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Advanced Neural Networks with Tensorflow [Video]

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Advanced Neural Networks with Tensorflow [Video]

Roland Meertens

Get hands-on and understand Advanced Neural Networks with TensorFlow
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Video Details

ISBN 139781788622752
Course Length3 hours and 34 minutes

Video Description

Neural Networks are at the forefront of almost all recent major technology breakthroughs. The intersection of big data, parallel programming, and AI generated a new wave of Neural Network research. In this course, you will be taken through some of the best uses of Neural Networks using TensorFlow.

You'll explore Deep Reinforcement Learning algorithms such as Generative Networks and Deep Q Learning. You will learn to implement some more complex types of neural networks such as Deep Q Learning with OpenAI Gym, autoencoders, and Siamese neural networks. During the course of the video, you will be working on real-world datasets to get a hands-on understanding of neural network programming. You will also get to train generative models and will learn Autoencoder applications.

By the end of this course, you will have a fair understanding of how you can leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle.

Style and Approach

The course has working examples and helpful advice about using advanced techniques Neural Network with Tensorflow. This practical course is divided into clear byte size chunks so you can learn at your own pace and focus on the areas of most interest to you.

Table of Contents

Working with TensorFlow
The Course Overview
The Approach of This Course
Installing Docker and Downloading the Source Code for This Course
Understanding Jupyter Notebooks and TensorFlow
Using TensorBoard
Visualizing Your Graph
Adding Summaries
Plotting the Weights in a Histogram
Inspecting Input and Output
Autoencoders
Encoding MNIST Characters
Practical Application –Denoising
The Dropout Layer
Variational Autoencoders
Siamese Neural Networks
The Omniglot Dataset
What Is a Siamese Neural Network?
Training and Testing a Siamese Neural Network
Alternative Loss Functions
Speed of Your Network
The OpenAI Gym
Getting Started with the OpenAI Gym
Random Search
Reinforcement Learning Explained
Reinforcement Learning Explained (Continued)

What You Will Learn

  • Understand how and when to apply autoencoders
  • Generate new images using variational autoencoders
  • Perform encoding MNIST characters in Autoencoders
  • Train and test a Siamese neural network
  • Work with the Omniglot dataset
  • Explore the input and output of different games in OpenAI Gym
  • Develop an autonomous agent in an Atari environment with OpenAI Gym

Authors

Table of Contents

Working with TensorFlow
The Course Overview
The Approach of This Course
Installing Docker and Downloading the Source Code for This Course
Understanding Jupyter Notebooks and TensorFlow
Using TensorBoard
Visualizing Your Graph
Adding Summaries
Plotting the Weights in a Histogram
Inspecting Input and Output
Autoencoders
Encoding MNIST Characters
Practical Application –Denoising
The Dropout Layer
Variational Autoencoders
Siamese Neural Networks
The Omniglot Dataset
What Is a Siamese Neural Network?
Training and Testing a Siamese Neural Network
Alternative Loss Functions
Speed of Your Network
The OpenAI Gym
Getting Started with the OpenAI Gym
Random Search
Reinforcement Learning Explained
Reinforcement Learning Explained (Continued)

Video Details

ISBN 139781788622752
Course Length3 hours and 34 minutes
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