TensorFlow: Working with NLP

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Jonathan Fernandes

41:19

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1. Introduction

001. Why TensorFlow.mp4

00:46

002. What you should know.mp4

00:31

003. What is TensorFlow.mp4

02:29

2. NLP and Transformers

004. What is NLP.mp4

02:45

005. Transformers for NLP.mp4

02:09

006. Transformers, their use, and history.mp4

03:07

007. Challenge NLP model size.mp4

00:45

008. Solution NLP model size.mp4

01:34

3. BERT and Transfer Learning

009. Bias in BERT and GPT.mp4

03:20

010. How was BERT trained.mp4

02:05

011. Transfer learning.mp4

02:51

4. Transformers and BERT

012. Transformer Architecture overview.mp4

01:31

013. BERT model and tokenization.mp4

03:35

014. Tokenizers.mp4

02:16

015. Self-attention.mp4

01:59

016. Multi-head attention and feedforward network.mp4

00:51

017. Fine-tuning BERT.mp4

07:58

5. Conclusion

018. Next steps.mp4

00:47

Description

TensorFlow 2.0 is quickly becoming one of the most popular deep learning frameworks and a must-have skill in your artificial intelligence toolkit. Using a hands-on approach, Jonathan Fernandes covers the key aspects of working with transformers in natural language processing, all in TensorFlow. He goes over the basics of working with text data, and explores transfer learning, fine-tuning BERT, and understanding the transformer model architecture. He also includes challenge/solution sets and assessment questions to help you optimize retention of the material.

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Natural Language Processing

TensorFlow

Jonathan Fernandes

Instructor's Courses

I am an experienced Data Scientist and Machine Learning/ Artificial Intelligence consultant that leads teams to create AI products that help meet business objectives. I have led teams to create solutions for a variety of verticals including banking, healthcare, energy, telecoms and travel and transport. Outside of work I spend time with my fabulous family, swim regularly in the sea throughout the year and am actively involved in my local church.

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