Deep learning with Python : a Hands-on introduction (Record no. 297467)

MARC details
000 -LEADER
fixed length control field 04574nam a22001817a 4500
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 978144240212
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.133
Item number KET-D
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Ketkar, Nikhil
245 ## - TITLE STATEMENT
Title Deep learning with Python : a Hands-on introduction
Statement of responsibility, etc. By Nikhil Ketkar.
250 ## - EDITION STATEMENT
Edition statement 1
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Name of publisher, distributor, etc. Apress
Date of publication, distribution, etc. c2017.
300 ## - PHYSICAL DESCRIPTION
Extent i-xvii+226P.
500 ## - GENERAL NOTE
General note First south Asian Edition published in 2019.
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note Chapter 1: Introduction to Deep Learning; Historical Context; Advances in Related Fields; Prerequisites ; Overview of Subsequent Chapters; Installing the Required Libraries ;<br/> 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; <br/>Chapter 4: Introduction to Theano; What is Theano; Theano Hands-On; Summary; <br/>Chapter 5: Convolutional Neural Networks; Convolution Operation; Pooling Operation; Convolution-Detector-Pooling Building Block; Convolution Variants; Intuition behind CNNs; Summary.<br/> Chapter 6: Recurrent Neural NetworksRNN Basics; Training RNNs; Bidirectional RNNs; Gradient Explosion and Vanishing; Gradient Clipping; Long Short Term Memory; Summary; <br/>Chapter 7: Introduction to Keras; Summary;<br/> 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;<br/> 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;<br/> Chapter 10: Introduction to GPUs;<br/>Chapter 11: Introduction to TensorflowChapter 12: Introduction to PyTorchChapter 13: Regularization TechniquesChapter 14: Training Deep Leaning Models
520 ## - SUMMARY, ETC.
Summary, etc. Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. You will: Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning. Python (Computer program language) Data mining.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Book
Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Current library Shelving location Date acquired Total Checkouts Full call number Barcode Date last seen Date last checked out Price effective from Koha item type
        Dept. of Computational Biology and Bioinformatics Dept. of Computational Biology and Bioinformatics Processing Center 22/10/2021 2 005.133 KET-D DCB3984 20/06/2022 15/01/2022 22/10/2021 Book
        Dept. of Linguistics Dept. of Linguistics Processing Center 12/11/2021 1 005.133 KET LIN10633 16/05/2023 08/02/2022 12/11/2021 Book