Deep Learning with PyTorch [Video]

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Deep Learning with PyTorch [Video]

Anand Saha
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Build useful and effective deep learning models with the PyTorch Deep Learning framework
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Video Details

ISBN 139781788475266
Course Length4 hours and 42 minutes

Video Description

This video course will get you up-and-running with one of the most cutting-edge deep learning libraries: PyTorch. Written in Python, PyTorch is grabbing the attention of all data science professionals due to its ease of use over other libraries and its use of dynamic computation graphs.

In this course, you will learn how to accomplish useful tasks using Convolutional Neural Networks to process spatial data such as images and using Recurrent Neural Networks to process sequential data such as texts. You will explore how you can make use of unlabeled data using Auto Encoders. You will also be training a neural network to learn how to balance a pole all by itself, using Reinforcement Learning. Throughout this journey, you will implement various mechanisms of the PyTorch framework to do these tasks.

By the end of the video course, you will have developed a good understanding of, and feeling for, the algorithms and techniques used. You'll have a good knowledge of how PyTorch works and how you can use it in to solve your daily machine learning problems.

Style and Approach

This is a very hands-on course where concepts and their implementations go hand in hand. The course maintains a balance between theory and practice. 

Table of Contents

Getting Started With PyTorch
The Course Overview
Introduction to PyTorch
Installing PyTorch on Linux and Windows
Installing CUDA
Introduction to Tensors and Variables
Working with PyTorch and NumPy
Working with PyTorch and GPU
Handling Datasets in PyTorch
Deep Learning Using PyTorch
Training Your First Neural Network
Building a Simple Neural Network
Loss Functions in PyTorch
Optimizers in PyTorch
Training the Neural Network
Saving and Loading a Trained Neural Network
Training the Neural Network on a GPU
Computer Vision – CNN for Digits Recognition
Computer Vision Motivation
Convolutional Neural Networks
The Convolution Operation
Concepts - Strides, Padding, and Pooling
Loading and Using MNIST Dataset
Building the Model
Training and Testing
Sequence Models – RNN for Text Generation
Sequence Models Motivation
Word Embedding
Recurrent Neural Networks
Building a Text Generation Model in PyTorch
Training and Testing
Autoencoder - Denoising Images
Autoencoders Motivation
How Autoencoders Work
Types of Autoencoders
Building Denoising Autoencoder Using PyTorch
Training and Testing
Reinforcement Learning – Balance Cartpole Using DQN
Reinforcement Learning Motivation
Reinforcement Learning Concepts
DQN, Experience Replay
The OpenAI Gym Environment
Building the Cartpole Agent Using DQN
Training and Testing

What You Will Learn

  • Understand PyTorch and Deep Learning concepts
  • Build your neural network using Deep Learning techniques in PyTorch.
  • Perform basic operations on your dataset using tensors and variables
  • Build artificial neural networks in Python with GPU acceleration
  • See how CNN works in PyTorch with a simple computer vision example
  • Train your RNN model from scratch for text generation
  • Use Auto Encoders in PyTorch to remove noise from images
  • Perform reinforcement learning to solve OpenAI's Cartpole task
  • Extend your knowledge of Deep Learning by using PyTorch to solve your own machine learning problems

Authors

Table of Contents

Getting Started With PyTorch
The Course Overview
Introduction to PyTorch
Installing PyTorch on Linux and Windows
Installing CUDA
Introduction to Tensors and Variables
Working with PyTorch and NumPy
Working with PyTorch and GPU
Handling Datasets in PyTorch
Deep Learning Using PyTorch
Training Your First Neural Network
Building a Simple Neural Network
Loss Functions in PyTorch
Optimizers in PyTorch
Training the Neural Network
Saving and Loading a Trained Neural Network
Training the Neural Network on a GPU
Computer Vision – CNN for Digits Recognition
Computer Vision Motivation
Convolutional Neural Networks
The Convolution Operation
Concepts - Strides, Padding, and Pooling
Loading and Using MNIST Dataset
Building the Model
Training and Testing
Sequence Models – RNN for Text Generation
Sequence Models Motivation
Word Embedding
Recurrent Neural Networks
Building a Text Generation Model in PyTorch
Training and Testing
Autoencoder - Denoising Images
Autoencoders Motivation
How Autoencoders Work
Types of Autoencoders
Building Denoising Autoencoder Using PyTorch
Training and Testing
Reinforcement Learning – Balance Cartpole Using DQN
Reinforcement Learning Motivation
Reinforcement Learning Concepts
DQN, Experience Replay
The OpenAI Gym Environment
Building the Cartpole Agent Using DQN
Training and Testing

Video Details

ISBN 139781788475266
Course Length4 hours and 42 minutes
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