Learn PyTorch 2021 – Best PyTorch courses & Best PyTorch books & Best PyTorch tutorials

Best PyTorch Courses 2021

 

Best PyTorch Books 2021

 

Best PyTorch Tutorials 2021

Intro to Machine Learning with PyTorch

This nanodegree program is meant to teach you the fundamentals of machine learning so that you can advance your career in AI and ML. Participating in this program will offer you with in-depth knowledge of supervised learning, neural network design fundamentals, and PyTorch training. You will also learn about deep learning algorithms, which are an important aspect of machine learning. The curriculum was developed by Udacity specialist educators who are well-versed in ML algorithms. If you have any questions about the program material, you will receive full assistance and support from the teachers during the course.

You will:

Discover the fundamental principles and techniques of machine learning, including data manipulation, unsupervised and supervised algorithms, and much more.

Discover how to use unsupervised learning approaches to solve a variety of challenges in any application.

Master ML skills by working on real-world projects in collaboration with industry experts and top-tier firms.

With the program, you will receive customized career coaching services to assist you in choosing the proper path after completing this course.

A learning plan that is adaptable to your daily schedule and work-life balance.

PyTorch for Deep Learning with Python Bootcamp

Welcome to the best online courses for learning Deep Learning with Python and PyTorch! PyTorch is an open source deep learning platform that provides a seamless path from research prototyping to production deployment. It is fast becoming one of the most popular deep learning frameworks for Python programming language. Deep integration into Python allows the use of popular libraries and packages to easily write neural network layers in Python. A rich ecosystem of tools and libraries extends PyTorch framework and supports development in computer vision, NLP and more.

This Pytorch tutorial focuses on balancing important theoretical concepts with hands-on exercises and projects that allow you to learn how to apply the concepts from the course to your own data sets! When you register for this course, you will have access to carefully laid out notebooks that explain the concepts in an easy to understand manner, including code and side-by-side explanations. You’ll also have access to our slides that explain the theory through easy-to-understand visualizations. In this course, we’ll teach you everything you need to know to get started with modern Deep Learning with Pytorch, including:

NumPy
Pandas
Machine learning theory
Separation of test / train / validation data
Model evaluation – Regression and classification tasks
Unsupervised learning tasks
Tensors with PyTorch
Neural network theory
Will perceive
Networks
Activation functions
Cost / loss functions
Backpropagation
Gradients
Artificial neural networks
Convolutional neural networks
Recurrent neural networks

By the end of this course, you will be able to create a wide variety of deep learning models to solve your own problems with your own custom dataset.

You will learn:

Learn how to use NumPy to format data in tables
Use pandas for data manipulation and cleansing
Learn the principles of classical machine learning theory
Use PyTorch Deep Learning Library for image classification
Using PyTorch with Recurrent Neural Networks for Sequence Time Series Data
Create cutting-edge deep learning models to work with tabular data

This is the best PyTorch course in 2021.

Deep Neural Networks with PyTorch


by Joseph Santarcangelo will show you how to use Pytorch to create deep learning models. The lecture will begin with tensors and the Automatic differentiation module in Pytorch. Then, in each section, new models will be covered, beginning with essentials such as Linear Regression and logistic/softmax regression. Feedforward deep neural networks are followed by the involvement of multiple activation functions, normalization, and dropout layers. Following that, Convolutional Neural Networks and Transfer Learning will be discussed. Finally, a number of different Deep Learning approaches will be discussed.

You will:

• be able to explain and apply their understanding of Deep Neural Networks and associated machine learning methods
• be familiar with Python packages such as PyTorch for Deep Learning applications
• use PyTorch to create Deep Neural Networks

Modern Deep Convolutional Neural Networks with PyTorch

The course consists of 4 blocks:

Introductory section, where I remind you, what is linear layers, SGD and how to train deep networks.
Convolution section, where we discuss convolutions, its parameters, advantages and disadvantages.
Regularization and Standardization section, where I share with you useful tips and tricks in Deep Learning.
Fine tuning, transfer learning, modern data sets and architectures

You will learn

Convolutional neural networks
Image processing
Advance deep learning techniques
Regularization, standardization
Transfer learning

PyTorch for Deep Learning and Computer Vision

PyTorch has quickly become one of the most transformative executives in the deep learning field. Since its launch, PyTorch has completely changed the landscape in the field of deep learning due to its flexibility and ease of use when creating deep learning models. Deep Learning jobs are among the highest paying in the development world. This course is intended to take you from comprehensive basics to creating cutting-edge applications for deep learning and computer vision with PyTorch. Learn and master deep learning with PyTorch in this fun and exciting course with top instructor Rayan Slim. With over 44,000 students, Rayan is a highly trained and experienced instructor who followed a “learn by doing” style to create this incredible course. You will go from being a beginner to an expert in Deep Learning and his instructor will complete each task with you step by step on the screen.

By the end of the course, he will have created cutting-edge deep learning and computer vision applications with PyTorch. The projects built in this course will impress even the most experienced developers and ensure you have practical skills that you can bring to any project or business. This course will teach you to:

Learn to work with the tensor data structure
Deploy Machine and Deep Learning Applications with PyTorch
Build neural networks from scratch
Create complex models with the applied theme of advanced imaging and computer vision
Learn to solve complex computer vision problems using highly sophisticated pre-trained models.
Use style transfer to create sophisticated artificial intelligence applications that can seamlessly recompose images in the style of other images.

PyTorch for Deep Learning and Computer Vision

Learn and master deep learning with PyTorch in this fun and exciting course with top instructor Rayan Slim. With over 44,000 students, Rayan is a highly trained and experienced instructor who followed a “learn by doing” style to create this incredible course. He will go from being a beginner to an expert in Deep Learning and his instructor will complete each task with you step by step on the screen. By the end of the course, he will have created cutting-edge deep learning and computer vision applications with PyTorch. The projects built in this course will impress even the most experienced developers and ensure you have practical skills that you can bring to any project or business. This course will teach you to:

Learn to work with the tensor data structure
Deploy Machine and Deep Learning Applications with PyTorch
Build neural networks from scratch
Create complex models with the applied theme of advanced imaging and computer vision
Learn to solve complex computer vision problems using highly sophisticated pre-trained models.
Use style transfer to create sophisticated artificial intelligence applications that can seamlessly recompose images in the style of other images.

Best PyTorch Books 2021

Deep Learning with PyTorch: Build, train, and tune neural networks using Python tool


Deep Learning with PyTorch: Build, train, and tune neural networks using Python tool by Eli Stevens, Luca Antiga and Thomas Viehmann teaches you how to create deep learning systems and neural networks with PyTorch. Deep Learning with PyTorch book helps you get started right away creating a tumor image sorter from scratch. Once he has covered the basics, he will learn best practices for the entire deep learning process, tackling advanced projects as his PyTorch skills become more sophisticated. All code samples are easy to explore in the downloadable Jupyter Notebooks. You will:

Understand deep learning data structures such as tensors and neural networks.
Best Practices for PyTorch Tensor API, Loading Data into Python, and Displaying Results
PyTorch Implementation of loss modules and functions
Using previously trained PyTorch Hub models
Networking methods with limited inputs.
Examine unreliable results to diagnose and troubleshoot your neural network
Improve your results with increased data, better Pytorch model architecture, and fine tuning

Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications


Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications by Ian Pointer shows you how to configure PyTorch in a cloud-based environment, then guides you through creating neural architectures that make it easy to operate images, sound, text, and more with in-depth analysis of each element. It also covers the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production. You will:

Learn how to implement deep learning models in production
Explore PyTorch use cases from various large companies
Learn how to apply transfer learning to images
Apply cutting edge NLP techniques using a model trained in Wikipedia
Use PyTorch’s torchaudio library to classify audio data with a convolution-based model
Debug PyTorch Models with TensorBoard and Flame Charts
Deploy PyTorch applications in production to Docker containers and Kubernetes clusters running on Google Cloud

PyTorch 1.x Reinforcement Learning Cookbook: Over 60 recipes to design, develop, and deploy self-learning AI models using Python


PyTorch 1.x Reinforcement Learning Cookbook by Yuxi (Hayden) Liu will explore important RL concepts and algorithm implementation in PyTorch 1.x. The recipes in the PyTorch book, along with real-world practical examples, will help you master various RL techniques, such as dynamic programming, Monte Carlo simulations, time differences, and Q-learning. He will also learn about the industry-specific applications of these techniques. The following chapters will guide you through solving problems such as the multi-armed bandit problem and the mast problem using the multi-arm bandit algorithm and the function approximation. He will also learn how to use Deep Q-Networks to complement Atari games, as well as how to effectively implement policy gradients. Lastly, he will learn how RL techniques apply to Blackjack, Gridworld environments, Internet advertising, and Flappy Bird games. You will:

Use Q-learning and the SARSA (state – action – reward – state – action) algorithm to solve various Gridworld problems
Develop a multi-armed bandit algorithm to optimize display advertising
Improve learning and control processes using Deep Q-Networks
Simulate Markov decision-making processes, OpenAI Gym environments, and other common control problems
Select and build RL models, evaluate their performance, optimize and implement them
Use Policy Gradient Methods to Solve Continuous RL Problems

Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD


Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD by Jeremy Howard and Sylvain Gugger is a how-to guide demonstrates that programmers comfortable with Python can achieve impressive deep learning results with little math knowledge, small amounts of data, and minimal code. How? ‘Or what? With fastai, the first library to provide a consistent interface for the most widely used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch library. You will also gradually dive into deep learning theory to gain a full understanding of the algorithms behind the scenes including:

Train models in machine vision, natural language processing, tabular data, and collaborative filtering
Learn the latest deep learning techniques that matter most in practice
Improve accuracy, speed, and reliability by understanding how deep learning models work
Find out how to turn your models into web applications
Implement deep learning algorithms from scratch
Consider the ethical implications of your work.
See the preface by PyTorch co-founder Soumith Chintala.

Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning


Natural Language Processing with PyTorch Build Intelligent Language Applications Using Deep Learning by Delip Rao and Brian McMahan. Natural Language Processing (NLP) offers limitless opportunities to solve artificial intelligence problems, making possible products like Amazon Alexa and Google Translate. If you are a developer or data scientist new to NLP and deep learning, this how-to guide shows you how to apply these methods using PyTorch, a Python-based deep learning library.

Explore computer graphics and the supervised learning paradigm
Master the basics of PyTorch’s optimized tensor manipulation library
Get an overview of traditional NLP concepts and methods
Learn the basic ideas involved in building neural networks.
Use inlays to represent words, phrases, documents, and other features
Explore sequence prediction and build sequence-by-sequence models
Learn about design patterns for NLP building production systems