indianvalantine / deep-learning-1 Goto Github PK

This repository contains examples of deep learning algorithms implemented in Python with mathematics behind them being explained.

• For Machine Learning algorithms please check Machine Learning repository.

• For Natural Language Processing (NLU = NLP + NLG) please check Natural Language Processing repository.

• For Computer Vision please check Computer Vision repository.

• CS 231N: Convolutional Neural Networks for Visual Recognition, Stanford
• CS 224N: Natural Language Processing with Deep Learning, Stanford
• Machine Learning Crash Course
• fast.ai: Practical Deep Learning for Coders"
• CS 285: Deep Reinforcement Learning, UC Berkeley
• CSC 2541: Differentiable Inference and Generative Models
• MIT 6.S191: Introduction to Deep Learning
  • MIT 6.S191: Introduction to Deep Learning
• Frontiers of Deep Learning (Simons Institute)
  • Course website
• New Deep Learning Techniques
  • Course website
• Geometry of Deep Learning (Microsoft Research)
  • Course website
• Deep Multi-Task and Meta Learning (Stanford CS330)
  • Course Website
• Advanced Deep Learning & Reinforcement Learning 2020 (DeepMind / UCL)
• Deep Reinforcment Learning, Decision Making and Control (UC Berkeley CS285)
• Full Stack Deep Learning 2019
• Emerging Challenges in Deep Learning
• Deep|Bayes 2019 Summer School
• Workshop on Theory of Deep Learning: Where next (Institure for Advanced Study)
• Deep Learning: Alchemy or Science? (Institure for Advanced Study)

• Caffe – a fast open framework for deep learning;
• Deep Learning - Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016);
• Deep Learning от Google — короткий курс для продвинутых. Основное внимание уделяется библиотеке для глубинного обучения TensorFlow;
• Deep Learning at Oxford (2015) – a YouTube playlist with lectures (read more);
• awesome-deep-vision – a curated list of deep learning resources for computer vision;
• awesome-deep-learning-papers – a curated list of the most cited deep learning papers (since 2010);
• Deep Learning Tutorials – notes and code;
• dl-docker – an all-in-one Docker image for deep learning. Contains all the popular DL frameworks (TensorFlow, Theano, Torch, Caffe, etc.);
• Self-Study Courses for Deep Learning от NVDIA — self-paced classes for deep learning that feature interactive lectures, hands-on exercises, and recordings of the office hours Q&A with instructors. You’ll learn everything you need to design, train, and integrate neural network-powered artificial intelligence into your applications with widely used open-source frameworks and NVIDIA software. During the hands-on exercises, you will use GPUs and deep learning software in the cloud;
• deep-rl-tensorflow - ensorFlow implementation of Deep Reinforcement Learning papers;
• TensorFlow 101 – Tensorflow tutorials;
• Introduction to Deep Learning for Image Recognition – this notebook accompanies the Introduction to Deep Learning for Image Recognition workshop to explain the core concepts of deep learning with emphasis on classifying images as the application;