# jonkrohn/DLTFpT

Deep Learning with TensorFlow, Keras, and PyTorch

repo name | jonkrohn/DLTFpT |

repo link | https://github.com/jonkrohn/DLTFpT |

homepage | |

language | Jupyter Notebook |

size (curr.) | 18959 kB |

stars (curr.) | 146 |

created | 2019-08-12 |

license | MIT License |

# Deep Learning with TensorFlow, Keras, and PyTorch

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

Installation instructions for running the code in this repository can be found in the installation directory.

## Notebooks

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.

#### Deep Learning with TensorFlow, Keras, and PyTorch

- 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)

#### Deep Learning for Natural Language Processing

- 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)
- Lesson 3: Modeling Natural Language Data (1 hour, 43 min) – free YouTube video here
- Lesson 4: Recurrent Neural Networks (25 min)
- Lesson 5: Advanced Models (54 min)

#### Machine Vision, GANs, and Deep Reinforcement Learning

- 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: