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.
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
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.
About The Author
Anand Saha: Anand Saha is a software professional with 15 years' experience in developing enterprise products and services. Back in 2007, he worked with machine learning to predict call patterns at TATA Communications. At Symantec and Veritas, he worked on various features of an enterprise backup product used by Fortune 500 companies. Along the way he nurtured his interests in Deep Learning by attending Coursera and Udacity MOOCs.
He is passionate about Deep Learning and its applications; so much so that he quit Veritas at the beginning of 2017 to focus full time on Deep Learning practices. Anand built pipelines to detect and count endangered species from aerial images, trained a robotic arm to pick and place objects, and implemented NIPS papers. His interests lie in computer vision and model optimization.
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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