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...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing Ltd,
2019.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 online resource |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781789344516 1789344514 |
Internformat
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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 | |
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| contents | 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? 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 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 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 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 |
| ctrlnum | (OCoLC)1110716984 |
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| publisher | Packt Publishing Ltd, |
| record_format | marc |
| spelling | Ravichandiran, Sudharsan, author. Hands-on deep learning algorithms with Python : master deep learning algorithms with extensive math by implementing them using TensorFlow / Sudharsan Ravichandiran. Birmingham : Packt Publishing Ltd, 2019. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier 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. Includes bibliographical references and index. 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? 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 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 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 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 TensorFlow. http://id.loc.gov/authorities/names/n2019020612 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Python (Langage de programmation) Logiciels d'application Développement. Application software Development fast Computer algorithms fast Machine learning fast Python (Computer program language) fast Electronic book. has work: Hands-On Deep Learning Algorithms with Python (Text) https://id.oclc.org/worldcat/entity/E39PCYMDxRGGxWJc8xKC44xrC3 https://id.oclc.org/worldcat/ontology/hasWork Print version: 1789344158 9781789344158 (OCoLC)1083564019 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2207102 Volltext |
| spellingShingle | Ravichandiran, Sudharsan Hands-on deep learning algorithms with Python : master deep learning algorithms with extensive math by implementing them using TensorFlow / 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? 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 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 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 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 TensorFlow. http://id.loc.gov/authorities/names/n2019020612 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Python (Langage de programmation) Logiciels d'application Développement. Application software Development fast Computer algorithms fast Machine learning fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/names/n2019020612 http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh95009362 |
| title | Hands-on deep learning algorithms with Python : master deep learning algorithms with extensive math by implementing them using TensorFlow / |
| title_auth | Hands-on deep learning algorithms with Python : master deep learning algorithms with extensive math by implementing them using TensorFlow / |
| title_exact_search | Hands-on deep learning algorithms with Python : master deep learning algorithms with extensive math by implementing them using TensorFlow / |
| title_full | Hands-on deep learning algorithms with Python : master deep learning algorithms with extensive math by implementing them using TensorFlow / Sudharsan Ravichandiran. |
| title_fullStr | Hands-on deep learning algorithms with Python : master deep learning algorithms with extensive math by implementing them using TensorFlow / Sudharsan Ravichandiran. |
| title_full_unstemmed | Hands-on deep learning algorithms with Python : master deep learning algorithms with extensive math by implementing them using TensorFlow / Sudharsan Ravichandiran. |
| title_short | Hands-on deep learning algorithms with Python : |
| title_sort | hands on deep learning algorithms with python master deep learning algorithms with extensive math by implementing them using tensorflow |
| title_sub | master deep learning algorithms with extensive math by implementing them using TensorFlow / |
| topic | TensorFlow. http://id.loc.gov/authorities/names/n2019020612 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Python (Langage de programmation) Logiciels d'application Développement. Application software Development fast Computer algorithms fast Machine learning fast Python (Computer program language) fast |
| topic_facet | TensorFlow. Python (Computer program language) Application software Development. Python (Langage de programmation) Logiciels d'application Développement. Application software Development Computer algorithms Machine learning Electronic book. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2207102 |
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