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Through a recent series of 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 bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.
With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you’ve learned. Programming experience is all you need to get started.
Use Scikit-learn to track an example ML project end to end Explore several models, including support vector machines, decision trees, random forests, and ensemble methods Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
From the Publisher
Prerequisites
This book assumes that you have some Python programming experience and that you are familiar with Python’s main scientific libraries, in particular NumPy, Pandas, and Matplotlib.
Also, if you care about what’s under the hood, you should have a reasonable understanding of college-level math as well (calculus, linear algebra, probabilities, and statistics).
About this Book
Machine Learning in Your Projects
So, naturally you are excited about Machine Learning and would love to join the party! Perhaps you’d like to give your homemade robot a brain of its own? Make it recognize faces? Or learn to walk around? Or maybe your company has tons of data (user logs, financial data, production data, machine sensor data, hotline stats, HR reports, etc.), and more than likely you could unearth some hidden gems if you just knew where to look. With Machine Learning, you can accomplish the following & much more:
Segment customers and find the best marketing strategy for each group. Recommend products for each client based on what similar clients bought. Detect which transactions are likely to be fraudulent. Forecast next year’s revenue.
Objective and Approach
This book assumes that you know close to nothing about Machine Learning. Its goal is to give you the concepts, tools, and intuition you need to implement programs capable of learning from data.
We will cover a large number of techniques, from the simplest and most commonly used (such as Linear Regression) to some of the Deep Learning techniques that regularly win competitions. For this, we will be using production-ready Python frameworks:
Scikit-Learn is very easy to use, yet it implements many Machine Learning algorithms efficiently, so it makes for a great entry point to learning Machine Learning.
TensorFlow is a more complex library for distributed numerical computation. It makes it possible to train and run very large neural networks efficiently by distributing the computations across potentially hundreds of multi-GPU (graphics processing unit) servers. TensorFlow (TF) was created at Google and supports many of its large-scale Machine Learning applications.
Keras is a high-level Deep Learning API that makes it very simple to train and run neural networks. Keras comes bundled with TensorFlow, and it relies on TensorFlow for all the intensive computations.
Publisher : O’Reilly Media; 3rd edition (November 8, 2022)
Language : English
Paperback : 861 pages
ISBN-10 : 1098125975
ISBN-13 : 978-1098125974
Item Weight : 2.97 pounds
Dimensions : 7.25 x 2 x 9.5 inches
