{"id":13679,"date":"2023-12-05T18:18:42","date_gmt":"2023-12-05T18:18:42","guid":{"rendered":"https:\/\/lacountystore.com\/\/?p=13679"},"modified":"2023-12-05T18:18:42","modified_gmt":"2023-12-05T18:18:42","slug":"best-books-on-deep-learning","status":"publish","type":"post","link":"https:\/\/lacountystore.com\/best-books-on-deep-learning\/","title":{"rendered":"20 Best Books On Deep Learning (2023 Update)"},"content":{"rendered":"

Are you looking for the Best Books On Deep Learning<\/strong>? If so, you’ve come to the right place.<\/p>\n

Choosing the Best Books On Deep Learning can be difficult as there are so many considerations, such as Apple, Elsevier, Jellycat, LEGO, Melissa & Doug, Penguin Random House, WHSmith, Amazon.com. We have done a lot of research to find the Top 20 Best Books On Deep Learning<\/strong> available.<\/p>\n

The average cost is $34.00<\/i>. Sold comparable range in price from a low of $5.90<\/i> to a high of $62.99<\/i>.<\/p>\n

Based on the research we did, we think Deep Learning: A Practitioner's Approach [Book]<\/strong><\/a> is the best overall. Read on for the rest of the great options and our buying guide, where you can find all the information you need to know before making an informed purchase.<\/p>\n

20 Best Books On Deep Learning (19 Sellers)<\/h2>\n
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Product Image<\/span><\/th>\nProduct Name<\/span><\/th>\nFeatures<\/span><\/th>\nCheck Price<\/span><\/th>\n<\/tr>\n<\/thead>\n
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    • Dive into machine learning concepts in general, as well as deep learning in particular<\/li>\n
    • Understand how deep networks evolved from neural network fundamentals<\/li>\n
    • Explore the major deep network architectures, including convolutional and recurrent<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Using the pytorch tensor api<\/li>\n
    • Understanding automatic differentiation in pytorch<\/li>\n
    • Training deep neural networks<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Train models in computer vision, natural language processing, tabular data, and collaborative filtering<\/li>\n
    • Learn the latest deep learning techniques that matter most in practice<\/li>\n
    • Improve accuracy, speed, and reliability by understanding how deep learning models work<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Binding type: paperback<\/li>\n
    • Year published: 2022-07-26<\/li>\n
    • Number of pages: 568<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Format: ebook<\/li>\n
    • Rental duration: lifetime<\/li>\n
    • Author: amita kapoor; antonio gulli; sujit pal; francois chollet<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • You have to plunge in the water of machine learning for a few years before coming back.<\/li>\n
    • Don&#39;t talk to me if it&#39;s not a doctor of the computer!-getting started is simple: 10 years of experience should be almost enough. it&#39;s probably enough.<\/li>\n
    • To sum it up: only great gods can do deep learning.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • About the book.<\/li>\n
    • A project-based guide to the basics of deep learning.<\/li>\n
    • Book synopsis.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • The dust jacket if present may be marked and have considerable heavy wear or might be missing.<\/li>\n
    • The book might be ex-library copy and may have the markings and stickers associated from the library.<\/li>\n
    • The book may have considerable highlights\/notes\/underlined pages but the text is legible.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Grand central pub<\/li>\n
    • Brand: grand central publishing<\/li>\n
    • Easy to understand<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Binding type: paperback<\/li>\n
    • Year published: 2019-09-02<\/li>\n
    • Number of pages: 296<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Care instruction: keep away from fire.<\/li>\n
    • It can be used as a gift.<\/li>\n
    • It is made up of premium quality material.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Key features.<\/li>\n
    • Structuring problems as markov decision processes.<\/li>\n
    • Popular algorithms such deep q-networks, policy gradient method and evolutionary algorithms and the intuitions that drive them.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Build machine and deep learning systems with the newly released tensorflow 2 and keras for the lab, production, and mobile devices<\/li>\n
    • Key features<\/li>\n
    • What you will learn<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation<\/li>\n
    • See how dl frameworks make it easier to develop more complicated and useful neural networks<\/li>\n
    • Discover how convolutional neural networks (cnns) revolutionize image classification and analysis<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Use matlab for deep learning<\/li>\n
    • Discover neural networks and multi-layer neural networks<\/li>\n
    • Work with convolution and pooling layers<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • The deep learning framework<\/li>\n
    • Vignettes and case studies from k-12 classrooms in 1,200 schools in seven countries<\/li>\n
    • Guidance for reaching disadvantaged and differently abled students<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • By: ian goodfellow; yoshua bengio; aaron courville<\/li>\n
    • The mit press<\/li>\n
    • Print isbn: 9780262035613, 0262035618<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Explore the machine learning landscape, particularly neural nets<\/li>\n
    • Use scikit-learn to track an example machine-learning project end-to-end<\/li>\n
    • Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Explore the machine learning landscape, particularly neural nets<\/li>\n
    • Use scikit-learn to track an example machine-learning project end-to-end<\/li>\n
    • Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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    • Deep learning from first principles<\/li>\n
    • Setting up your own deep-learning environment<\/li>\n
    • Image classification and generation<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/td>\n
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