Deep learning with Python : a hands-on introduction /: a hands-on introduction. (2017)
- Record Type:
- Book
- Title:
- Deep learning with Python : a hands-on introduction /: a hands-on introduction. (2017)
- Main Title:
- Deep learning with Python : a hands-on introduction
- Further Information:
- Note: Nikhil Ketkar.
- Authors:
- Ketkar, Nikhil
- Contents:
- At a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Chapter 1: Introduction to Deep Learning; Historical Context; Advances in Related Fields; Prerequisites ; Overview of Subsequent Chapters; Installing the Required Libraries ; Chapter 2: Machine Learning Fundamentals; Intuition; Binary Classification; Regression; Generalization; Regularization; Summary; Chapter 3: Feed Forward Neural Networks; Unit; Overall Structure of a Neural Network; Expressing the Neural Network in Vector Form; Evaluating the output of the Neural Network. Training the Neural NetworkDeriving Cost Functions using Maximum Likelihood; Binary Cross Entropy; Cross Entropy; Squared Error; Summary of Loss Functions; Types of Units/Activation Functions/Layers; Linear Unit; Sigmoid Unit; Softmax Layer; Rectified Linear Unit (ReLU); Hyperbolic Tangent; Neural Network Hands-on with AutoGrad; Summary; Chapter 4: Introduction to Theano; What is Theano; Theano Hands-On; Summary; Chapter 5: Convolutional Neural Networks; Convolution Operation; Pooling Operation; Convolution-Detector-Pooling Building Block; Convolution Variants; Intuition behind CNNs; Summary. Chapter 6: Recurrent Neural NetworksRNN Basics; Training RNNs; Bidirectional RNNs; Gradient Explosion and Vanishing; Gradient Clipping; Long Short Term Memory; Summary; Chapter 7: Introduction to Keras; Summary; Chapter 8: Stochastic Gradient Descent; Optimization Problems; Method of Steepest Descent; Batch, Stochastic (SingleAt a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Chapter 1: Introduction to Deep Learning; Historical Context; Advances in Related Fields; Prerequisites ; Overview of Subsequent Chapters; Installing the Required Libraries ; Chapter 2: Machine Learning Fundamentals; Intuition; Binary Classification; Regression; Generalization; Regularization; Summary; Chapter 3: Feed Forward Neural Networks; Unit; Overall Structure of a Neural Network; Expressing the Neural Network in Vector Form; Evaluating the output of the Neural Network. Training the Neural NetworkDeriving Cost Functions using Maximum Likelihood; Binary Cross Entropy; Cross Entropy; Squared Error; Summary of Loss Functions; Types of Units/Activation Functions/Layers; Linear Unit; Sigmoid Unit; Softmax Layer; Rectified Linear Unit (ReLU); Hyperbolic Tangent; Neural Network Hands-on with AutoGrad; Summary; Chapter 4: Introduction to Theano; What is Theano; Theano Hands-On; Summary; Chapter 5: Convolutional Neural Networks; Convolution Operation; Pooling Operation; Convolution-Detector-Pooling Building Block; Convolution Variants; Intuition behind CNNs; Summary. Chapter 6: Recurrent Neural NetworksRNN Basics; Training RNNs; Bidirectional RNNs; Gradient Explosion and Vanishing; Gradient Clipping; Long Short Term Memory; Summary; Chapter 7: Introduction to Keras; Summary; Chapter 8: Stochastic Gradient Descent; Optimization Problems; Method of Steepest Descent; Batch, Stochastic (Single and Mini-batch) Descent; Batch; Stochastic Single Example; Stochastic Mini-batch; Batch vs. Stochastic; Challenges with SGD; Local Minima; Saddle Points; Selecting the Learning Rate; Slow Progress in Narrow Valleys; Algorithmic Variations on SGD; Momentum. Nesterov Accelerated Gradient (NAS)Annealing and Learning Rate Schedules; Adagrad; RMSProp; Adadelta; Adam; Resilient Backpropagation; Equilibrated SGD; Tricks and Tips for using SGD; Preprocessing Input Data; Choice of Activation Function; Preprocessing Target Value; Initializing Parameters; Shuffling Data; Batch Normalization; Early Stopping; Gradient Noise; Parallel and Distributed SGD; Hogwild; Downpour; Hands-on SGD with Downhill; Summary; Chapter 9: Automatic Differentiation; Numerical Differentiation; Symbolic Differentiation; Automatic Differentiation Fundamentals. Forward/Tangent Linear ModeReverse/Cotangent/Adjoint Linear Mode; Implementation of Automatic Differentiation; Source Code Transformation; Operator Overloading; Hands-on Automatic Differentiation with Autograd; Summary; Chapter 10: Introduction to GPUs; Summary; Index. … (more)
- Publisher Details:
- United States : Apress
- Publication Date:
- 2017
- Extent:
- 1 online resource
- Subjects:
- 005.13/3
Computer science
Machine learning
Python (Computer program language)
Data mining
COMPUTERS -- Programming Languages -- Python
Data mining
Machine learning
Python (Computer program language)
Computer Science
Computing Methodologies
Programming Techniques
Programming Languages, Compilers, Interpreters
Mathematical Logic and Formal Languages
Computers -- Programming -- General
Computers -- Programming Languages -- General
Mathematics -- Logic
Computer programming / software development
Programming & scripting languages: general
Mathematical theory of computation
Artificial intelligence
Computers -- Intelligence (AI) & Semantics
Artificial intelligence
Electronic books - Languages:
- English
- ISBNs:
- 9781484227664
1484227662 - Related ISBNs:
- 9781484227657
1484227654 - Notes:
- Note: Includes bibliographical references.
Note: Online resource; title from PDF title page (EBSCO, viewed April 20, 2017). - Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.368929
- Ingest File:
- 01_349.xml