Inhaltsangabe
Learn how to redesign NLP applications from scratch.
Key Features
Get familiar with the basics of any Machine Learning or Deep Learning application. Understand how does preprocessing work in NLP pipeline. Use simple PyTorch snippets to create basic building blocks of the network commonly used in NLP. Get familiar with the advanced embedding technique, Generative network, and Audio signal processing techniques.
Description
Natural language processing (NLP) is one of the areas where many Machine Learning and Deep Learning techniques are applied.
This book covers wide areas, including the fundamentals of Machine Learning, Understanding and optimizing Hyperparameters, Convolution Neural Networks (CNN), and Recurrent Neural Networks (RNN). This book not only covers the classical concept of text processing but also shares the recent advancements. This book will empower users in designing networks with the least computational and time complexity. This book not only covers basics of Natural Language Processing but also helps in deciphering the logic behind advanced concepts/architecture such as Batch Normalization, Position Embedding, DenseNet, Attention Mechanism, Highway Networks, Transformer models and Siamese Networks. This book also covers recent advancements such as ELMo-BiLM, SkipThought, and Bert. This book also covers practical implementation with step by step explanation of deep learning techniques in Topic Modelling, Text Generation, Named Entity Recognition, Text Summarization, and Language Translation. In addition to this, very advanced and open to research topics such as Generative Adversarial Network and Speech Processing are also covered.
What you will learn
Learn how to leveraging GPU for Deep Learning Learn how to use complex embedding models such as BERT Get familiar with the common NLP applications Learn how to use GANs in NLP Learn how to process Speech data and implementing it in Speech applications
Who this book is for
This book is a must-read to everyone who wishes to start the career with Machine learning and Deep Learning. This book is also for those who want to use GPU for developing Deep Learning applications.
Table of Contents
1. Understanding the basics of learning Process
2. Text Processing Techniques
3. Representing Language Mathematically
4. Using RNN for NLP
5. Applying CNN In NLP Tasks
6. Accelerating NLP with Advanced Embeddings
7. Applying Deep Learning to NLP tasks
8. Application of Complex Architectures in NLP
9. Understanding Generative Networks
10. Techniques of Speech Processing
11. The Road Ahead
About the Authors
Sunil Patel has completed his master’s in Information Technology from the Indian Institute of Information technology-Allahabad with a thesis focused on investigating 3D protein-protein interactions with deep learning. Sunil has worked with TCS Innovation Labs, Excelra, and Innoplexus before joining to Nvidia. The main areas of research were using Deep Learning, Natural language processing in Banking, and healthcare domain.
Sunil started experimenting with deep learning by implanting the basic layer used in pipelines and then developing complex pipelines for a real-life problem. Apart from this, Sunil has also participated in CASP-2014 in collaboration with SCFBIO-IIT Delhi to efficiently predict possible Protein multimer formation and its impact on diseases using Deep Learning. Currently, Sunil works with Nvidia as Data Scientist – III.
LinkedIn Profile:https://www.linkedin.com/in/linus1/
Über die Autorin bzw. den Autor
Sunil Patel has completed his master’s in Information Technology from the Indian Institute of Information technology-Allahabad with a thesis focused on investigating 3D protein-protein interactions with deep learning. Sunil has worked with TCS Innovation Labs, Excelra, and Innoplexus before joining to Nvidia. The main areas of research were using Deep Learning, Natural language processing in Banking, and healthcare domain. Sunil started experimenting with deep learning by implanting the basic layer used in pipelines and then developing complex pipelines for a real-life problem. Apart from this, Sunil has also participated in CASP-2014 in collaboration with SCFBIO-IIT Delhi to efficiently predict possible Protein multimer formation and its impact on diseases using Deep Learning. Currently, Sunil works with Nvidia as Data Scientist – III. In Nvidia, Sunil has expanded the area of interest to computer vision and simulated environments. At Nvidia, Sunil extensively works in area Banking, defense, and healthcare verticals. The one area where Sunil is currently focused on is using GPUs for high fidelity physics simulation. Sunil has 3 pending US patents and 2 Publications in the domain of Deep Learning.
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