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learning-concurrency-in-python

Learning Concurrency in Python by Packt

learning-concurrency-in-python-video-

Learning Concurrency in Python[Video], Published by Packt

learning-core-audio-book-code-sample

This contains all the sample code in the Learning Core Audio book. See http://www.amazon.com/Learning-Core-Audio-Hands-On-Programming/dp/0321636848

learning-flask

Sample application for the Learning Flask course by Packt Publishing.

learning-from-human-preferences

Reproduction of OpenAI and DeepMind's "Deep Reinforcement Learning from Human Preferences"

learning-generative-adversarial-networks

Learning-Generative-Adversarial-Networks, published by Packt

learning-google-bigquery

Learning Google BigQuery, published by Packt

learning-notes

💡 Repo of learning notes in DRL and DL, theory, codes, models and notes maybe.

learning-python-application-development

Code repository for Learning Python Application Development, published by Packt

learning-python-for-forensics

Learning Python for Forensics by Packt Publishing

learning-pytorch

This Repository contains notebook for learning pytorch and implementing deep learning model for both Computer Vision and Natural Language Processing

learning-spark

Example code from Learning Spark book

learning-to-build-apps-using-watson-ai

Code repository for Learning to Build Apps Using Watson AI, published by Packt

learning-to-communicate

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

learning-to-learn

Learning to Learn in TensorFlow

learning-to-learn--runopti

TensorFlow implementation of the paper "Learning to learn by gradient descent by gradient descent ( https://arxiv.org/abs/1606.04474 )"

learning-to-prune

Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing

learning-to-rank-from-implicit-feedback

learning-tsp

Code for the paper 'Learning TSP Requires Rethinking Generalization' (arXiv Pre-print)

learning-vipassana

Code for all the things I learn

learning_to_compare

Demo for the paper titled "Learning to Compare: Relation Network for Few-Shot Learning"

learning_to_execute

Learning to Execute

learningmlearning

Helping Humans Learn Machine Learning

learningshapelets

learningsimplealgorithms

We present an approach for learning simple algorithms such as copying, multi-digit addition and single digit multiplication directly from examples. Our framework consists of a set of interfaces, accessed by a controller. Typical interfaces are 1-D tapes or 2-D grids that hold the input and output data. For the controller, we explore a range of neural network-based models which vary in their ability to abstract the underlying algorithm from training instances and generalize to test examples with many thousands of digits. The controller is trained using Q-learning with several enhancements and we show that the bottleneck is in the capabilities of the controller rather than in the search incurred by Q-learning.

learningtocompare_fsl

PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part)

learningtocompare_zsl

PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Zero-Shot Learning part)

learningtosee

learningx

Deep & Classical Reinforcement Learning + Machine Learning Examples in Python

learntools

Tools and tests used in Kaggle Learn exercises