Grokking deep learning
Material type: TextPublication details: Greenwich : Manning, ©2019Description: 309 p. : ill. ; 24 cmISBN:- 9781617293702
- 006.31 TRA-D
Item type | Current library | Collection | Call number | Status | Notes | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|---|
Books | IIITD Reference | Computer Science and Engineering | CB 006.31 TRA-D (Browse shelf(Opens below)) | Checked out | DBT Project Grant | 29/01/2025 | 012943 |
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CB 006.310 MUR-P Probabilistic machine learning : advanced topics | CB 006.31 STA-S The statquest illustrated guide to machine learning | CB 006.31 SUT-R Reinforcement learning : an introduction | CB 006.31 TRA-D Grokking deep learning | CB 006.312 WIC-R R for data science : | CB 006.32 STE-D Deep learning with PyTorch | CB 006.35 RAV-G Getting started with Google BERT : build and train state-of-the-art natural language processing models using BERT |
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
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