Deep learning with Python : a hands-on introduction /: a hands-on introduction. (2017)

• Record Type: Book
• 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 (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.
• 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.