آموزش پیشرفته شبکه های عصبی با Tensorflow
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.
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.
Released: Thursday, February 15, 2018
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
Visualizing Your Graph
Plotting the Weights in a Histogram
Inspecting Input and Output
Encoding MNIST Characters
Practical Application –Denoising
The Dropout Layer
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
Reinforcement Learning Explained
Reinforcement Learning Explained (Continued)