Grokking deep learning

By:

Trask, Andrew W

Material type: Text

9781617293702

Subject(s):

DDC classification:

006.31 TRA-D

Contents:

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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Holdings
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	10/06/2024	012943

Total holds: 0

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Previous	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 :	Next

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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