis to Detect Suspicious Activity Based on Deep Learning
- Building a Translation System In Minutes
- Google and Uber’s Best Practices for Deep Learning
- Introducing Gluon — An Easy-to-Use Programming Interface for Flexible Deep Learning : [번역]
- Gender Distribution in North Korean Posters
- Attention in Neural Networks and How to Use It
- TF-Slim 시작하기
- Improving Real-Time Object Detection with YOLO
- How to unit test machine learning code
- Batch normalization in Neural Networks
- Dog Breed Classification using Deep Learning: hands-on approach
- 레진 데이터 챌린지 2017
- Distributed training in the cloud: Cloud Machine Learning Engine
- Object detection with TensorFlow
- Simple MNIST Autoencoder in TensorFlow
- What is a CapsNet or Capsule Network?
- Latest Deep Learning OCR with Keras and Supervisely in 15 minutes
- Machine Learning Meets Fashion
- [카카오AI리포트]딥러닝과 데이터
- CapsuleNet on MNIST
- How do CNNs Deal with Position Differences? : [번역]
- 옹쿠님의 Capsule Network 정리
- NVIDIA DIGITS 알아보기!
- Getting Started with the AWS Deep Learning Conda and Base AMIs
- Announcing ONNX Support for Apache MXNet
- TechtreeAI - AI 학습법
- Dynamic Routing Between Capsules - 캡슐 간 동적 라우팅
- 10 more Deep Learning projects based on Apache MXNet
- Keras + Horovod = Distributed Deep Learning on Steroids
- Run Deep Learning Frameworks with GPU Instance Types on Amazon EMR
- [번역] Go와 Tensorflow로 이미지 인식 API 만들기
- TensorFlow Lite 101 - MoblieNet 맛보기
- 딥러닝을 제대로 이해하기 위해서 필요한 배경지식맵
- 여러가지 합성곱 신경망 레이어들 - InceptionV1[Googlenet]
- CNN in numpy
- Serving TensorFlow Models. Serverless
- Distributed TensorFlow: A Gentle Introduction
- Structured Deep Learning
- Amazon SageMaker – Accelerating Machine Learning : [번역]
- AWS SageMaker: AI’s Next Game Changer
- How to Build a Real-time Hand-Detector using Neural Networks [SSD] on Tensorflow
- How to Find Wally with a Neural Network
- Using T-SNE to Visualise how your Model thinks
- SuaLab Research Blog - Deep Learning, Computer Vision
- Grad CAM을 이용한 딥러닝 모형 해석
- Gluon을 이용한 Grad CAM
- seq2seq기반 덧셈 모형 빌드[with Gluon]
- 전이학습[transfer learning]으로 모형 재사용하기 [Gluon 기반]
- 딥러닝이 덧셈을 하는 방법, Attention Mechanism으로 살펴보기[Gluon]
- AWS Contributes to Milestone 1.0 Release of Apache MXNet Including the Addition of a New Model Serving Capability : [번역]
- Introducing Model Server for Apache MXNet
- How to Generate Music using a LSTM Neural Network in Keras
- Transfer learning from multiple pre-trained computer vision models
- Deploying Object Detection Model with TensorFlow Serving
- Building an Automated Image Captioning Application
- Serverless deep/machine learning in production — the pythonic way
- TFGAN: A Lightweight Library for Generative Adversarial Networks
- AWS Lambda에 Tensorflow/Keras 배포하기
- GCP CloudML
- Leveraging Low Precision and Quantization for Deep Learning Using the Amazon EC2 C5 Instance and BigDL
- Fine-tuning Convolutional Neural Network on own data using Keras Tensorflow
- Backpropagation – Algorithm For Training A Neural Network
- gradient를 활용한 DNN 해석 방안
- Building a spam classifier: PySpark+MLLib vs SageMaker+XGBoost
- CNN을 이용한 얼굴 분류기
- TensorFlow Lite를 사용한 온디바이스 대화형 모델링에 대해 확인해 보세요
- Real-time forecasts in the cloud: from market feed capture to ML predictions
- 당근마켓에서 딥러닝 활용하기
- Deep Learning Inference & Serving Architecture 를 위한 실험 및 고찰 1 - GPU vs CPU
- Deep Learning Multi Host & Multi GPU Architecture 고찰 및 구성 1
- Deep Learning Multi Host & Multi GPU Architecture #2 - Keras 를 이용한 Scale Up, Horovod 를 이용한 Scale Out 성능 비교
- One-Shot Learning: Face Recognition using Siamese Neural Network
- Neural Networks with Google CoLaboratory | Artificial Intelligence Getting started
- How to Deploy Deep Learning Models with AWS Lambda and Tensorflow
- Zero to Hero: Guide to Object Detection using Deep Learning: Faster R-CNN,YOLO,SSD
- Object Detection using Single Shot Multibox Detector
- Release Of A New Machine Learning Toolkit By Kubernetes: KubeFlow
- AI and Deep Learning in 2017 – A Year in Review : [번역]
- Predicting Cryptocurrency Price With Tensorflow and Keras
- Build a Taylor Swift detector with the TensorFlow Object Detection API, ML Engine, and Swift
- How to break a CAPTCHA system in 15 minutes with Machine Learning
- Build Amazon SageMaker notebooks backed by Spark in Amazon EMR
- Predicting Cryptocurrency Price With Tensorflow and Keras
- Now available in Amazon SageMaker: DeepAR algorithm for more accurate time series forecasting
- Neural Machine Translation — Using seq2seq with Keras
- Turning Design Mockups Into Code With Deep Learning
- Deep Image Retrieval
- Fitting larger networks into memory
- Ideas for 9th Kaggle TensorFlow Speech Recognition Challenge
- Freeze Tensorflow models and serve on web
- How To Create Data Products That Are Magical Using Sequence-to-Sequence Models
- Basics of image classification with Keras
- [Deep Learning - GAN] Simple Generative Adversarial Network with MNIST dataset
- [Keras] A thing you should know about Keras if you plan to train a deep learning model on a large dataset
- TensorFlow에서 커스텀 Estimator를 만드는 방법에 대해 확인해 보세요
- AI 기반 스마트 폰의 명암 [The light and dark of AI-powered smartphones]
- Real Time Object Detection with TensorFlow Detection Model
- Financial forecasting with probabilistic programming and Pyro
- Anomaly detection with Apache MXNet
- Tensorflow: Kaggle Spooky Authors Bag of Words Model
- Google Colab Free GPU Tutorial
- How to use Detectron — Facebook’s Free Platform for Object Detection
- Digging into AWS SageMaker — First Look
- Predicting world temperature with time series and DeepAR on Amazon SageMaker
- Only Numpy: Implementing GAN [General Adversarial Networks] and Adam Optimizer using Numpy with Interactive Code. [Run GAN Online]
- Introduction to LSTMs with TensorFlow
- fast.ai : the BEST things in life are always FREE
- How to use Dataset in TensorFlow
- Introducing capsule networks
- Logo detection using Apache MXNet
- Using Deep Learning for Structured Data with Entity Embeddings
- Getting Text into Tensorflow with the Dataset API
- Machine Learning with TensorFlow on Google Cloud Platform: code samples
- Build generative models using Apache MXNet
- How to generate realistic yelp restaurant reviews with Keras
- Deep learning in production with Keras, Redis, Flask, and Apache
- TensorFlow Object Detection in Action
- How to predict Bitcoin and Ethereum price with RNN-LSTM in Keras
- Keras Tutorial: Deep Learning in Python
- Gluon으로 구현해보는 한영기계번역 모형 – 마이크로소프트웨어 기고문
- The Building Blocks of Interpretability
- Predicting e-sports winners with Machine Learning - Hero2vec: Embeddings are all you need
- Automatic feature engineering using deep learning and Bayesian inference
- How I implemented iPhone X’s FaceID using Deep Learning in Python
- Convolutional Neural Networks with TensorFlow
- Deploy TensorFlow models
- GPU EC2 스팟 인스턴스에 Cuda/cuDNN와 Tensorflow/PyTorch/Jupyter Notebook 세팅하기
- How to train custom Word Embeddings using GPU on AWS
- Understanding Capsule Networks — AI’s Alluring New Architecture : [Code]
- Introducing TensorFlow Model Analysis: Scaleable, Sliced, and Full-Pass Metrics
- Introducing TensorFlow Hub: A Library for Reusable Machine Learning Modules in TensorFlow
- Introducing TensorFlow.js: Machine Learning in Javascript
- Entity extraction using Deep Learning
- Five video classification methods implemented in Keras and TensorFlow
- Implementing Autoencoders in Keras: Tutorial
- [Keras] 케라스로 풀어보는 다변수 입력에 대한 선형회귀 예제 - 나이, 체중에 대한 혈액지방함량 문제 -
- 파이썬 손코딩으로 하는 딥러닝 - MNIST
- Deep Learning With Apache Spark — Part 1
- Deep Learning With Apache Spark — Part 2
- Automated front-end development using deep learning
- Building a simple Keras + deep learning REST API
- A 60-minute Gluon Crash Course
- Text Classification with TensorFlow Estimators
- Data2Vis: Automatic Generation of Data Visualizations Using Sequence-to-Sequence Recurrent Neural Networks
- CIFAR-10 Image Classification in TensorFlow
- GAN with Keras: Application to Image Deblurring
- Diabetes Prediction — Artificial Neural Network Experimentation
- A Beginner's Guide to Object Detection
- Visualizing Artificial Neural Networks [ANNs] with just One Line of Code
- First time with Kaggle: A ConvNet to classify toxic comments with Keras
- Stock Market Predictions with LSTM in Python
- Introduction to Deep Learning with Keras
- Real-time Human Pose Estimation in the Browser with TensorFlow.js
- Naver Tech Talk: 오토인코더의 모든 것 [2017년 11월]
- Learning Entity Embeddings in one breath
- Neural Style Transfer 따라하기
- Demystifying Generative Adversarial Nets [GANs]
- PyTorch로 딥러닝하기: 60분만에 끝장내기
- [번역글] Image Segmentation에 대한 짧은 이야기: R-CNN 에서 부터 Mask R-CNN 까지
- 이미지 Detection 문제와 딥러닝: YOLOv2로 얼굴인식하기
- Keras와 HDF5으로 대용량 데이터 학습하기
- MXBoard — MXNet Data Visualization
- MXNet - Keras gets a lightning fast backend!
- Sentiment Analysis on movie reviews using CNN-LSTM architecture
- Introducing Machine Learning Practica
- DIY Deep Learning Projects
- Credit Card Default Prediction Using TensorFlow [Part-1 Deep Neural Networks]
- Relational Network Review
- TensorBoard Tutorial
- A curated list of MXNet examples, tutorials and blogs
- TensorFlow Estimator & Dataset APIs
- Realtime tSNE Visualizations with TensorFlow.js
- Transfer Learning in Tensorflow [VGG19 on CIFAR-10]: Part 1
- Transfer Learning in Tensorflow [VGG19 on CIFAR-10]: Part 2 : [Code]
- Sagify: Training and Deploying ML/DL models on AWS SageMaker made simple
- 이미지 탐지기 쉽게 구현하기
- Deploying deep learning models: Part 1 an overview
- How to EASILY put Machine Learning Models into Production using Tensorflow Serving
- Stanfoard CS231n 2017 요약
- Reconstructing Brain MRI Images Using Deep Learning [Convolutional Autoencoder]
- Applied ML on Structured Data: Deep Learning vs Tree Methods [Part 1]
- Train a model in tf.keras with Colab, and run it in the browser with TensorFlow.js
- 구글 콜래보래토리 소개 [revised]
- Logo Detection Using PyTorch
- How to deploy TensorFlow models to production using TF Serving
- NLP's ImageNet moment has arrived
- How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning
- A Project Based Introduction to TensorFlow.js
- FAST.AI - PART 1 - LESSON 1 - ANNOTATED NOTES
- Universal Language Model to Boost Your NLP Models
- Building a Question-Answering System from Scratch— Part 1
- How to Use MLflow, TensorFlow, and Keras with PyCharm
- xkcd.com + Artificial Intelligence : [Code]
- ENAS[Efficient Neural Architecture Search via Parameter Sharing]
- Learning From Noisy Large-Scale Datasets With Minimal Supervision Review
- What do machine learning practitioners actually do?
- An Opinionated Introduction to AutoML and Neural Architecture Search
- Google's AutoML: Cutting Through the Hype
- 딥러닝 프레임워크로 임베딩 제대로 학습해보기
- Getting Started with SageMaker
- 94% accuracy on CIFAR-10 in 10 minutes with Amazon SageMaker
- Leveling up on SageMaker
- Autoencoder as a Classifier using Fashion-MNIST Dataset
- 쌩초보자의 Python 케라스[Keras] GAN 코드 분석 [draft]
- [번역+약간해설] 케라스[Keras] 모델 만들기: Sequential vs. Functional
- TF Jam — Shooting Hoops with Machine Learning
- Artificial Neural Networks Explained
- CNN의 stationarity와 locality
- Mario vs. Wario: Image Classification in Python
- A tutorial on using Google Cloud TPUs
- YOLOv2 to detect your own objects using Darkflow
- Running fast.ai notebooks with Amazon SageMaker
- GluonNLP — Deep Learning Toolkit for Natural Language Processing
- Google AI Chief Jeff Dean’s ML System Architecture Blueprint
- WaveNet Review
- Kaggle Tensorflow Speech Recognition Challenge
- Deploying Keras Deep Learning Models with Flask
- Building an image search service from scratch
- AutoKeras: The Killer of Google’s AutoML
- Image Super-Resolution using Multi-Decoder Framework
- How I implemented iPhone X’s FaceID using Deep Learning in Python
- Intuitively Understanding Convolutions for Deep Learning
- Neural Style Transfer: Creating Art with Deep Learning using tf.keras and eager execution
- Train a model with Keras and Prediction using TensorFlow.js
- Auto-Keras, or How You can Create a Deep Learning Model in 4 Lines of Code
- How to serve an embedding trained with Estimators
- A comprehensive guide on how to fine-tune deep neural networks using Keras on Google Colab [Free GPU]
- Eye in the Sky — Image Classification using Transfer Learning and Data Augmentation
- Building a text classification model with TensorFlow Hub and Estimators
- Neural Networks from Scratch. Easy vs hard
- Deep Dive into Math Behind Deep Networks
- Training and Serving ML models with tf.keras
- Introduction to Object Detection
- What is a Generative Adversarial Network?
- Introduction to Word Embeddings
- Slide
- Deep Learning 101: Slides
- Layer Normalization
- TensorFlow Dev Summit 2017 요약
- Google Dev Summit Extended Seoul - TensorFlow: Tensorboard & Keras
- 2017 tensor flow dev summit
- CNN 초보자가 만드는 초보자 가이드 (VGG 약간 포함)
- TensorFlow Tutorial
- Knowing when to look : Adaptive Attention via A Visual Sentinel for Image Captioning
- 기계 학습의 현재와 미래
- Amazon 인공 지능(AI) 서비스 및 AWS 기반 딥러닝 활용 방법
- 지적 대화를 위한 깊고 넓은 딥러닝 PyCon APAC 2016 : [Video]
- 딥러닝(Deep Learning) using DeepDetect
- Explaining and harnessing adversarial examples (2015)
- Paper Reading : Learning from simulated and unsupervised images through adversarial training
- One-Shot Learning
- A Gentle Autoencoder Tutorial (with keras) : [Code]
- Toward Best Practices of TensorFlow Code Patterns
- Generative adversarial networks
- AI 그까이거
- 인공지능: 변화와 능력개발
- 인공지능, 기계학습 그리고 딥러닝
- Deep Learning Into Advance - 1. Image, ConvNet
- 텐서플로 걸음마 (TensorFlow Tutorial)
- Convolutional neural network in practice
- 쫄지말자딥러닝2 - CNN RNN 포함버전
- Introduction to Deep Learning with TensorFlow
- 딥러닝을 이용한 자연어처리의 연구동향
- 기계학습 / 딥러닝이란 무엇인가
- Spark machine learning & deep learning
- 의료빅데이터 컨테스트 결과 보고서
- Deep learning
- Squeezing Deep Learning Into Mobile Phones
- Image Segmentation
- Understanding deep learning requires rethinking generalization 2017 1/2
- Understanding deep learning requires rethinking generalization 2017 2/2
- 대전AI포럼 - 1회 자료
- Scalable Deep Learning Using MXNet
- Introduction For seq2seq and RNN
- Visual Detection, Recognition and Tracking with Deep Learning
- Distributed Deep Learning At Scale On Apache Spark With BigDL
- Attention mechanisms with tensorflow
- 텐서플로우 & 딥러닝 수박 겉핥기
- Deep Learing Tutorial
- SNU TF 스터디 발표 자료
- Practical Neural Machine Translation
- [NDC2017] 딥러닝으로 게임 콘텐츠 제작하기 - VAE를 이용한 콘텐츠 생성 기법 연구 사례
- NDC 2017 키노트: 이은석 - 다가오는 4차 산업혁명 시대의 게임개발
- Recent Progress on Object Detection
- TensorFlow@HKUST
- Wasserstein GAN 수학 이해하기 I
- Deep Generative Models
- Deep learning with Keras
- Sentiment analysis on Twitter using word2vec and keras
- [한국어] Safe Multi-Agent Reinforcement Learning for Autonomous Driving
- 딥러닝 프레임워크 비교
- 자바로 Mnist 구현하고_스프링웹서버붙이기
- Generative adversarial networks
- Variants of GANs
- 머신러닝으로 얼굴 인식 모델 개발 삽질기
- Deep Learning을 위한 AWS 기반 인공 지능
- 알기쉬운 Variational AutoEncoder
- Sequence learning and modern RNNs
- Variational Autoencoder를 여러 가지 각도에서 이해하기
- Text classification using a cnn on tensorflow
- [PR12]Continuous Control with Deep Reinforcement Learning
- 딥러닝 책 정리 자료
- Autoencoders - A way for Unsupervised Learning of Nonlinear Manifold
- AutoML & AutoDraw
- Learning by association
- A Practitioner’s Guide to MXNet
- 모두를 위한 MxNET - AWS Summit Seoul 2017 : [Code]
- AWS re:Invent 2016: Workshop: Deploy a Deep Learning Framework on Amazon ECS : [Code]
- PYCON KR 2017 - 구름이 하늘의 일이라면[Python과 TensorFlow를 이용한 기상예측]
- Deep learning framework 제작
- 1시간만에 GAN[Generative Adversarial Network] 완전 정복하기 : [Video]
- Build, Scale, and Deploy Deep Learning Pipelines with Ease Using Apache Spark
- Deep learning text NLP and Spark Collaboration. 역 딥러닝 Text NLP & Spark
- Understanding RCNN Family
- 자습해도 모르겠던 딥러닝, 머리속에 인스톨 시켜드립니다.
- Applying deep learning to medical data
- Deep Learning, Where are you going? - 조경현[NYU 교수] : [Video]
- Learning to reason by reading text and answering questions - 서민준님 : [Video]
- 딥러닝 기본 원리의 이해
- Step-by-step approach to question answering : [Video]
- Finding connections among images using CycleGAN : [Video]
- Multimodal Sequential Learning for Video QA : [Video]
- 딥러닝을 활용한 비디오 스토리 질의응답: 뽀로로QA와 심층 임베딩 메모리망 : [Video]
- Predictive Maintenance with Deep Learning and Apache Flink : [Video]
- Video Object Segmentation in Videos : [Video]
- NLP_with_Deep_Learning_한국어
- 텐서플로우로 배우는 딥러닝
- Introduction to Capsule Networks [CapsNets] : [Video], [Video2]
- 그림 그리는 AI - GAN : [Video]
- Deep Learning: Practice and Trends - NIPS 2017 : [Video]
- [PR12] Capsule Networks - Jaejun Yoo : [Video]
- Tensorflow & GCP - 그렇고 그런 사이
- 슬로우캠퍼스 딥러닝스쿨[한대희] 파트#1-이론
- 슬로우캠퍼스 딥러닝스쿨[한대희] 파트#2-딥러닝핵심
- GCP CloudML Intro
- Tutorial on Object Detection [Faster R-CNN]
- Amazon SageMaker을 통한 손쉬운 Jupyter Notebook 활용하기 - 윤석찬 : [Video]
- Variational AutoEncoder
- Notes from Coursera Deep Learning courses by Andrew Ng
- Deep learning overview
- 텐서플로 120% 활용하기
- AWS Lambda를 통한 Tensorflow 및 Keras 기반 추론 모델 서비스하기
- TensorFlow.Data 및 TensorFlow Hub
- Recurrent Neural Network and its Application
- Introduction to GAN
- 소프트웨어 2.0을 활용한 게임 어뷰징 검출
- 빠르게 구현하는 RNN
- Deep learning [Machine learning] tutorial for beginners
- 여러 컨볼루션 레이어 테크닉과 경량화 기법들
- Deep Learning for AI [1]
- Deep Learning for AI [2]
- Deep Learning for AI [3]
- [GAN by Hung-yi Lee]Part 1: General introduction of GAN : [Video]
- [GAN by Hung-yi Lee]Part 2: The application of GAN to speech and text processing : [Video]
- [GAN by Hung-yi Lee]Part 3: The recent research of my group : [Video]
- Various seminars on ML/DL
- 조희철님의 딥러닝 자료
- Video
- Andrej Karpathy's Youtube Channel
- Intro to Deep Learning (Udacity Nanodegree) - Siraj Raval
- Feeding your own data set into the CNN model in Keras
- Intro into Image classification using Keras
- Integrating Keras & TensorFlow: The Keras workflow, expanded
- 테리님의 딥러닝 토크
- DeepLearning.TV
- Deep Learning From A to Z - Raphael Gontijo Lopes
- 페이스북, AI대가 '얀 레쿤 교수' 인공지능 강의 공개
- Deep Learning with Keras and Python
- How Deep Neural Networks Work
- TensorFlow Tutorial
- Deep Learning with Python
- How to Deploy Keras Models to Production
- Python Plays: Grand Theft Auto V
- PyDataTV
- Deep Learning with Tensorflow - Cognitive Class
- 12인회 논문 읽기 비디오
- Deep learning with Keras
- 머신러닝/딥러닝 실전 입문
- Neural Networks - 3Blue1Brown
- Deep Learning and Streaming in Apache Spark 2 x - Matei Zaharia & Sue Ann Hong
- Apache MXNet으로 배워보는 딥러닝
- 헬로 딥러닝 - 남세동님 : [eBook]
- 빅데이터, 머신러닝, 그리고 AI
- AWS의 새로운 통합 딥러닝 서비스, Amazon SageMaker - 김무현 솔루션즈 아키텍트 [AWS]
- Getting Started With AWS SageMaker
- AWS SageMaker Deep Learning for Breast Cancer Prediction
- How To Pull Data into S3 using AWS Sagemaker
- An overview of Amazon SageMaker
- Image classification with Amazon SageMaker
- Deep Learning Practitioner의 캐글 2회 참가기
- PR-099: MRNet-Product2Vec
- 주재걸 교수님의 머신러닝/딥러닝/선형대수 강의영상
- 최성철 교수님의 머신러닝/데이터과학 강의영상
- Code
- Fast PixelCNN++: speedy image generation
- Keras with Deeplearning4j
- DeepDream in Keras
- Neural-Chatbot by Keras
- Detects Clickbait Headlines Using Deep Learning: Clickbait Detector
- A self-driving car simulator built with Unity
- deep-facebook-commenter
- Sequential model in Keras -> ASCII
- Deep Q&A
- TensorFlow Tutorials
- A toy chatbot powered by deep learning and trained on data from Reddit
- ML_Practice with TensorFlow
- Keras-Tutorials
- Tensorflow Tutorials using Jupyter Notebook
- Simple implementation of Generative Adversarial Networks
- Generative Adversarial Network for approximating a 1D Gaussian distribution
- pytorch-tutorial
- DeepLearningForNLPInPytorch
- Building an image classifier using keras
- Deep Learning for Self-Driving Cars
- Keras Generative Adversarial Networks
- DiscoGAN - SKT Brain
- DiscoGAN in PyTorch
- DiscoGAN in Tensorflow
- Variational Auto-Encoder for MNIST
- Kind PyTorch Tutorial for beginners
- Distributed Deep Learning on AWS Using MXNet and TensorFlow
- Keras-GAN-Animeface-Character
- Object Recognition using TensorFlow and Java
- Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV
- Keras based Neural Style Transfer
- RNN-implementation-using-Numpy-binary-digit-addition
- keras implementation of [A simple neural network module for relational reasoning]
- Building AnswerBot with Keras and Tensorflow
- Traffic Sign Recognition with Keras
- Neural image captioning implementation with Keras 2
- Seq2seq Chatbot for Keras
- Digit Recognizer with CNN on Keras
- MXNet Notebooks
- Textgenrnn - Python module to easily generate text using a pretrained character-based recurrent neural network
- Mxnet_Tutorial
- Tensorflow implementation of different GANs and their comparisions
- An end-to-end tutorial for OCR recognition using CNN
- Notebook from the Deep Learning Twitch Series on AWS - MXNet
- Tensorflow implementation of various GANs and VAEs
- Pytorch implementation of various GANs
- Chatbot in 200 lines of code
- Jupyter notebooks for the code samples of the book "Deep Learning with Python"
- Python package with source code from the course "Creative Applications of Deep Learning w/ TensorFlow"
- GANs comparison without cherry-picking
- Simple GAN model using keras with Fashion-mnist data
- Lambda API to caption images [with im2txt]
- Multi-layer Recurrent Neural Networks for character-level language models in Python using Tensorflow by 1.3 version [Estimator, Experiment, Dataset]
- Keras-GAN
- Demo of running NNs across different deep learning frameworks
- Distributed TensorFlow Guide
- Jupyter-Tensorboard: Start tensorboard in Jupyter notebook
- TensorNets - High level network definitions with pre-trained weights in TensorFlow
- A neural chatbot using sequence to sequence model with attentional decoder implements by Tensorflow 1.4 version
- Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
- Pytorch implementations of various Deep NLP models in cs-224n[Stanford Univ]
- Korean Restaurant Reservation Dialogue System
- 구글 머신러닝 워크샵 교육 코드
- Convolutional Neural Networks for Sentence Classification[TextCNN] implements by TensorFlow
- imgaug - Image augmentation for machine learning experiments
- Pytorch easy-to-follow Capsule Network tutorial
- Understanding NN - Tensorflow tutorial for "Methods for Interpreting and Understanding Deep Neural Networks"
- Neural Korean to English Machine Translater with Gluon
- Simple Tensorflow DatasetAPI Tutorial for reading image
- This repository provides tutorial python scripts used in the EverybodyTensorlfow lecture by Jaewook Kang
- Unsupervised anomaly detection with generative model, keras implementation
- GAN in Numpy
- Deep Learning Study with Gluon
- Deploy Keras Model with Flask as Web App in 10 Minutes
- NLP Tutorial with Deep Learning using tensorflow
- TensorFlow Advanced Tutorials
- Repo for the Deep Learning Nanodegree Foundations program
- Experiments with Deep Learning
- Tool
- TensorFlow - Google
- Keras - Google
- Caff2 - Facebook
- PyTorch - Facebook
- MXNet - AWS
- CNTK - Microsoft
- PaddlePaddle - Baidu
- Neural Network Libraries - Sony
- Caffe
- Theano
- Torch
- DeepLearning4J
- Chainer
- Kur
- OpenNMT - An open-source neural machine translation system
- tf-seq2seq
- ParlAI - A framework for training and evaluating AI models on a variety of openly available dialog datasets
- NeuroNER - A Named-Entity Recognition Program based on Neural Networks and Easy to Use
- spaCy - Industrial-Strength Natural Language Processing
- Keras Visualization Toolkit
- Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data
- DeepForge - A Modern Development Environment for Deep Learning
- TensorFire - A framework for running neural networks in the browser, accelerated by WebGL
- deeplearn.js - A hardware-accelerated machine intelligence library for the web
- Beholder - A TensorBoard plugin for visualizing arbitrary tensors in a video as your network trains
- AllenNLP - An open-source NLP research library, built on PyTorch
- StarSpace - Learning embeddings for classification, retrieval and ranking
- Fabrik – Collaboratively build, visualize, and design neural nets in the browser : [Code]
- LUMINOTH - Open source Computer Vision toolkit
- Horovod - Uber’s Open Source Distributed Deep Learning Framework for TensorFlow
- Deepo - A Docker image containing almost all popular deep learning frameworks
- Skorch - A scikit-learn compatible neural network library that wraps PyTorch
- Kubeflow - Machine Learning Toolkit for Kubernetes
- Darkon: Toolkit to Hack Your Deep Learning Models
- Detectron - FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet
- Visual Search with MXNet Gluon
- AutoKeras - open source software library for automated machine learning [AutoML]
- Dataset
Reinforcement Learning
- Fundamental of Reinforcement Learning
- OpenAI : A non-profit artificial intelligence research company
- Reinforcement Learning 그리고 OpenAI
- LEARNING REINFORCEMENT LEARNING (WITH CODE, EXERCISES AND SOLUTIONS)
- Deep Q Learning with Keras and Gym
- Minimal Monte Carlo Policy Gradient (REINFORCE) Algorithm Implementation in Keras
- 이슈카님 강화학습 블로그
- Building a deep learning DOOM bot
- A DOOM flavored primer to reinforcement learning
- [ RL ] CS 294: Deep Reinforcement Learning —(1) Introduction and course overview
- Tutorial: Introduction to Reinforcement Learning with Function Approximation
- Introduction to Markov chains
- [리뷰] DEVIEW : 쿠키런 AI 구현하기
- 딥 강화학습 쉽게 이해하기
- Reinforcement Learning
- 모두의 알파고
- Torch DQN 강화학습 소개
- Doom Bots in TensorFlow
- Keras plays catch, a single file Reinforcement Learning example
- Demystifying Deep Reinforcement Learning
- Deep Reinforcement Learning with Neon
- jayyang님의 머신러닝 블로그
- Introduction to Q-Learning
- Practical Reinforcement Learning
- A Deep Learning Research Review of Reinforcement Learning
- Playing Atari with Deep Reinforcement Learning
- Minimal and Clean Reinforcement Learning Examples
- [IGC] 엔씨소프트 이경종 - 강화 학습을 이용한 NPC AI 구현
- Deep Reinforcement Learning
- TensorForce: A TensorFlow library for applied reinforcement learning
- Introduction to reinforcement learning and OpenAI Gym
- Tic-Tac-Toe-Machine-Leaning-Using-Reinforcement-Learning
- Deep Q-Learning with Pytorch
- [한국어] Safe Multi-Agent Reinforcement Learning for Autonomous Driving
- Reinforcement learning for complex goals, using TensorFlow
- Reinforcement Learning w/ Keras + OpenAI: Actor-Critic Models
- Deep Reinforcement Learning, Decision Making, and Control - ICML 2017 Tutorial
- Open-AI의 gym 이용해 강화학습 훈련하기 1: Q-learning
- 실용주의 머신러닝 6회차 [Jeju ML camp 2017] - Deep Reinforcement Learning based Self Driving Car : [Code]
- Introduction to Imitation Learning
- 파이썬과 케라스로 배우는 강화학습 저자특강
- 알아두면 쓸데있는 신기한 강화학습 NAVER 2017 : [Video]
- 스타2 강화학습 튜토리얼
- Contextual Bandits and Reinforcement Learning
- RLCode와 A3C 쉽고 깊게 이해하기 : [Video]
- Introduction of Deep Reinforcement Learning : [Video]
- 5 Ways to Get Started with Reinforcement Learning
- 강화학습 공부 로드맵
- 게임과 AI #1 심층강화학습과 AI
- 게임과 AI #2 블레이드 & 소울과 게임 AI Part. 1
- Reinforcement learning on stock trading
- Deep RL Bootcamp
- 스타크래프트2 강화학습
- 슈퍼마리오에 모두를 위한 RL 수업의 딥러닝 코드 붙이기
- 알파고는 스스로 신의 경지에 올랐다
- CNTK 2.1 + Keras + Reinforcement Learning in Python with Flapping Bird
- AlphaGo Zero Explained In One Diagram
- [카카오AI리포트]강화학습 & 슈퍼마리오 part1
- 강화학습으로 풀어보는 슈퍼마리오 part 2.
- Teaching an Actor-Critic Agent Through Optimal Scripted Agent Trajectories
- Doing Deep Reinforcement learning with PPO
- Direct Future Prediction - Supervised Learning for Reinforcement Learning
- Introduction to Various Reinforcement Learning Algorithms
- Reinforcement Learning - 첫번째 이야기
- 강화학습으로 똑똑한 슈퍼마리오 만들기
- How to build your own AlphaZero AI using Python and Keras
- 강화학습 소개 - 이동민님
- Monte Carlo Tree Search – beginners guide
- My Journey to Reinforcement Learning — Part 0: Introduction
- My Journey to Reinforcement Learning — Part 1: Q-Learning with Table
- My Journey to Reinforcement Learning — Part 1.5: Simple Binary Image Transformation with Q-Learning
- Multi-armed Bandits
- An introduction to Reinforcement Learning
- reinforcement_learning_an_introduction
- Hallucinogenic Deep Reinforcement Learning Using Python and Keras
- How I built an AI to play Dino Run
- Build an AI to play Dino Run
- RL Basics: 1. Markov Process
- RL: 2. Markov Decision Process
- 강화학습에 대한 기본적인 알고리즘 구현
- 안.전.제.일. 강화학습!
- 강화학습 기초부터 DQN까지 [Reinforcement Learning from Basics to DQN]
- Rl from scratch part1
- Rl from scratch part2
- Rl from scratch part3
- Rl from scratch part4
- Rl from scratch part5
- Rl from scratch part6
- Rl from scratch part7
- From REINFORCE to PPO
- AI in Video Games: Improving Decision Making in League of Legends using Markov Chains, Real Match Statistics and Personal Preferences
- Python Implementation of Reinforcement Learning: An Introduction
- Deep Reinforcement Learning Course
- Safe Reinforcement Learning
- 웅이님의 강화학습 블로그
- 인공지능 슈퍼마리오의 거의 모든 것[Pycon 2018 정원석]
- The Future with Reinforcement Learning — Part 1
Machine Learning
- Machine Learning Top 10 Articles for the Past Year (v.2017)
- Natural Language Processing using Word2Vec
- 6 Fun Machine Learning Projects for Beginners
- 50+ Data Science, Machine Learning Cheat Sheets, updated
- Prophet: forecasting at scale - Time Series Data Analysis
- Paper Reading : Enriching word vectors with subword information(2016)
- 머신러닝, 제대로 배우는 법
- Predicting Breast Cancer Using Apache Spark Machine Learning Logistic Regression
- 2016 여름 머신러닝 워크샵 1일차 강의 (KAIST 오혜연 교수님)
- 휴먼 러닝 : 머신 러닝 학습 노트
- Word2Vec Vector Algebra Comparison - Python(Gensim) VS Scala(Spark)
- The Amazing Power of Word Vectors
- Word2Vec, Bag-Of-Words
- word2vec 관련 이론 정리
- Machine Learning Recipes with Josh Gordon
- How to use pre-trained word vectors from Facebook’s fastText
- 한국어와 NLTK, Gensim의 만남
- Winning Tips on Machine Learning Competitions by Kazanova, Current Kaggle #3
- Applying Machine Learning To March Madness
- Scikit-Learn Tutorial Series
- 머신 러닝 뉴스 주제 분류
- Transfer Learning - Machine Learning's Next Frontier
- [용어 정리] 입개발자를 위한 Accuracy, Precision, Recall
- Ultimate Guide to Understand & Implement Natural Language Processing
- [용어 정리] 입 개발자를 위한 TF-IDF
- PRML[Pattern Recognition & Machien Learning, Bishop] 을 정리한 문서
- 머신러닝 기반 주차 문제 예측 시스템 개발기 by Google
- Machine learning methods - infographic
- Modern Machine Learning Algorithms: Strengths and Weaknesses
- Dimensionality Reduction Algorithms: Strengths and Weaknesses
- 머신러닝 모델링 알고리즘
- A Collection of Jupyter Notebooks for Machine Learning
- Tuning Your DBMS Automatically with Machine Learning
- End to End Application for Monitoring Real-Time Uber Data Using Apache APIs: Kafka, Spark, HBase – Part 4: Spark Streaming, DataFrames, and HBase
- Coursera Machine Learning으로 기계학습 배우기
- Brief Introduction to Machine Learning without Deep Learning
- SOM: Self Organazing Map 으로 Clustering 코드구현 까지
- Prophet - facebook 의 시계열예측 API
- [선형대수학 #4] 특이값 분해[Singular Value Decomposition, SVD]의 활용
- Facebook Prophet
- Machine Learning 강의노트
- Churn Prediction with Apache Spark Machine Learning
- MNIST 시각화 - 차원 감소
- precision, recall의 이해
- SVD와 PCA, 그리고 잠재의미분석[LSA]
- ElasticSearch Machine Learning
- Gaussian Process Regression tutorial
- 머신러닝을 위한 기초 수학 살펴보기 by mingrammer
- Kaggle 뉴욕시 임대 아파트 문제 머신러닝 튜토리얼 - Pycon Korea 2017
- [SPSS 22] ROC 곡선
- Machine Learning Mindmap / Cheatsheet
- Ensemble Learning to Improve Machine Learning Results
- MEET MICHELANGELO: UBER’S MACHINE LEARNING PLATFORM
- Dimensionality Reduction Using t-SNE
- In Raw Numpy: t-SNE
- Interpreting Decision Trees and Random Forests
- 쉽게 씌어진 word2vec
- A Gentle Introduction on Market Basket Analysis — Association Rules
- Kaggle Zero To All
- Visualising high-dimensional datasets using PCA and t-SNE in Python
- Singular Value Decomposition [SVD] Tutorial: Applications, Examples, Exercises
- 빛나유님의 Data Mining 블로그
- Get Started In Machine Learning in 5 Steps
- soynlp - 김형준님의 한국어 분석을 위한 python library
- How to make your data and models interpretable by learning from cognitive science
- [번역]AI 머신러닝을 시작하는 방법에 대한 조언
- Three Effective Feature Selection Strategies
- [AI] The fastest way to identify keywords in news articles — TFIDF with Wikipedia [Python version]
- Linear Regression in Python; Predict The Bay Area’s Home Prices
- Best Method to Learn Essential Machine Learning Skills Fast
- 캐글[Kaggle] 데이터분석 배우기
- 2017년 가을 Azure Machine Learning 스터디 계획 및 자료 관리
- Predict Employee Turnover With Python
- Kaggle-Knowhow[Korean Ver] 한국분들을 위한 Kaggle 자료 모음
- 자연어 처리[NLP] 관련 블로그
- Solving A Simple Classification Problem with Python — Fruits Lovers’ Edition
- Introduction to Kaggle Kernels
- 머신러닝 강의 - 허민석님 : [English]
- 오늘의 캐글[Kaggle] : [Code]
- Interactive Machine Learning: Make Python ‘Lively’ Again
- Machine Learning for Diabetes
- A Kaggle Master Explains Gradient Boosting
- A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning
- Gradient Boosting 알고리즘: 개념
- Introduction to Boosted Trees [한국어 버전]
- Gradient Boosting from scratch
- XGBoost - eXtreme Gradient Boosting
- A Gentle Introduction to XGBoost for Applied Machine Learning : [번역]
- XGBoost 사용하기
- Kaggle Tutorial - DataCamp
- Ensemble Learning in Machine Learning | Getting Started
- 차원축소 훑어보기 [PCA, SVD, NMF]
- Kaggle Titanic Competition - A Data Science Framework: To Achieve 99% Accuracy
- How to score 0.8134 in Titanic Kaggle Challenge
- General Tips for participating Kaggle Competitions
- 멀티 암드 밴딧[Multi-Armed Bandits]
- 톰슨 샘플링 for Bandits
- 정보 이론: Information Theory 1편
- 정보 이론 2편: KL-Divergence
- Who will survive the shipwreck?! - Kaggle Titanic
- Stacked Regressions : Top 4% on LeaderBoard - Kaggle House Prices
- Using Yelp Data to Predict Restaurant Closure
- Random Forest in Python
- Improving the Random Forest in Python Part 1
- Hyperparameter Tuning the Random Forest in Python
- Time Series Analysis in Python: An Introduction
- Machine Learning Zero-to-Hero: Everything you need in order to compete on Kaggle for the first time, step-by-step!
- Time Series Analysis Tutorial with Python
- End-to-end Distributed ML using AWS EMR, Apache Spark [Pyspark] and MongoDB Tutorial with MillionSongs Data
- How to Handle Imbalanced Classes in Machine Learning
- Introduction to Python Ensembles
- Data ScienceTutorial for Beginners
- Machine Learning Tutorial for Beginners
- Feature Selection and Data Visualization
- 초짜 대학원생의 입장에서 이해하는 Support Vector Machine [1]
- 열 개의 팔을 가진 강도 - Multi Armed Bandit
- Word2vec을 응용한 컨텐츠 클러스터링
- Why, How and When to apply Feature Selection
- Regression 모델 평가 방법
- Minimizing the Negative Log-Likelihood, in Korean [1]
- Minimizing the Negative Log-Likelihood, in Korean [2]
- Dealing with Imbalanced Classes in Machine Learning
- Topic Modeling with Scikit Learn
- An illustrated introduction to the t-SNE algorithm : [Code]
- Gradient Descent[경사하강법]
- Multi-Class Text Classification with Scikit-Learn
- Multi-Class Text Classification with PySpark
- Multi Label Text Classification with Scikit-Learn
- Common Design for Distributed Machine Learning
- Machine Learning Workflow on Diabetes Data : Part 01
- Machine Learning Workflow on Diabetes Data : Part 02
- Kaggle House Prices Advanced Regression Techniques: One hour analysis
- Always start with a stupid model, no exceptions
- How to solve 90% of NLP problems: a step-by-step guide
- Multi-Class Text Classification with Scikit-Learn
- Logistic Regression — Detailed Overview
- Time Series for scikit-learn People Part I: Where's the X Matrix?
- Time Series for scikit-learn People Part II: Autoregressive Forecasting Pipelines
- Topic Modeling with Gensim[Python]
- Topic Modelling in Python with NLTK and Gensim
- Save Lives With 10 Lines of Code: Detecting Parkinson’s with XGBoost
- Machine Learning Study[Boosting 기법 이해]
- Introduction to Bayesian Linear Regression
- A note about finding anomalies
- Machine Learning for Text Classification Using SpaCy in Python
- Interpretable Machine Learning with XGBoost
- Bayesian Linear Regression in Python: Using Machine Learning to Predict Student Grades Part 1
- Bayesian Linear Regression in Python: Using Machine Learning to Predict Student Grades Part 2
- Visualizing data using t-SNE
- Automatic feature extraction with t-SNE
- How to Use Machine Learning to Predict the Quality of Wines : [Code]
- 구글 ML 엔진 - scikit-learn, XGBoost 지원
- Machine Learning - Ensemble, Bagging, Boosting
- PCA using Python [scikit-learn]
- Use the built-in Amazon SageMaker Random Cut Forest algorithm for anomaly detection
- Using Word2Vec for Better Embeddings of Categorical Features
- A visual introduction to machine learning Part I
- A visual introduction to machine learning Part II - Model Tuning and the Bias-Variance Tradeoff
- Facebook’s Field Guide to Machine Learning video series
- Dimensionality Reduction in Machine Learning by stacking PCA and t-SNE
- Python Machine Learning: Scikit-Learn Tutorial
- Running KMeans clustering on Spark
- Using K-Means to analyse hacking attacks
- K-Means Clustering in Python
- Introduction to K-means Clustering
- Clustering with Sklearn
- Python K-Means Data Clustering and finding of the best K
- ELI5: ROC Curve, AUC metrics
- Generate Quick and Accurate Time Series Forecasts using Facebook’s Prophet [with Python & R codes]
- Let’s learn about AUC ROC Curve!
- Bias-Variance Tradeoff
- Another Twitter sentiment analysis with Python — Part 1
- Another Twitter sentiment analysis with Python-Part 2
- Another Twitter sentiment analysis with Python — Part 3 [Zipf’s Law, data visualisation]
- Another Twitter sentiment analysis with Python — Part 4 [Count vectorizer, confusion matrix]
- Another Twitter sentiment analysis with Python — Part 5 [Tfidf vectorizer, model comparison, lexical approach]
- Another Twitter sentiment analysis with Python — Part 6 [Doc2Vec]
- Another Twitter sentiment analysis with Python — Part 7 [Phrase modeling + Doc2Vec]
- Another Twitter sentiment analysis with Python — Part 8 [Dimensionality reduction: Chi2, PCA]
- Another Twitter sentiment analysis with Python — Part 9 [Neural Networks with Tfidf vectors using Keras]
- Another Twitter sentiment analysis with Python — Part 10 [Neural Network with Doc2Vec/Word2Vec/GloVe]
- Another Twitter sentiment analysis with Python — Part 11 [CNN + Word2Vec]
- Sentiment Analysis with PySpark
- Yet Another Twitter Sentiment Analysis Part 1 — tackling class imbalance
- In Depth: Parameter tuning for Gradient Boosting
- Understanding Random Forests Classifiers in Python
- Unsupervised Learning with Python
- Predicting the Survival of Titanic Passengers
- A Complete Machine Learning Project Walk-Through in Python: Part One
- A Complete Machine Learning Walk-Through in Python: Part Two
- A Complete Machine Learning Walk-Through in Python: Part Three
- Automated Machine Learning on the Cloud in Python
- Machine Learning Kaggle Competition Part One: Getting Started
- Machine Learning Kaggle Competition Part Two: Improving
- Automated Feature Engineering in Python
- A Feature Selection Tool for Machine Learning in Python
- A Conceptual Explanation of Bayesian Model-Based Hyperparameter Optimization for Machine Learning
- An Introductory Example of Bayesian Optimization in Python with Hyperopt
- Automated Machine Learning Hyperparameter Tuning in Python
- Machine Learning Kaggle Competition: Part Three Optimization
- Why Automated Feature Engineering Will Change the Way You Do Machine Learning
- Time Series - Machine Learning Mastery
- Comprehensive Guide to Time Series Analytics, Visualization and Prediction with Python
- 푸른생선의 신바람 금융바다 - 통계, Time Series 데이터 분석
- ARIMA, Python으로 하는 시계열분석 [feat. 비트코인 가격예측]
- A comprehensive beginner’s guide to create a Time Series Forecast [with Codes in Python]
- Time Series Visualization and Forecasting
- Koshort - 한국어 NLP를 위한 high-level API 프로젝트
- The Logistic Regression Algorithm
- Apache Spark and Amazon SageMaker, the Infinity Gems of analytics
- Using Chalice to serve SageMaker predictions
- Trend, Seasonality, Moving Average, Auto Regressive Model : My Journey to Time Series Data with Interactive Code
- Building Trust in Machine Learning Models [using LIME in Python]
- Using categorical data in machine learning with python
- Time Series Analysis for Financial Data I— Stationarity, Autocorrelation and White Noise
- Time Series Analysis for Financial Data II — Auto-Regressive Models
- Time Series Analysis for Financial Data III— Moving Average Models
- Time Series Analysis for Financial Data IV— ARMA Models
- Time Series Analysis for Financial Data V — ARIMA Models
- Time Series Analysis for Financial Data VI— GARCH model and predicting SPX returns
- Featuretools - An open source framework for automated feature engineering
- Manage your Machine Learning Lifecycle with MLflow — Part 1.
- Kaggle Fundamentals: The Titanic Competition
- Getting Started with Kaggle: House Prices Competition
- Machine Learning Fundamentals: Predicting Airbnb Prices
- Machine Learning with Python: A Tutorial
- Human Interpretable Machine Learning [Part 1] — The Need and Importance of Model Interpretation
- Kaggle 강의 자료
- Predicting the Status of H-1B Visa Applications
- Sentiment analysis on reviews: Train Test Split, Bootstrapping, Cross Validation & Word Clouds
- K-Means Clustering
- Realtime prediction using Spark Structured Streaming, XGBoost and Scala
- Unboxing Outliers In Machine Learning
- Detecting Financial Fraud Using Machine Learning: Winning the War Against Imbalanced Data
- Using regression models to predict per capita and median household income in NYC
- Philadelphia Housing Data Part-I: Data Analysis
- Philadelphia Housing Data Part-II: Features Engineering
- Philadelphia Housing Data Part-III: Machine Learning
- Approaching a competition on Kaggle: Avito Demand Prediction Challenge [Part 1] : [Code]
- Approaching a competition on Kaggle: Avito Demand Prediction Challenge [Part 2]
- Approaching a competition on Kaggle: Avito Demand Prediction Challenge [Part 3 — linear regression]
- Chapter-1 Machine Learning Introduction
- Chapter-2 Data and It’s Different Types
- Chapter-3 Bias and Variance Trade-off in Machine Learning
- Learn How to Code and Deploy Machine Learning Models on Structured Streaming
- 광고 클릭 예측을 통해 페이스북이 얻은 실용적인 교훈
- K-Means Clustering in Python with scikit-learn
- Effective Outlier Detection Techniques in Machine Learning
- Topic Modeling and Latent Dirichlet Allocation [LDA] in Python
- 데이터로부터 정보 추출해내기 [Feature Engineering]
- 불균형 데이터셋의 처리를 위한 training data의 처리
- Introduction to Clinical Natural Language Processing: Predicting Hospital Readmission with Discharge Summaries
- Using XGBoost in Python
- Support Vector Machines with Scikit-learn
- Understanding Model Predictions with LIME
- Machine Learning Rules in a Nutshell
- Winning solutions of kaggle competitions
- 'Machine Learning Yearning' 책의 한국어 번역
- Machine Learning Yearning 번역문서 목록
- Elbow Clustering for Artificial Intelligence
- Kaggle 튜토리얼
- Serve Machine Learning Models with Clipper
- Optimal Coupon Targeting for Grocery Items: an Instacart Case Study
- Detecting Financial Fraud Using Machine Learning: Winning the War Against Imbalanced Data
- A Gentle Introduction to Data Science for Credit Risk Modeling — Part 1
- A Gentle Introduction to Credit Risk Modeling with Data Science — Part 2
- Detecting True and Deceptive Hotel Reviews using Machine Learning
- An End-to-End Project on Time Series Analysis and Forecasting with Python
- Google - ML Universal Guides
- Kaggle Solutions
- Probability for Machine Learning
- 파이썬으로 머신러닝 배우기
- Brewing up custom ML models on AWS SageMaker
- Comparing Multi-Armed Bandit Algorithms on Marketing Use Cases
- DBSCAN: A Macroscopic Investigation in Python
- Improve Your Model Performance using Cross Validation [in Python and R]
- Gradient Descent — Demystified
- Introduction to Automated Feature Engineering Using Deep Feature Synthesis [DFS]
- Application of Machine Learning Techniques to Trading
- Predicting Employee Churn in Python
- Hyperparameter Optimization in Machine Learning Models
- Unsupervised Text Summarization using Sentence Embeddings
- Parallelizing Feature Engineering with Dask
- Introducing mlflow-apps: A Repository of Sample Applications for MLflow
- Build a model to predict the impact of weather on urban air quality using Amazon SageMaker
Data Science
- 데이터는 차트가 아니라 돈이 되어야 한다
- 수강료 500만원 데이터 사이언스 커리큘럼을 대체하는 무료강의 15개 커리큘럼
- DODOMIRA님의 Data Analysis 블로그
- 데이터 입수 이상징후 탐지
- 네이버 파이낸스 - 재무제표 크롤링 | FinanceData
- Essence of linear algebra
- E-Mail 데이터 곱씹어보기
- Python_numpy_pandas_matplotlib 이해하기
- 2016 PyCon APAC - 너의 사진은 내가 지난 과거에 한 일을 알고 있다
- [FAQ] - Daum부동산 - DataFrame 행 추출과 컬럼으로 합치기
- gimmesilver님의 데이터 분석 블로그
- gimmesilver님의 데이터 분석 브런치
- datageek님의 데이터 분석 블로그
- PinkWink님의 데이터 분석 블로그
- LEARNING PYTHON FOR DATA SCIENCE: CHEAT SHEETS
- NumPy Tutorial: Data analysis with Python
- K-MOOC Operation Research : Numpy Part #1
- K-MOOC Operation Research : Numpy Part #2
- Reproducible Data Analysis in Jupyter
- 에어브릿지 블로그 - Data science
- Analyze one year of radio station songs aired with SQL, Spark, Spotify, and Databricks
- 데이터 사이언스 스쿨
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Chatbot
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