This repository is home to the code that accompanies Jon Krohn's Deep Learning with TensorFlow, Keras, and PyTorch series of video tutorials.
There are three sets of video tutorials in the series:
- The eponymous Deep Learning with TensorFlow, Keras, and PyTorch (released in Feb 2020)
- Deep Learning for Natural Language Processing, 2nd Ed. (Feb 2020)
- Machine Vision, GANs, and Deep Reinforcement Learning (Mar 2020)
The above order is the recommended sequence in which to undertake these tutorials. That said, the first in the series provides a strong foundation for either of the other two.
Taken all together, the series -- over 18 total hours of instruction and hands-on demos -- parallels the entirety of the content in the book Deep Learning Illustrated. This means that the videos introduce all of deep learning:
- What deep neural networks are and how they work, both mathematically and using the most popular code libraries
- Machine vision, primarily with convolutional neural networks
- Natural language processing, including with recurrent neural networks
- Artistic creativity with generative adversarial networks (GANs)
- Complex, sequential decision-making with deep reinforcement learning
These video tutorials also includes some extra content that is not available in the book, such as:
- Detailed interactive examples involving training and testing deep learning models in PyTorch
- How to generate novel sequences of natural language in the style of your training data
- High-level discussion of transformer-based natural-language-processing models like BERT, ELMo, and GPT-2
- Detailed interactive examples of training advanced machine vision models (image segmentation, object detection)
- All hands-on code demos involving TensorFlow or Keras have been updated to TensorFlow 2
Installation instructions for running the code in this repository can be found in the installation directory.
There are dozens of meticulously crafted Jupyter notebooks of code associated with these videos. All of them can be found in this directory.
Below is a breakdown of the lessons covered across the videos, including their duration and associated notebooks.
- Seven hours and 13 minutes total runtime
- Lesson 1: Introduction to Deep Learning and Artificial Intelligence (1 hour, 47 min)
- Lesson 2: How Deep Learning Works (2 hours, 16 min) -- free YouTube video here
- Lesson 3: High-Performance Deep Learning Networks (1 hour, 16 min)
- Lesson 4: Convolutional Neural Networks (47 min)
- Lesson 5: Moving Forward with Your Own Deep Learning Projects (1 hour, 4 min)
- Five hours total runtime
- Lesson 1: The Power and Elegance of Deep Learning for NLP (46 min)
- Lesson 2: Word Vectors (1 hour, 7 min) -- free YouTube video here
- Lesson 3: Modeling Natural Language Data (1 hour, 43 min)
- Lesson 4: Recurrent Neural Networks (25 min)
- Lesson 5: Advanced Models (54 min)
- Six hours and six minutes total runtime
- Lesson 1: Orientation (35 min)
- Lesson 2: Convolutional Neural Networks for Machine Vision (2 hours, 2 min) -- free YouTube video here
- Lesson 3: Generative Adversarial Networks for Creativity (1 hour, 22 min)
- Lesson 4: Deep Reinforcement Learning (38 min)
- Lesson 5: Deep Q-Learning and Beyond (1 hour, 25 min)
You've reached the bottom of this page! As a reward, here's a myopic trilobite created by Aglae Bassens, a co-author of the book Deep Learning Illustrated:
