Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand ima...
Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks.
作者简介
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Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform.
目录
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Introducing deep learning: why you should learn it
Fundamental concepts: how do machines learn?
Introduction to neural prediction: forward propagation
Introduction to neural learning: gradient descent
Learning multiple weights at a time: generalizing gradient descent
Building your first deep neural network: introduction to backpropagation
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Introducing deep learning: why you should learn it
Fundamental concepts: how do machines learn?
Introduction to neural prediction: forward propagation
Introduction to neural learning: gradient descent
Learning multiple weights at a time: generalizing gradient descent
Building your first deep neural network: introduction to backpropagation
How to picture neural networks: in your head and on paper
Learning signal and ignoring noise:introduction to regularization and batching
Modeling probabilities and nonlinearities: activation functions
Neural learning about edges and corners: intro to convolutional neural networks
Neural networks that understand language: king - man + woman == ?
Neural networks that write like Shakespeare: recurrent layers for variable-length data
Introducing automatic optimization: let's build a deep learning framework
Learning to write like Shakespeare: long short-term memory
Deep learning on unseen data: introducing federated learning
Where to go from here: a brief guide
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原文摘录
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for i in range(len(true)):
error[i] = (pred[i] - true[i]) ** 2
delta = pred[i] - true[i] (查看原文)
The structure trick
When a neural network needs to use the same idea in multiple places, endeavor to use the same weights in both places. This will make those weights more intelligent by giving them more samples to learn from, increasing generalization. (查看原文)
0 有用 llak 2020-02-06 15:09:39
No math,觉得非常友好通俗，有记忆点。作者还分享了学习心得，是通用的。虽然说起来容易做起来难。讲解问题的逐步深入。还讲了framework. 仰慕一下作者的学习能力和才思
0 有用 omom 2020-03-29 22:01:01
小白硬啃了一个月看了个大概，很有收获！顺带的案例还在慢慢学习，希望能更进一步！
0 有用 小茉莉要勇敢呀 2024-05-07 10:32:46 美国
前面讲得很清楚，后面条理有点乱，建议不懂的名词直接搜专门的讲解视频
0 有用 豆友199285186 2021-10-20 12:24:32
挺好的书，前十章是基础，从第十一章开始，难度陡然增加。我建议所有的初学者都应该把里面的代码都学会了，自己学习写个框架，然后自己操作，再去学习类似于pytorch，keras等框架。
2 有用 Hephaestus 2019-02-25 14:45:00
适合入门的一本书