This is the code repository for TensorFlow 1.x Deep Learning Recipes for Artificial Intelligence Applications [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
This course is all about some of the most exciting applications of Deep Learning and how to implement them in TensorFlow. You will learn how to build models to solve problems in different domains such as Computer vision, Natural Language Processing, Reinforcement Learning, Finance, and more.
Taking a Cookbook approach, this course presents you with easy-to-follow recipes to show the use of advanced Deep Learning techniques and their implementation in TensorFlow. After taking this tutorial you will be able to start building advanced Deep Learning models with TensorFlow for applications with a wide range of fields.
- Understand how to implement Convolutional Neural Networks and use them for Computer Vision
- Learn how to build Recurrent Neural Network models and use them for Natural Language Processing tasks
- Apply foundational models in Reinforcement Learning
- Use Deep Learning models implemented in TensorFlow to solve problems in many domains
- Start building your own Deep Learning applications
To fully benefit from the coverage included in this course, you will need:
This course is intended for Data Analysts, Data Scientists, Data Engineers, Software Engineers, and anyone working with Python and data who wants to perform Machine Learning on a regular basis and use TensorFlow to build Deep Learning models.
Fluency in Python 3, familiarity with basic Machine Learning concepts, a basic awareness of the most common Machine Learning algorithms, familiarity with Neural Networks and related concepts, and a basic understanding of how TensorFlow works will be advantageous.