Deep learning approach for natural language processing, speech, and computer vision : techniques and use cases /: techniques and use cases. (2023)

• Record Type: Book
• Further Information: Note: L. Ashok Kumar and D. Karthika Renuka.
• Authors: Kumar, L. Ashok; Renukay, D. Karthika, 1981-
• Contents: 1. Introduction. 1.1. Introduction. 1.2. Machine Learning methods for NLP, CV and Speech. 1.3 Tools, Libraries, Datasets and Resourses for the Practiontioners. 2. Natural Language Processing. 2.1 Natural Language Processing. 2.2 Generic NLP Pipeline. 2.3 Text Pre-processing. 2.4 Feature Engineering. 2.5 Modeling. 2.6 Evaluation. 2.7 Deployment. 2.8 Monitoring and Model Updating. 2.9 Vector Representation for NLP. 2.10 Language modeling with N-grams. 2.11 Vector Semantics and Embeddings. 3. State-of the Art Natural Language Processing. 3.1 Introduction. 3.2 Sequence to Sequence Models. 3.3 Recurrent Neural Networks. 3.4 Attention Mechanisms. 3.5 Transformer Model. 4. Applications of Natural Language Processing. 4.1 Introduction. 4.2 Word Sense Disambiguation. 4.3 Text Classification. 4.4 Sentiment Analysis. 4.5 Spam E-mail Classification. 4.6 Question Answering. 4.7 Chatbots and Dialogue Systems. 5. Fundamentals of Speech Recognition. 5.1 Introduction. 5.2 Structure of Speech. 5.3 Basic Audio Features. 5.4 Characteristics of Speech Recognition System. 5.5 Working of a Speech Recognition System. 5.6 Audio Feature Extraction Techniques. 5.7 Statistical Speech Recognition. 5.8 Speech Recognition Applications. 5.9 Challenges in Speech Recognition. 6. Deep Learning models for Speech Recognition. 6.1 Traditional methods of Speech Recognition. 6.2 RNN-Based Encoder-Decoder Architecture. 6.3 Attention based Encoder-Decoder Architecture. 6.4 Challenges in traditional ASR and motivation for end-to-end ASR. 7. End to End Speech Recognition Models.7.1 End to End Speech Recognition Models. 7.2 Self-Supervised Models for Automatic Speech Recognition. 7.3 Online/Streaming ASR. 8. Computer Vision Basics.8.1 Introduction. 8.2 Image segmentation. 8.3 Feature extraction. 8.4 Image Classification. 8.5 Tools and Libraries for computer vision. 8.6 Applications of computer vision. 9. Deep Learning models for Computer Vision.9.1 Deep Learning for Computer Vision. 9.2 Pre Trained Architectures for Computer Vision. 10. Applications of Computer Vision.10.1 Introduction. 10.2 Optical Character Recognition. 10.3 Face and Facial Expresion Recognition. 10.4 Visual based gesture recognition. 10.5 Posture detection and Correction. ;
• Edition: 1st
• Publisher Details: Boca Raton : CRC Press
• Publication Date: 2023
• Extent: 1 online resource (226 pages), illustrations (black and white)
• Subjects: 006.35; Natural language processing (Computer science); Computer vision; Deep learning (Machine learning)
• Languages: English
• ISBNs: 9781000875607; 9781000875577
• Related ISBNs: 9781032391656
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; resource not viewed.
• 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.