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a_guide_to_running_tensorflow_models_on_android

This is the code for"A Guide to Running Tensorflow Models on Android" By SIraj Raval on Youtube

a_hybrid_bandit_model_with_visual_priors_for_creative_ranking

A Hybrid Bandit Model with Visual Priors for Creative Ranking in Display Advertising

awesome-tensorflow

TensorFlow - A curated list of dedicated resources http://tensorflow.org

awesome-tensorflow-1

A curated list of awesome projects using the TensorFlow machine learning library.

awesome-text-classification

Awesome-Text-Classification Projects,Papers,Tutorial .

awesome-text-classification-1

Text classification meets word embeddings.

awesome-text-summarization

The guide to tackle with the Text Summarization

awesome-text-summarization-1

A curated list of resources dedicated to text summarization

awesome-topic-segmentation

awesome-torch

A curated list of awesome Torch tutorials, projects and communities

awesome-transfer-learning

Best transfer learning and domain adaptation resources (papers, tutorials, codes, etc.)

awesome-very-deep-learning

A curated list of papers and code about very deep neural networks

awesome-vqa

Visual Q&A reading list

awesome_tensorflow_implementations

awesome_time_series_in_python

This curated list contains python packages for time series analysis

aws-big-data-blog

aws-lambda-ffmpeg

An S3-triggered Amazon Web Services Lambda function that runs your choice of FFmpeg 🎬 commands on a file 🎥 and uploads the outputs to a bucket.

awsome-domain-adaptation

A collection of AWESOME things about domian adaptation

awsrn

PyTorch code for our paper "Lightweight Image Super-Resolution with Adaptive Weighted Learning Network"

axa-insurance-telematics-kaggle

I developed this case study only in 7 days with Pyspark (Spark 1.6.0) SQL & MLlib. I used Databricks cluster and AWS. %90 AUC is achieved (without involving Trip Matching-Repeated Trips feature) with Random Forest. Many ensembles with RF, GBT and Logistic Regression and outlier elimination could be used to improve this result. There are two versions of my code (test and full execution). Since AWS costs have exceeded my budget I sopped to train my model(s) all dataset for full dataset execution. There is also a ppt that presents my outputs in test execution. Full Data Execution code is more production ready and slightly different version. I had to use Databricks Table Caching to TRAIN and TEST data tables to obtain acceptable performance in production ready version.

azure-ai-personalized-offers

Generate real-time personalized offers on a retail website to engage more closely with customers.

b-cnn

Sample code of B-CNN paper (https://arxiv.org/abs/1709.09890) written in Python3+.

babi-tasks

Task generation for testing text understanding and reasoning

babi_tools

Processing scripts for the bAbI Dialog Tasks dataset

baby-a3c

A high-performance Atari A3C agent in 180 lines of PyTorch

baby-vqa

Some (baby) Keras deep neural networks for the Visual Question Answering task.

babyai

BabyAI platform. A testbed for training agents to understand and execute language commands.

babynames_classifier

pytorch implementation of LSTM + Self Attention for character level name classification

background-matting

Background Matting: The World is Your Green Screen

backtrader

Python Backtesting library for trading strategies