Deep Learning with PyTorch

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Download or read online Deep Learning with PyTorch PDF book by Luca Pietro Giovanni Antiga in ePUB, PDF or Kindle eBooks. Published by Simon and Schuster in 01 July 2020 the book become immediate popular and critical acclaim in Computers books with total hardcover page 518. Click Download Book button to get book file. Read some info about this book below.

  • Author : Luca Pietro Giovanni Antiga
  • Publisher : Simon and Schuster
  • Release : 01 July 2020
  • ISBN : 9781638354079
  • Page : 518 pages
  • Language : English
  • Total View : 4616 Views
  • File Size : 50,8 Mb

Deep Learning with PyTorch Book PDF summary

“We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production

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Deep Learning with PyTorch PDF

Deep Learning with PyTorch
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DOWNLOAD BOOKDeep Learning with PyTorch

Deep Learning with PyTorch PDF book is popular Computers book written by Luca Pietro Giovanni Antiga,Eli Stevens,Thomas Viehmann. The book was released by Simon and Schuster in 01 July 2020 with total hardcover pages 518. “We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free

Python Machine Learning By Example PDF

Python Machine Learning By Example
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Python Machine Learning By Example PDF book is popular Computers book written by Yuxi (Hayden) Liu. The book was released by Packt Publishing Ltd in 30 October 2020 with total hardcover pages 518. Equipped with the latest updates, this third edition of Python Machine Learning By Example provides a comprehensive course for ML enthusiasts to strengthen their command of ML concepts, techniques, and algorithms.

Machine Learning with PyTorch and Scikit-Learn PDF

Machine Learning with PyTorch and Scikit-Learn
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Machine Learning with PyTorch and Scikit-Learn PDF book is popular Computers book written by Sebastian Raschka,Yuxi (Hayden) Liu,Vahid Mirjalili,Dmytro Dzhulgakov. The book was released by Packt Publishing Ltd in 25 February 2022 with total hardcover pages 518. This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework Key FeaturesLearn applied machine learning with a solid foundation in theoryClear, intuitive explanations take you deep into the theory and practice of Python machine learningFully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practicesBook Description Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow PDF

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow PDF book is popular Computers book written by Aurélien Géron. The book was released by "O'Reilly Media, Inc." in 05 September 2019 with total hardcover pages 518. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems.

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits PDF

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits PDF book is popular Computers book written by Tarek Amr. The book was released by Packt Publishing Ltd in 24 July 2020 with total hardcover pages 518. Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems Key Features Delve into machine learning with this comprehensive guide to scikit-learn and scientific Python Master the art of data-driven problem-solving with hands-on examples Foster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithms Book Description Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular

Programming PyTorch for Deep Learning PDF

Programming PyTorch for Deep Learning
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Programming PyTorch for Deep Learning PDF book is popular Computers book written by Ian Pointer. The book was released by O'Reilly Media in 20 September 2019 with total hardcover pages 518. Deep learning is changing everything. This machine-learning method has already surpassed traditional computer vision techniques, and the same is happening with NLP. If you're looking to bring deep learning into your domain, this practical book will bring you up to speed on key concepts using Facebook's PyTorch framework. Once author Ian Pointer helps you set up PyTorch on a cloud-based environment, you'll learn how use the framework to create neural architectures for performing operations on images, sound, text, and other

Python Machine Learning PDF

Python Machine Learning
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Python Machine Learning PDF book is popular Computers book written by Sebastian Raschka. The book was released by Packt Publishing Ltd in 23 September 2015 with total hardcover pages 518. Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to

PyTorch 1.x Reinforcement Learning Cookbook PDF

PyTorch 1.x Reinforcement Learning Cookbook
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PyTorch 1.x Reinforcement Learning Cookbook PDF book is popular Computers book written by Yuxi (Hayden) Liu. The book was released by Packt Publishing Ltd in 31 October 2019 with total hardcover pages 518. Implement reinforcement learning techniques and algorithms with the help of real-world examples and recipes Key FeaturesUse PyTorch 1.x to design and build self-learning artificial intelligence (AI) modelsImplement RL algorithms to solve control and optimization challenges faced by data scientists todayApply modern RL libraries to simulate a controlled environment for your projectsBook Description Reinforcement learning (RL) is a branch of machine learning that has gained popularity in recent times. It allows you to train AI models that learn from their own