Grokking deep learning
Trask, Andrew W.
Grokking deep learning by Andrew W. Trask. - Greenwich : Manning, ©2019 - 309 p. : ill. ; 24 cm.
Including index.
Chapter 1. Introducing deep learning: why you should learn it
Chapter 2. Fundamental concepts: how do machines learn?
Chapter 3. Introduction to neural prediction: forward propagation Chapter 4. Introduction to neural learning: gradient descent Chapter 5. Learning multiple weights at a time: generalizing gradient descent
Chapter 6. Building your first deep neural network: introduction to backpropagation Chapter 7. How to picture neural networks: in your head and on paper Chapter 8. Learning signal and ignoring noise: introduction to regularization and batching Chapter 9. Modeling probabilities and nonlinearities: activation functions
Chapter 10. Neural learning about edges and corners: intro to convolutional neural networks
Chapter 11. Neural networks that understand language: king – man + woman == ? Chapter 12. Neural networks that write like Shakespeare: recurrent layers for variable-length data Chapter 13. Introducing automatic optimization: let’s build a deep learning framework Chapter 14. Learning to write like Shakespeare: long short-term memory Chapter 15. Deep learning on unseen data: introducing federated learning
Chapter 16. Where to go from here: a brief guide
9781617293702
neural networks
language modeling
006.31 / TRA-D
Grokking deep learning by Andrew W. Trask. - Greenwich : Manning, ©2019 - 309 p. : ill. ; 24 cm.
Including index.
Chapter 1. Introducing deep learning: why you should learn it
Chapter 2. Fundamental concepts: how do machines learn?
Chapter 3. Introduction to neural prediction: forward propagation Chapter 4. Introduction to neural learning: gradient descent Chapter 5. Learning multiple weights at a time: generalizing gradient descent
Chapter 6. Building your first deep neural network: introduction to backpropagation Chapter 7. How to picture neural networks: in your head and on paper Chapter 8. Learning signal and ignoring noise: introduction to regularization and batching Chapter 9. Modeling probabilities and nonlinearities: activation functions
Chapter 10. Neural learning about edges and corners: intro to convolutional neural networks
Chapter 11. Neural networks that understand language: king – man + woman == ? Chapter 12. Neural networks that write like Shakespeare: recurrent layers for variable-length data Chapter 13. Introducing automatic optimization: let’s build a deep learning framework Chapter 14. Learning to write like Shakespeare: long short-term memory Chapter 15. Deep learning on unseen data: introducing federated learning
Chapter 16. Where to go from here: a brief guide
9781617293702
neural networks
language modeling
006.31 / TRA-D