English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 3h 34m | 534 MB
Get hands-on and understand Advanced Neural Networks with TensorFlow
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.
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.
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
Table of Contents
Working with TensorFlow
1 The Course Overview
2 The Approach of This Course
3 Installing Docker and Downloading the Source Code for This Course
4 Understanding Jupyter Notebooks and TensorFlow
Using TensorBoard
5 Visualizing Your Graph
6 Adding Summaries
7 Plotting the Weights in a Histogram
8 Inspecting Input and Output
Autoencoders
9 Encoding MNIST Characters
10 Practical Application –Denoising
11 The Dropout Layer
12 Variational Autoencoders
Siamese Neural Networks
13 The Omniglot Dataset
14 What Is a Siamese Neural Network
15 Training and Testing a Siamese Neural Network
16 Alternative Loss Functions
17 Speed of Your Network
The OpenAI Gym
18 Getting Started with the OpenAI Gym
19 Random Search
20 Reinforcement Learning Explained
21 Reinforcement Learning Explained (Continued)
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