TensorFlow 1.x Deep Learning Cookbook
Over 90 Unique Recipes To Solve ArtificialIntelligence Driven Problems With Python
by Antonio Gulli
Should I read this?
appears in Neural Networks and Neural Network.
Looking for Kindle, hardcover, paperback, or audiobook editions?
Check formats, pricing, and current availability directly.
Why recommended
appears in Neural Networks and Neural Network.
Recommendation Signals
Recommendation proof is sourced from public posts, interviews, reading lists, and cited references.
No verified recommendation proof available yet.
Appears In
Not sure if this is the right fit?
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.”
Similar books
Deep Learning
Ian Goodfellow
HandsOn Machine Learning with ScikitLearn, Keras, and TensorFlow
Aurélien Géron
HandsOn Deep Learning Algorithms with Python
Sudharsan Ravichandiran
Neural Networks and Deep Learning
Charu C. Aggarwal
Deep Learning
Josh PattersonNeural Smithing
Russell ReedNeural Networks for Pattern Recognition
Christopher M. BishopHow recommendation signals are reviewed
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.
TensorFlow 1.x Deep Learning Cookbook
View on Amazon →