-
Book Overview & Buying
-
Table Of Contents
-
Feedback & Rating

Machine Learning with PyTorch and Scikit-Learn
By :

Machine Learning with PyTorch and Scikit-Learn
By:
Overview of this book
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.
Table of Contents (22 chapters)
Preface
In Progress
| 0 / 6 sections completed |
0%
Giving Computers the Ability to Learn from Data
In Progress
| 0 / 7 sections completed |
0%
Training Simple Machine Learning Algorithms for Classification
In Progress
| 0 / 5 sections completed |
0%
A Tour of Machine Learning Classifiers Using Scikit-Learn
In Progress
| 0 / 9 sections completed |
0%
Building Good Training Datasets – Data Preprocessing
In Progress
| 0 / 8 sections completed |
0%
Compressing Data via Dimensionality Reduction
In Progress
| 0 / 5 sections completed |
0%
Learning Best Practices for Model Evaluation and Hyperparameter Tuning
In Progress
| 0 / 7 sections completed |
0%
Combining Different Models for Ensemble Learning
In Progress
| 0 / 7 sections completed |
0%
Applying Machine Learning to Sentiment Analysis
In Progress
| 0 / 7 sections completed |
0%
Predicting Continuous Target Variables with Regression Analysis
In Progress
| 0 / 10 sections completed |
0%
Working with Unlabeled Data – Clustering Analysis
In Progress
| 0 / 5 sections completed |
0%
Implementing a Multilayer Artificial Neural Network from Scratch
In Progress
| 0 / 7 sections completed |
0%
Parallelizing Neural Network Training with PyTorch
In Progress
| 0 / 7 sections completed |
0%
Going Deeper – The Mechanics of PyTorch
In Progress
| 0 / 10 sections completed |
0%
Classifying Images with Deep Convolutional Neural Networks
In Progress
| 0 / 6 sections completed |
0%
Modeling Sequential Data Using Recurrent Neural Networks
In Progress
| 0 / 5 sections completed |
0%
Transformers – Improving Natural Language Processing with Attention Mechanisms
In Progress
| 0 / 7 sections completed |
0%
Generative Adversarial Networks for Synthesizing New Data
In Progress
| 0 / 6 sections completed |
0%
Graph Neural Networks for Capturing Dependencies in Graph Structured Data
In Progress
| 0 / 7 sections completed |
0%
Reinforcement Learning for Decision Making in Complex Environments
In Progress
| 0 / 7 sections completed |
0%
Other Books You May Enjoy
In Progress
| 0 / 1 sections completed |
0%
Index
In Progress
| 0 / 1 sections completed |
0%
Customer Reviews