Deep learning with Tensorflow JS projects (Record no. 4996)
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| fixed length control field | 04946nam a22002057a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230321183143.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230321b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789354642401 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | SHA |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Sharma,Umang |
| 245 ## - TITLE STATEMENT | |
| Title | Deep learning with Tensorflow JS projects |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc. | Wiley India Pvt. Ltd. |
| Place of publication, distribution, etc. | New Delhi |
| Date of publication, distribution, etc. | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxi, 245 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 749.00 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Table of content<br/><br/>Chapter 1 Getting Started with TensorFlow JS and ML Web Apps<br/><br/>1.1 Introduction<br/><br/>1.2 Using TF.js in Your Web App<br/><br/>1.3 API Overview of TF.js and its Usage<br/><br/>1.4 Doing Deep Learning in JavaScript<br/><br/>1.5 Engines behind TF.js<br/><br/>1.6 Capabilities of TF.js<br/><br/>1.7 Visualizing Data in TF.js<br/><br/>1.8 ML Web App Architecture<br/><br/> <br/><br/>Chapter 2 Creating a Web App to Perform Sentiment Analysis<br/><br/>2.1 Introduction<br/><br/>2.2 Technical Requirements<br/><br/>2.3 Project Overview<br/><br/>2.4 Sentiment Analysis: Problem and the Solution<br/><br/>2.5 Getting Started<br/><br/>2.6 Building the Sentimental Analysis Web App<br/><br/>2.7 Word Embeddings<br/><br/>2.8 Configuring and Training the Model<br/><br/>2.9 Completing the Train.js<br/><br/>2.10 Creating the UI of the Web Apps<br/><br/>2.11 Loading the Pre-Trained Models<br/><br/>2.12 Creating Index.js of the App<br/><br/>2.13 Binding the Code and Launching the Application<br/><br/>2.14 Training and Creating Visualizations<br/><br/> <br/><br/>Chapter 3 Building a Self-Learning Web App to Perform Addition Using RNNs and GRU<br/><br/>3.1 Introduction<br/><br/>3.2 Recurrent Neural Networks: RNNs<br/><br/>3.3 Project Overview: The Addition Problem<br/><br/>3.4 Technical Requirements<br/><br/>3.5 Creating the Self Learning Addition Web App<br/><br/>3.6 Long Term Dependencies and their Solutions<br/><br/>3.7 Sequence to Sequence Encoder-Decoder Architectures<br/><br/>3.8 Building the Neural Net<br/><br/>3.9 Creating the Backend Engine of the App<br/><br/> <br/><br/>Chapter 4 Creating the Web App for Text Generation Using LSTM<br/><br/>4.1 Introduction<br/><br/>4.2 Technical Requirements<br/><br/>4.3 Understanding the Text Generation Problem<br/><br/>4.4 Long Short-Term Memory Networks<br/><br/>4.5 Resolving Text Generation Problem Using LSTM<br/><br/>4.6 Project Overview<br/><br/> <br/><br/>Chapter 5 Building Webcam-Based PacMan Game on Your Browser Using MobileNet<br/><br/>5.1 Introduction<br/><br/>5.2 Technical Requirements<br/><br/>5.3 The Need for Computationally Efficient CNNs<br/><br/>5.4 Introducing MobileNet Class of Architectures<br/><br/>5.5 Getting Started<br/><br/>5.6 Building the Webcam-Based PacMan Web App Using MobileNet<br/><br/> <br/><br/>Chapter 6 Building Real-Time Pose and Body Parts Detector Using PoseNet<br/><br/>6.1 Introduction<br/><br/>6.2 Technical Requirements<br/><br/>6.3 Pose Estimation Problem<br/><br/>6.4 Atrous Convolutions<br/><br/>6.5 FRCNN Overview<br/><br/>6.6 The PoseNet<br/><br/>6.7 The Output of Google PoseNet<br/><br/>6.8 Getting Started with Pose Estimation Project<br/><br/>6.9 Building the Pose Estimation Web-App<br/><br/>6.10 Deploying PoseNet in Production<br/><br/> <br/><br/>Chapter 7 Getting Invisible and Adding Cool Features to Images Using BodyPix<br/><br/>7.1 Introduction<br/><br/>7.2 Technical Requirements<br/><br/>7.3 Understanding Image Segmentation<br/><br/>7.4 The BodyPix Model and its Capabilities<br/><br/>7.5 Idea Behind BodyPix<br/><br/>7.6 Adding Effects to Pictures Using BodyPix<br/><br/>7.7 Getting Started<br/><br/>7.8 Building the BodyPix Demo Web-App<br/><br/> <br/><br/>Chapter 8 Building a Synthetic Images Generator Using GANs<br/><br/>8.1 Introduction<br/><br/>8.2 Technical Requirements<br/><br/>8.3 Generative Modeling and Basics of Image Statistics<br/><br/>8.4 Generative Adversarial Networks<br/><br/>8.5 Auxiliary Classifier GANs (ACGANs)<br/><br/>8.6 Project Overview<br/><br/>8.7 Building a Web App to Generate Synthetic Images Using ACGANs<br/><br/>8.8 Visualizing the Training using TensorBoard<br/><br/>8.9 Tweaking the Hyper-Parameters<br/><br/> <br/><br/>Chapter 9 Building an App to Encode, Decode, and Generate Images Using Variational Autoencoder<br/><br/>9.1 Introduction<br/><br/>9.2 Autoencoders<br/><br/>9.3 Variational Autoencoders (VAEs)<br/><br/>9.4 Project Overview<br/><br/>9.5 Getting Started<br/><br/>9.6 The MNIST Fashion Dataset<br/><br/>9.7 Building the VAE Web-App<br/><br/> <br/><br/>Chapter 10 Building a Solution to Pole-Cart Problem Using Reinforcement Learning<br/><br/>10.1 Introduction<br/><br/>10.2 Technical Requirements<br/><br/>10.3 Introduction to Reinforcement Learning<br/><br/>10.4 Policy Gradient Method<br/><br/>10.5 The Pole-Cart Problem<br/><br/>10.6 Building the Cart Pole Web App<br/><br/> <br/><br/>Summary<br/><br/>Multiple Choice Questions<br/><br/>Review Questions<br/><br/>Exercises<br/><br/>Index |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Deep Learning with TensorFlow JS Projects aims to teach Deep Learning application development with ease. This book is designed to teach both JS and Machine Learning expertly. Each chapter starts with a bit of theory on a particular Deep Learning concept and then goes on to build a full-fledged fun Web app using the same. Doing Deep Learning in production is critical for its success, and this book intends to teach that. This book can also be used to build a strong foundation of difficult DL concepts such as CNNs, RNNs, GANs, and much more. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Deep learning (Machine learning) |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Book |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Bill No | Bill Date | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Total Checkouts | Full call number | Accession Number | Date last seen | Copy number | Cost, replacement price | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | IT & Decisions Sciences | TB3162 | 16-02-2023 | Indian Institute of Management LRC | Indian Institute of Management LRC | General Stacks | 03/21/2023 | Technical Bureau India Pvt. Ltd. | 524.30 | 006.31 SHA | 004856 | 03/21/2023 | 1 | 749.00 | 03/21/2023 | Book |