Hands-on deep learning algorithms with Python : master deep learning algorithms with extensive math by implementing them using TensorFlow /
This book introduces basic-to-advanced deep learning algorithms used in a production environment by AI researchers and principal data scientists; it explains algorithms intuitively, including the underlying math, and shows how to implement them using popular Python-based deep learning libraries such...
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| Main Author: | |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham :
Packt Publishing Ltd,
2019.
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| Subjects: | |
| Online Access: | CONNECT CONNECT |
MARC
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| 100 | 1 | |a Ravichandiran, Sudharsan, |e author. | |
| 245 | 1 | 0 | |a Hands-on deep learning algorithms with Python : |b master deep learning algorithms with extensive math by implementing them using TensorFlow / |c Sudharsan Ravichandiran. |
| 264 | 1 | |a Birmingham : |b Packt Publishing Ltd, |c 2019. | |
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| 520 | |a This book introduces basic-to-advanced deep learning algorithms used in a production environment by AI researchers and principal data scientists; it explains algorithms intuitively, including the underlying math, and shows how to implement them using popular Python-based deep learning libraries such as TensorFlow. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Section 1: Getting Started with Deep Learning; Chapter 1: Introduction to Deep Learning; What is deep learning?; Biological and artificial neurons; ANN and its layers; Input layer; Hidden layer; Output layer; Exploring activation functions; The sigmoid function; The tanh function; The Rectified Linear Unit function; The leaky ReLU function; The Exponential linear unit function; The Swish function; The softmax function; Forward propagation in ANN; How does ANN learn? | |
| 505 | 8 | |a Debugging gradient descent with gradient checkingPutting it all together; Building a neural network from scratch; Summary; Questions; Further reading; Chapter 2: Getting to Know TensorFlow; What is TensorFlow?; Understanding computational graphs and sessions; Sessions; Variables, constants, and placeholders; Variables; Constants; Placeholders and feed dictionaries; Introducing TensorBoard; Creating a name scope; Handwritten digit classification using TensorFlow; Importing the required libraries; Loading the dataset; Defining the number of neurons in each layer; Defining placeholders | |
| 505 | 8 | |a Forward propagationComputing loss and backpropagation; Computing accuracy; Creating summary; Training the model; Visualizing graphs in TensorBoard; Introducing eager execution; Math operations in TensorFlow; TensorFlow 2.0 and Keras; Bonjour Keras; Defining the model; Defining a sequential model; Defining a functional model; Compiling the model; Training the model; Evaluating the model; MNIST digit classification using TensorFlow 2.0; Should we use Keras or TensorFlow?; Summary; Questions; Further reading; Section 2: Fundamental Deep Learning Algorithms | |
| 505 | 8 | |a Chapter 3: Gradient Descent and Its VariantsDemystifying gradient descent; Performing gradient descent in regression; Importing the libraries; Preparing the dataset; Defining the loss function; Computing the gradients of the loss function; Updating the model parameters; Gradient descent versus stochastic gradient descent; Momentum-based gradient descent; Gradient descent with momentum; Nesterov accelerated gradient; Adaptive methods of gradient descent; Setting a learning rate adaptively using Adagrad; Doing away with the learning rate using Adadelta | |
| 505 | 8 | |a Overcoming the limitations of Adagrad using RMSPropAdaptive moment estimation; Adamax -- Adam based on infinity-norm; Adaptive moment estimation with AMSGrad; Nadam -- adding NAG to ADAM; Summary; Questions; Further reading; Chapter 4: Generating Song Lyrics Using RNN; Introducing RNNs; The difference between feedforward networks and RNNs; Forward propagation in RNNs; Backpropagating through time; Gradients with respect to the hidden to output weight, V; Gradients with respect to hidden to hidden layer weights, W; Gradients with respect to input to the hidden layer weight, U | |
| 500 | |a O'Reilly Online Learning Platform: Academic Edition (SAML SSO Access) |5 TMurS | ||
| 500 | |a EBSCO eBook Academic Comprehensive Collection North America |5 TMurS | ||
| 630 | 0 | 0 | |a TensorFlow. |
| 650 | 0 | |a Python (Computer program language) | |
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