Deep Learning with Transformers

Description:

Learn the state-of-art Transformers for NLP and how to implement them from scratch!

Basic knowledge:

Python and a Basic Knowledge about Neural Networks

Students in machine learning, deep learning, artificial intelligence, and data science. Professionals in machine learning, deep learning, artificial intelligence, and data science. Anyone interested in state-of-the-art natural language processing. Featured review.

Course Completion Certificate

What will you learn:

In this course, we will cover the following in full detail:

• Course Outline
• Sequence Modelling
• RNNs and LSTMs
• Attention Mechanism
• How Attention Works
• Word Embeddings
• Visualizing and Measuring Word Embeddings
• Introduction to Transformers
• Input Embeddings
• Positional Encoding
• MultiHead Attention
• Concat and Linear
• Residual Learning
• Layer Normalization
• Feed Forward
• Masked MultiHead Attention
• MultiHead Attention in Decoder
• Cross Entropy Loss
• KL Divergence Loss
• Label Smoothing
• Dropout
• Learning Rate Warmup
• All concepts discussed above are visualized with diagrams to ease your understanding!

After that, we will implement all what we've learned above to build a Chatbot in PyTorch!

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Why Simpliv

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