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Best PyTorch Tutorials 2020

Practical Deep Learning with PyTorch

Practical Deep Learning with PyTorch by Deep Learning Wizard will help you learn deep learning with PyTorch. You will learn to effectively use PyTorch, a Python based framework used to build Deep Learning projects. This PyTorch course uses more than 100 custom-made diagrams that clearly explain the transition from one model to another and understand the models comprehensively. This is a practical PyTorch course that teaches by doing. You will follow along the PyTorch code in detail. By the end, you will master Deep Learning concepts and implement them in PyTorch. Learn PyTorch from one of the best PyTorch course in 2020.

Best PyTorch Courses 2020

Modern Deep Convolutional Neural Networks with PyTorch

Modern Deep Convolutional Neural Networks with PyTorch by Denis Volkhonskiy will help you learn PyTorch for Image Recognition with Convolutional Neural Networks. You will get a refresher on Linear layers, SGD, and how to train Deep Networks. Then you will move to the Convolution section, where convolutions is discussed along with it’s parameters, advantages and disadvantages. The PyTorch training will teach you Regularization and normalization along with Deep Learning tips and tricks. You will learn about fine tuning, transfer learning, modern datasets and architectures. This is one of the best PyTorch tutorial in 2020.

PyTorch for Deep Learning with Python Bootcamp

PyTorch for Deep Learning with Python Bootcamp by Jose Portilla will help you get started with learning PyTorch. PyTorch is an open source deep learning platform. This PyTorch course focuses on balancing important theory concepts with practical hands-on exercises and projects. You will learn how to apply the concepts to your own data sets. This PyTorch course will help you learn how to use NumPy to format data into arrays. Pandas is used data manipulation and cleaning. You will learn classic machine learning theory principles. You will make use of PyTorch Deep Learning Library for image classification and Recurrent Neural Networks for Sequence Time Series Data. This course will teach you to create state of the art Deep Learning models to work with tabular data. This is one of the best PyTorch course for Deep Learning in 2020.

 

Best PyTorch Books 2020

Bestsellers

SaleBestseller No. 1
Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD
  • Howard, Jeremy (Author)
  • English (Publication Language)
  • 624 Pages - 08/04/2020 (Publication Date) - O'Reilly Media (Publisher)
SaleBestseller No. 2
Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications
  • Pointer, Ian (Author)
  • English (Publication Language)
  • 220 Pages - 10/08/2019 (Publication Date) - O'Reilly Media (Publisher)
Bestseller No. 3
PyTorch Computer Vision Cookbook: Over 70 recipes to master the art of computer vision with deep learning and PyTorch 1.x
  • Avendi, Michael (Author)
  • English (Publication Language)
  • 364 Pages - 03/20/2020 (Publication Date) - Packt Publishing (Publisher)
Bestseller No. 4
Deep Learning with PyTorch
  • Stevens, Eli (Author)
  • English (Publication Language)
  • 450 Pages - 06/09/2020 (Publication Date) - Manning Publications (Publisher)
SaleBestseller No. 5
Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play
  • Foster, David (Author)
  • English (Publication Language)
  • 330 Pages - 07/16/2019 (Publication Date) - O'Reilly Media (Publisher)
SaleBestseller No. 6
Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning
  • Rao, Delip (Author)
  • English (Publication Language)
  • 256 Pages - 02/19/2019 (Publication Date) - O'Reilly Media (Publisher)
Bestseller No. 7
PyTorch 1.x Reinforcement Learning Cookbook: Over 60 recipes to design, develop, and deploy self-learning AI models using Python
  • Liu, Yuxi (Hayden) (Author)
  • English (Publication Language)
  • 340 Pages - 10/31/2019 (Publication Date) - Packt Publishing (Publisher)
SaleBestseller No. 8
Deep Learning with Python
  • Chollet, François (Author)
  • English (Publication Language)
  • 384 Pages - 12/22/2017 (Publication Date) - Manning Publications (Publisher)
Bestseller No. 9
PyTorch Deep Learning Hands-On: Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily
  • Thomas, Sherin (Author)
  • English (Publication Language)
  • 250 Pages - 04/30/2019 (Publication Date) - Packt Publishing (Publisher)
SaleBestseller No. 10
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
  • Géron, Aurélien (Author)
  • English (Publication Language)
  • 856 Pages - 10/15/2019 (Publication Date) - O'Reilly Media (Publisher)

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

Take the next step towards mastering on in-depth learning, the machine learning method that transforms the world around us in one second. In this hands-on book, you’ll learn the basics using Facebook’s open source Pytorch framework and learn your latest skills for creating your own neural networks.

Ian Pointer shows you how to configure PyTorch in a cloud-based environment, then guides you through creating neural architectures that facilitate operations on images, sound, text, etc. by facilitating a deep dive into each element. It also criticizes the application of transition learning to images, debugging models, and the pitcher in production.

Find out how to set up a deep learning model in production. Discover the uses of PyTorch from several large companies. Learn how to apply transition learning to images. Apply advanced NLP techniques using a model trained on Wikipedia. Use PyTorch Library to classify audio data with the Convolution-based model. Debug PyTorch models using tensorboard and flame graphics. Install the PyTorch app in Google Cloud running docker containers and production in the Kubernetes cluster.

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: Over 60 recipes to design, develop, and deploy self-learning AI models using Python
  • Liu, Yuxi (Hayden) (Author)
  • English (Publication Language)
  • 340 Pages - 10/31/2019 (Publication Date) - Packt Publishing (Publisher)

Apply reinforcing learning techniques and algorithms using real-world examples and recipes. Use PyTorch 1.x to design and create artificial intelligence (AI) self-learning models. Apply RL algorithms to solve the control and optimization challenges faced by information scientists today. Apply a modern RL library to simulate a controlled environment for your projects. Reinforcement Learning (RL) is a branch of machine learning that has gained popularity in recent times. It allows you to train AI models who learn from their own actions and optimize their behavior. RL models have also become the preferred tool for training due to the PyTorch efficiency and ease of use.

This book lets you apply important RL concepts and algorithms to PyTorch 1.x. Book recipes, as well as real-world examples will help you master various RL techniques such as dynamic programming, Monte Carlo simulations, time difference and queue learning for you will also find an overview for specific application art techniques. The following chapters will guide you to solve multi-armed robber problems and cartpool problems such as using multi-armed robber algorithms and approximate actions. You will learn how to use deep queue networks to finish Atari games, as well as apply strategic gradients effectively. Finally, you’ll discover how RL strategies are applied to blackjack, gridworld environments, internet advertising, and floppy bird games.

Towards the end of this book, you will gain your practical skills to implement popular RL algorithms and build skills and use RL techniques to solve real problems.
What will you learn?

Use key-learning and state-action-reward-state-action algorithms (SARSA) to solve various problems in the gridworld
Develop multi-arm bandit algorithm to optimize display ad
Expand learning and control processes through Deep Q-Networks
Imitate Markov’s decision-making processes, the OpenAI gym environment and other common control issues
Select and build RL models, evaluate their functionality, customize and deploy them
Use the policy gradient method to solve the running LR problem

Machine learning engineers, data scientists and AI researchers will find this book useful in finding quick solutions to various augmentation learning problems. Although prior knowledge of machine learning concepts is required, experience with PyTorch will be effective but not necessary.