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Natural Language Processing with TensorFlow
Gain insights into NLP with TensorFlow and Keras, building embeddings, and mastering CNNs, RNNs, transformers for tasks like text generation, translation, and question answering with BERT.
122 Lessons
15h
Updated 5 months ago
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- Familiarity with word embeddings, including the skip-gram model, continuous bag-of-words (CBOW), and Global Vector representations (GloVe)
- Understanding of deep models like CNNs, RNNs, LSTMs, and GRUs
- Hands-on experience building NLP tools using TensorFlow, Keras, and Hugging Face libraries
- Working knowledge of transformers and their variants in sequence-to-sequence modeling
Learning Roadmap
1.
Introduction to Natural Language Processing
Introduction to Natural Language Processing
Get familiar with NLP tasks, traditional approaches, and deep learning advancements.
2.
Understanding TensorFlow 2
Understanding TensorFlow 2
Get started with TensorFlow 2's architecture, operations, data pipelines, and Keras API for neural networks.
Introduction: Understanding TensorFlow 2What Is TensorFlow 2?TensorFlow 2 Architecture: Building and Executing GraphsCafé Le TensorFlow 2: Understanding TensorFlow 2 with an AnalogyFlashback: TensorFlow 1Defining Inputs in TensorFlowBuilding a Data Pipeline Using the tf.data APIDefining Variables and Output in TensorFlowDefining Operations in TensorFlowNeural Network-Related OperationsKeras: TensorFlow's Model-Building APIImplementing Our First Neural NetworkSummary: Understanding TensorFlow 2Quiz: Understanding TensorFlow 2
3.
Word2vec: Learning Word Embeddings
Word2vec: Learning Word Embeddings
10 Lessons
10 Lessons
Examine Word2vec and classical methods for word representation, with TensorFlow implementation.
4.
Advanced Word Vector Algorithms
Advanced Word Vector Algorithms
13 Lessons
13 Lessons
Grasp the fundamentals of advanced word vector algorithms like GloVe and ELMo for NLP.
5.
Sentence Classification with Convolutional Neural Networks
Sentence Classification with Convolutional Neural Networks
13 Lessons
13 Lessons
Dive into CNNs for image and sentence classification with TensorFlow, emphasizing practical applications.
6.
Recurrent Neural Networks
Recurrent Neural Networks
10 Lessons
10 Lessons
Focus on Recurrent Neural Networks' structure, training, and applications in sequence data tasks.
7.
Understanding Long Short-Term Memory Networks
Understanding Long Short-Term Memory Networks
7 Lessons
7 Lessons
Build on LSTM networks to handle short-term and long-term dependencies effectively.
8.
Applications of LSTM: Generating Text
Applications of LSTM: Generating Text
9 Lessons
9 Lessons
Learn how to use LSTMs, GRUs, and beam search for efficient text generation.
9.
Sequence-to-Sequence Learning: Neural Machine Translation
Sequence-to-Sequence Learning: Neural Machine Translation
13 Lessons
13 Lessons
Discover the logic behind sequence-to-sequence learning for neural machine translation, including model architecture, data preparation, training, and evaluation.
10.
Transformers
Transformers
10 Lessons
10 Lessons
Master the steps to leverage transformers and BERT for NLP tasks and question answering.
11.
Image Captioning with Transformers
Image Captioning with Transformers
10 Lessons
10 Lessons
Enhance your skills in image captioning with transformer models using deep learning techniques.
13.
Appendix: Mathematical Foundations and Advanced TensorFlow
Appendix: Mathematical Foundations and Advanced TensorFlow
6 Lessons
6 Lessons
Investigate essential mathematical tools, data structures, and TensorBoard for understanding NLP with TensorFlow.
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Developed by MAANG Engineers
ABOUT THIS COURSE
Deep learning has revolutionized natural language processing (NLP) and NLP problems that require a large amount of work in terms of designing new features. Tuning models can now be efficiently solved using NLP.
In this course, you will learn the fundamentals of TensorFlow and Keras, which is a Python-based interface for TensorFlow. Next, you will build embeddings and other vector representations, including the skip-gram model, continuous bag-of-words, and Global Vector representations. You will then learn about convolutional neural networks, recurrent neural networks, and long short-term memory networks. You’ll also learn to solve NLP tasks like named entity recognition, text generation, and machine translation using them. Lastly, you will learn transformer-based architectures and perform question answering (using BERT) and caption generation.
By the end of this course, you will have a solid foundation in NLP and the skills to build TensorFlow-based solutions for a wide range of NLP problems.
ABOUT THE AUTHOR
Packt
A tech learning platform that provides online courses, eBooks, videos, and other resources to help individuals and organizations stay ahead of emerging and popular technologies.
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ML Engineer
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Software Developer
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Front-end Developer
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Vinay Krishnaiah
Software Developer
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