Summary: Understanding TensorFlow 2

Explore the foundational components of TensorFlow 2 essential for NLP, including its architecture, data handling pipelines, and various operations. Understand how to utilize Keras APIs to build neural networks, culminating in implementing a three-layer model to classify digits. This lesson equips you with practical knowledge to develop NLP solutions using TensorFlow 2.

We'll cover the following...

TensorFlow architecture
- TensorFlow client
- TensorFlow operations
Using Keras with TensorFlow
Neural network implementation

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. ...

1.Introduction to Natural Language Processing

2.Understanding TensorFlow 2

3.Word2vec: Learning Word Embeddings

4. Advanced Word Vector Algorithms

5.Sentence Classification with Convolutional Neural Networks

6.Recurrent Neural Networks

7.Understanding Long Short-Term Memory Networks

8.Applications of LSTM: Generating Text

9.Sequence-to-Sequence Learning: Neural Machine Translation

10.Transformers

Project

11.Image Captioning with Transformers

12.Final Remarks

13.Appendix: Mathematical Foundations and Advanced TensorFlow

Mock Interview

Summary: Understanding TensorFlow 2

TensorFlow architecture