Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Machine Learning with PyTorch and Scikit-Learn
Machine Learning with PyTorch and Scikit-Learn

Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

Arrow left icon
Profile Icon Sebastian Raschka Profile Icon Vahid Mirjalili Profile Icon Yuxi (Hayden) Liu
Arrow right icon
€22.99 €32.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (95 Ratings)
eBook Feb 2022 774 pages 1st Edition
eBook
€22.99 €32.99
Paperback
€41.99
Subscription
Free Trial
Renews at €18.99p/m
Arrow left icon
Profile Icon Sebastian Raschka Profile Icon Vahid Mirjalili Profile Icon Yuxi (Hayden) Liu
Arrow right icon
€22.99 €32.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (95 Ratings)
eBook Feb 2022 774 pages 1st Edition
eBook
€22.99 €32.99
Paperback
€41.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€22.99 €32.99
Paperback
€41.99
Subscription
Free Trial
Renews at €18.99p/m

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Product feature icon AI Assistant (beta) to help accelerate your learning
OR
Modal Close icon
Payment Processing...
tick Completed

Billing Address

Table of content icon View table of contents Preview book icon Preview Book

Machine Learning with PyTorch and Scikit-Learn

Training Simple Machine Learning Algorithms for Classification

In this chapter, we will make use of two of the first algorithmically described machine learning algorithms for classification: the perceptron and adaptive linear neurons. We will start by implementing a perceptron step by step in Python and training it to classify different flower species in the Iris dataset. This will help us to understand the concept of machine learning algorithms for classification and how they can be efficiently implemented in Python.

Discussing the basics of optimization using adaptive linear neurons will then lay the groundwork for using more sophisticated classifiers via the scikit-learn machine learning library in Chapter 3, A Tour of Machine Learning Classifiers Using Scikit-Learn.

The topics that we will cover in this chapter are as follows:

  • Building an understanding of machine learning algorithms
  • Using pandas, NumPy, and Matplotlib to read in, process, and visualize...

Artificial neurons – a brief glimpse into the early history of machine learning

Before we discuss the perceptron and related algorithms in more detail, let’s take a brief tour of the beginnings of machine learning. Trying to understand how the biological brain works in order to design an artificial intelligence (AI), Warren McCulloch and Walter Pitts published the first concept of a simplified brain cell, the so-called McCulloch-Pitts (MCP) neuron, in 1943 (A Logical Calculus of the Ideas Immanent in Nervous Activity by W. S. McCulloch and W. Pitts, Bulletin of Mathematical Biophysics, 5(4): 115-133, 1943).

Biological neurons are interconnected nerve cells in the brain that are involved in the processing and transmitting of chemical and electrical signals, which is illustrated in Figure 2.1:

Figure 2.1: A neuron processing chemical and electrical signals

McCulloch and Pitts described such a nerve cell as a simple logic gate with binary outputs; multiple...

Implementing a perceptron learning algorithm in Python

In the previous section, we learned how Rosenblatt’s perceptron rule works; let’s now implement it in Python and apply it to the Iris dataset that we introduced in Chapter 1, Giving Computers the Ability to Learn from Data.

An object-oriented perceptron API

We will take an object-oriented approach to defining the perceptron interface as a Python class, which will allow us to initialize new Perceptron objects that can learn from data via a fit method and make predictions via a separate predict method. As a convention, we append an underscore (_) to attributes that are not created upon the initialization of the object, but we do this by calling the object’s other methods, for example, self.w_.

Additional resources for Python’s scientific computing stack

If you are not yet familiar with Python’s scientific libraries or need a refresher, please see the following resources...

Adaptive linear neurons and the convergence of learning

In this section, we will take a look at another type of single-layer neural network (NN): ADAptive LInear NEuron (Adaline). Adaline was published by Bernard Widrow and his doctoral student Tedd Hoff only a few years after Rosenblatt’s perceptron algorithm, and it can be considered an improvement on the latter (An Adaptive “Adaline” Neuron Using Chemical “Memistors”, Technical Report Number 1553-2 by B. Widrow and colleagues, Stanford Electron Labs, Stanford, CA, October 1960).

The Adaline algorithm is particularly interesting because it illustrates the key concepts of defining and minimizing continuous loss functions. This lays the groundwork for understanding other machine learning algorithms for classification, such as logistic regression, support vector machines, and multilayer neural networks, as well as linear regression models, which we will discuss in future chapters.

The key...

Summary

In this chapter, we gained a good understanding of the basic concepts of linear classifiers for supervised learning. After we implemented a perceptron, we saw how we can train adaptive linear neurons efficiently via a vectorized implementation of gradient descent and online learning via SGD.

Now that we have seen how to implement simple classifiers in Python, we are ready to move on to the next chapter, where we will use the Python scikit-learn machine learning library to get access to more advanced and powerful machine learning classifiers, which are commonly used in academia as well as in industry.

The object-oriented approach that we used to implement the perceptron and Adaline algorithms will help with understanding the scikit-learn API, which is implemented based on the same core concepts that we used in this chapter: the fit and predict methods. Based on these core concepts, we will learn about logistic regression for modeling class probabilities and support...

Left arrow icon Right arrow icon
Download code icon Download Code

Key benefits

  • 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

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.

Who is this book 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.

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

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Feb 25, 2022
Length: 774 pages
Edition : 1st
Language : English
ISBN-13 : 9781801816380
Category :

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Product feature icon AI Assistant (beta) to help accelerate your learning
OR
Modal Close icon
Payment Processing...
tick Completed

Billing Address

Product Details

Publication date : Feb 25, 2022
Length: 774 pages
Edition : 1st
Language : English
ISBN-13 : 9781801816380
Category :

Packt Subscriptions

See our plans and pricing
Modal Close icon
€18.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
€189.99 billed annually
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just €5 each
Feature tick icon Exclusive print discounts
€264.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just €5 each
Feature tick icon Exclusive print discounts

Frequently bought together


Stars icon
Total 119.97
Machine Learning with PyTorch and Scikit-Learn
€41.99
Modern Time Series Forecasting with Python
€39.99
Deep Learning with TensorFlow and Keras – 3rd edition
€37.99
Total 119.97 Stars icon

Table of Contents

21 Chapters
Giving Computers the Ability to Learn from Data Chevron down icon Chevron up icon
Training Simple Machine Learning Algorithms for Classification Chevron down icon Chevron up icon
A Tour of Machine Learning Classifiers Using Scikit-Learn Chevron down icon Chevron up icon
Building Good Training Datasets – Data Preprocessing Chevron down icon Chevron up icon
Compressing Data via Dimensionality Reduction Chevron down icon Chevron up icon
Learning Best Practices for Model Evaluation and Hyperparameter Tuning Chevron down icon Chevron up icon
Combining Different Models for Ensemble Learning Chevron down icon Chevron up icon
Applying Machine Learning to Sentiment Analysis Chevron down icon Chevron up icon
Predicting Continuous Target Variables with Regression Analysis Chevron down icon Chevron up icon
Working with Unlabeled Data – Clustering Analysis Chevron down icon Chevron up icon
Implementing a Multilayer Artificial Neural Network from Scratch Chevron down icon Chevron up icon
Parallelizing Neural Network Training with PyTorch Chevron down icon Chevron up icon
Going Deeper – The Mechanics of PyTorch Chevron down icon Chevron up icon
Classifying Images with Deep Convolutional Neural Networks Chevron down icon Chevron up icon
Modeling Sequential Data Using Recurrent Neural Networks Chevron down icon Chevron up icon
Transformers – Improving Natural Language Processing with Attention Mechanisms Chevron down icon Chevron up icon
Generative Adversarial Networks for Synthesizing New Data Chevron down icon Chevron up icon
Graph Neural Networks for Capturing Dependencies in Graph Structured Data Chevron down icon Chevron up icon
Reinforcement Learning for Decision Making in Complex Environments Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
(95 Ratings)
5 star 78.9%
4 star 6.3%
3 star 2.1%
2 star 4.2%
1 star 8.4%
Filter icon Filter
Top Reviews

Filter reviews by




Machiel Kruger Feb 22, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Feefo Verified review Feefo
Carlo Estopia Feb 18, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Feefo Verified review Feefo
Kam F Siu Jan 30, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Feefo Verified review Feefo
Robin Samson Jan 29, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Feefo Verified review Feefo
Romee Panchal Apr 30, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Feefo Verified review Feefo