TensorFlow architecture

In this chapter, we took our first steps to solving NLP tasks by understanding the primary underlying platform (TensorFlow) on which we’ll be implementing our algorithms. First, we discussed the underlying details of TensorFlow architecture. Next, we discussed the essential ingredients of a meaningful TensorFlow program. We got to know some new features in TensorFlow 2, such as the AutoGraph feature, in depth. We then discussed more exciting elements in TensorFlow, such as data pipelines and various TensorFlow operations. ...

Introduction to Natural Language Processing

Understanding TensorFlow 2

Word2vec: Learning Word Embeddings

Advanced Word Vector Algorithms

Sentence Classification with Convolutional Neural Networks

Recurrent Neural Networks

Understanding Long Short-Term Memory Networks

Applications of LSTM: Generating Text

Sequence-to-Sequence Learning: Neural Machine Translation

Transformers

Sarcasm Classification Using BERT

Image Captioning with Transformers

Caption Generation Using PyTorch

Final Remarks

Appendix: Mathematical Foundations and Advanced TensorFlow

Summary: Understanding TensorFlow 2

TensorFlow architecture