Machine Learning with PyTorch and Scikit-Learn PDF Download Free

Machine Learning with PyTorch and Scikit-Learn PDF

Attributes of Machine Learning with PyTorch and Scikit-Learn PDF

Machine Learning with PyTorch and Scikit-Learn PDF is a comprehensive guide to machine and deep learning using PyTorch’s simple to code framework

Key Features

  • Learn applied machine learning with a solid foundation in theory
  • Clear, intuitive explanations take you deep into the theory and practice of Python machine learning
  • Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices

Book Description

Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you’ll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself.

Why PyTorch?

PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric.

You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP).

This PyTorch book is your companion to machine learning with Python, whether you’re a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

What you will learn

  • Explore frameworks, models, and techniques for machines to ‘learn’ from data
  • Use scikit-learn for machine learning and PyTorch for deep learning
  • Train machine learning classifiers on images, text, and more
  • Build and train neural networks, transformers, and boosting algorithms
  • Discover best practices for evaluating and tuning models
  • Predict continuous target outcomes using regression analysis
  • Dig deeper into textual and social media data using sentiment analysis

Who this book is for

If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch.

Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.

Table of Contents

  1. Giving Computers the Ability to Learn from Data
  2. Training Simple Machine Learning Algorithms for Classification
  3. A Tour of Machine Learning Classifiers Using Scikit-Learn
  4. Building Good Training Datasets – Data Preprocessing
  5. Compressing Data via Dimensionality Reduction
  6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
  7. Combining Different Models for Ensemble Learning
  8. Applying Machine Learning to Sentiment Analysis
  9. Predicting Continuous Target Variables with Regression Analysis
  10. Working with Unlabeled Data – Clustering Analysis

(N.B. Please use the Look Inside option to see further chapters)

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Illustrations of Machine Learning with PyTorch and Scikit-Learn PDF

For students of all the branches of medicine and surgery and health professionals that aspire to be greater and better at their procedures and medications. A renowned book by those who have read it and learnt from it. Many have already ordered it and is on the way to their home. Whether you work in the USA, Canada, UK or anywhere around the world. If you are working as a health professional then this is a must read..  The most reviewed on book Machine Learning with PyTorch and Scikit-Learn PDF is available for grabs now here on our website free. Whatever books, mainly textbooks we have in professional courses specially Medicine and surgery is a compendium in itself so understand one book you need to refer another 2-10 books. Beside this there are various other text material which needs to be mastered!! Only reference books are partially read but all other books have to be read, commanded and in fact read multiple times.

The Writers

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.

Yuxi (Hayden) Liu is a Software Engineer, Machine Learning at Google. He is developing and improving machine learning models and systems for ads optimization on the largest search engine in the world.

Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.

Proportions of Machine Learning with PyTorch and Scikit-Learn PDF

  • Publisher ‏ : ‎ Packt Publishing (February 25, 2022)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 770 pages
  • International Standard Book Number-10 ‏ : ‎ 1801819319
  • International Standard Book Number-13 ‏ : ‎ 978-1801819312
  • Item Weight ‏ : ‎ 2.86 pounds
  • Dimensions ‏ : ‎ 7.5 x 1.74 x 9.25 inches

Reviews From Customers

I am planning to start medical school this year. I asked a friend who is a second-year student what he would recommend for A&P prep. He said they use Netter. So I searched just on Netter and came across this option. I was very fortunate to get a “like new” set for a phenomenal price from a seller who very obviously did not use this set much, if at all. I am very pleased with the quality and detail. I am excited like a little kid! I will continue to read through as much material prior to starting school, and I know these will be absolutely invaluable throughout my entire med school journey.

moishele32
Different volumes from different sets
March 21, 2021

The collection is fine. I personally got volume 9 from a different set. It’s pretty pathetic when you pay $711.

Michael B.
A must have for student doctors
October 8, 2019

The green books are a phenomenal resource for medicine. The images and information about various diseases have been an invaluable resource in my medical education and practice.

Choe Seong Choon
Wonderful pieces, but the paper…
April 23, 2019

Wonderful artworks from Dr. F. Netter, I admire him from medical students. But the paper is not so good in order to read in the room light…

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