Description: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 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 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 Shipping We offer FREE shipping on specialized orders! We ship within Three business days of payment, usually sooner. We use a selection of shipping services such as UPS, FedEx, USPS etc. We only ship to the lower 48 states, no APO/FPO addresses or PO Boxes allowed. Local pickups and combined shipping options are not provided at this time. Return You can return a product for up to 30 days from the date you purchased it. Any product you return must be in the same condition you received it and in the original packaging. Please keep the receipt. Payment We accept payment by any of the following methods:PayPalPlease pay as soon as possible after winning an auction, as that will allow us to post your item to you sooner!Credit/Debit CardPlease pay within 2 days of buying now, as it makes it easier to ship as fast as possible to you! Feedback Customer satisfaction is very important to us. If you have any problem with your order, please contact us and we will do our best to make you satisfied. Contact Us If you have any queries, please contact us via ebay. We usually respond within 24 hours on weekdays. Please visit our eBay store to check out other items for sale! Thank you for shopping at our store.
Price: 102.92 USD
Location: San Gabriel, California
End Time: 2024-11-01T23:31:44.000Z
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Return shipping will be paid by: Buyer
All returns accepted: Returns Accepted
Item must be returned within: 30 Days
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EAN: 9781098125974
ISBN: 9781098125974
Package Dimensions LxWxH: 10.0x7.13x1.77 Inches
Weight: 2.84 Pounds
MPN: Does not apply
Model: Does not apply
Brand: None
Item Length: 9.2in
Item Height: 1.7in
Item Width: 6.9in
Author: Aurélien Géron
Publication Name: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems
Format: Trade Paperback
Language: English
Publisher: O'reilly, Incorporated
Publication Year: 2022
Type: Textbook
Item Weight: 52.7 Oz
Number of Pages: 861 Pages
