![[Review] Deep Learning from Scratch: Building with Python from First Principles (Seth Weidman) Summarized.](https://episodes.castos.com/660078c6833215-59505987/images/2679531/c1a-085k3-pkkxzqjzhkg-u6itxz.jpg)
Show Notes
Deep Learning from Scratch: Building with Python from First Principles (Seth Weidman)
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- Read more: https://english.9natree.com/read/1492041416/
#Forwardpropagation #Backpropagation #Objectorientedneuralnetworkframework #Convolutionalneuralnetworks #Recurrentneuralnetworks #DeepLearningfromScratch
Deep Learning from Scratch: Building with Python from First Principles is a technical instructional book by Seth Weidman, published by O Reilly in 2019. It is aimed at readers who already have some familiarity with Python, mathematics, and machine learning but want to understand the machinery concealed by high level deep learning libraries. Rather than beginning with model training APIs, the book develops neural network components directly in Python and explains the mathematics that governs them. Its scope moves from dense multilayer networks and supervised learning fundamentals to convolutional and recurrent architectures. Central concerns include forward computation, loss measurement, gradient calculation, parameter updates, and the organization of these operations into reusable software. The book is therefore neither a broad survey of modern AI nor a framework reference. It is a guided examination of how core neural network systems are constructed. By pairing equations with implementations, Weidman seeks to connect conceptual understanding to code that readers can inspect, modify, and use as a basis for later work with production oriented tools.
- Amazon USA Store: https://www.amazon.com/dp/1492041416?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Deep-Learning-from-Scratch%3A-Building-with-Python-from-First-Principles-Seth-Weidman.html
- eBay: https://www.ebay.com/sch/i.html?_nkw=Deep+Learning+from+Scratch+Building+with+Python+from+First+Principles+Seth+Weidman+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/1492041416/
#Forwardpropagation #Backpropagation #Objectorientedneuralnetworkframework #Convolutionalneuralnetworks #Recurrentneuralnetworks #DeepLearningfromScratch
Deep Learning from Scratch: Building with Python from First Principles is a technical instructional book by Seth Weidman, published by O Reilly in 2019. It is aimed at readers who already have some familiarity with Python, mathematics, and machine learning but want to understand the machinery concealed by high level deep learning libraries. Rather than beginning with model training APIs, the book develops neural network components directly in Python and explains the mathematics that governs them. Its scope moves from dense multilayer networks and supervised learning fundamentals to convolutional and recurrent architectures. Central concerns include forward computation, loss measurement, gradient calculation, parameter updates, and the organization of these operations into reusable software. The book is therefore neither a broad survey of modern AI nor a framework reference. It is a guided examination of how core neural network systems are constructed. By pairing equations with implementations, Weidman seeks to connect conceptual understanding to code that readers can inspect, modify, and use as a basis for later work with production oriented tools.
