Master deep learning algorithms with extensive math by implementing them using TensorFlow
by Sudharsan Ravichandiran
Recommended by Kirk Borne
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Recommended by 1 source.
Understand basic to advanced deep learning algorithms, the mathematical principles behind them, and their practical applications. Key Features Get uptospeed with building your own neural networks from scratch Gain insights into the mathematical principles behind deep learning algorithms Implement popular deep learning algorithms such as CNNs, RNN...
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Why recommended
Recommended by 1 source.
People and public figures who have recommended this book.
Recommendation proof is sourced from public posts, interviews, reading lists, and cited references.
Kirk Borne
“#DataScientist’s Dilemma: The Cold Start Problem – see 10 #MachineLearning Examples: ———————— #BigData #DataScience #Algorithms #DataLiteracy #MetaLearning #AI #NeuralNetworks #DeepLearning #Mathematics ————— +See this book”
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Consider Deep Learning by Ian Goodfellow. Recommended by 10 sources.
“Equation-forward introduction covering probability, linear-algebra foundations, optimization methods, model families, and common architectures. Sections trade short conceptual summaries for formal derivations and algorithm descriptions; occasional practical notes appear but runnable code is rare. Most useful for building a technical picture of why methods behave as they do and for informed follow-up experimentation. Main limitation: dense notation and extended proofs demand slow, focused study, so readers seeking hands-on walkthroughs will be left wanting.”
Each recommendation is collected from a public source — interviews, articles, or curated lists — and linked to its original URL. Books with many verifiable recommendations from respected people rank higher.
