Build useful and effective deep learning models with the PyTorch Deep Learning framework
About This Video
- Explore PyTorch and the impact it has made on Deep Learning
- Design and implement powerful neural networks to solve some impressive problems in a step-by-step manner
- Follow the examples to solve similar use cases outside this course
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.
This course uses Python 3.6, and PyTorch 0.3, while not the latest version available, it provides relevant and informative content for legacy users of Python, and PyTorch.
This course is for Python programmers who have some knowledge of machine learning and want to build Deep Learning systems with PyTorch. Python programming knowledge and minimal math skills (matrix/vector manipulation, simple probabilities) is assumed.
Table of contents
- Chapter 1 : Getting Started With PyTorch
- Chapter 2 : Training Your First Neural Network
- Chapter 3 : Computer Vision – CNN for Digits Recognition
- Chapter 4 : Sequence Models – RNN for Text Generation
- Chapter 5 : Autoencoder - Denoising Images
- Chapter 6 : Reinforcement Learning – Balance Cartpole Using DQN
- Title: Deep Learning with PyTorch
- Release date: April 2018
- Publisher(s): Packt Publishing
- ISBN: 9781788475266
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