NLP is a critical aspect in almost every sector in the modern world, including web search, advertising, and even customer relations. This would make these applications even more potent if deep learning forms the foundation of the applications. ‘’Hands-on Natural Language Processing with Python” covers all the topics, from the basic to the advanced level, to help the readers understand how to use deep learning models to solve various NLP problems, adhering to the current standards.
At the beginning of the book, you will be introduced to NLP and deep learning basics, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and semantic embedding, as well as Word2vec. I will show you how to train and use these neural networks to complete every NLP task I’ve described. Popular use cases like text classification and sequence labeling are expounded, which are extremely helpful for developing sentiment analysis, chatbots for customer use, and exploring for outliers. You will also learn how to implement some of the algorithms using TensorFlow, a deep-learning library for Python.
By the end of this book, you, the reader of “Hands-on Natural Language Processing with Python,” will have all you need to develop high-level NLP applications supported by deep learning. I will give the example of the course where you can study word vector arithmetic, distinguishing between the twis of tweets and a news, and functioning of question-answer systems with the help of RNN, besides, you will get knowledge about generation of voice with the help of WaveNet. Also, you will be introduced to text-to-speech and speech-to-text and training of the models using DeepSpeech. This knowledge will enable you to address common NLP difficulties in your projects and appropriately apply the most advanced solutions.
Hands-on Natural Language Processing with Python Table of Contents:
- Getting Started
- Text Classification and POS Tagging Using NLTK
- Deep Learning and TensorFlow
- Semantic Embedding Using Shallow Models
- Text Classification Using LSTM
- Searching and DeDuplicating Using CNNs
- Named Entity Recognition Using Character LSTM
- Text Generation and Summarization Using GRUs
- Question-Answering and Chatbots Using Memory Networks
- Machine Translation Using the Attention-Based Model
- Speech Recognition Using DeepSpeech
- Text-to-Speech Using Tacotron
- Deploying Trained Models
Who is this course for?
- Deep learning application developers who wish to implement NLP for constructing their particular apps.
- Any machine learning engineers interested in the improvement of their proficiency in NLP.
- It is seen that the NLP engineers want to adopt deep learning in their projects.
- Machine learning specialists willing to try deep learning for NLP tasks.
