Artificial Intelligence with TensorFlow

Upcoming Packt video course, taught by Brandon McKinzie. The purpose of the course is to provide users with a comprehensive overview of what TensorFlow has to offer.

Here you will find all code used throughout the course, grouped by section. To learn more about the contents of any subdirectory, click its link in the table of contents below.

Table of Contents

• section1: TensorFlow and Machine Learning Fundamentals.
• section2: Computer Vision.
• section3: Natural Language Processing and Recurrent Neural Networks.
• section4: Tips and Tricks.
• section5: TensorFlow in Production. Also solely responsible for:
  • tools directory containing a bazelrc file.
  • workspace.bzl containing the tensorflow_http_archive function that allows us to import tensorflow via Bazel.
  • WORKSPACE: standard Bazel WORKSPACE file for import TensorFlow and any dependencies.
• section6: Miscellaneous Topics and Course Summary. Contains eager.py from video 6.3 on TensorFlow Eager. (No other video in section 6 had code)
• templates: contains the template files we used throughout the course for custom estimators and training hooks.

Getting Setup

1. Install TensorFlow for Python 3.
2. Download data into the data directory from the shared Google Drive.

Additional Resources

The README for each section directory will contain section-specific links to additional resources. Below are a few links that were applicable throughout the entirety of the course.

• Deep Learning Book. Free online book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. An excellent overview of the the field of machine learning in general and deep learning in particular.
• My Notes on deep learning and natural language processing. If there is a topic in the course that we didn't have time to explain in depth, there is a good chance I've written extensive notes on it here.