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Deep Learning With Transformers

Deep Learning With Transformers


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

Overview

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

Course Information

Python and a Basic Knowledge about Neural Networks

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!

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
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Course Specifications

Our great AI and Machine Learning courses include Artificial Intelligence, Coding and Programming. 

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Adult education is the non-credential activity of gaining skills and improved education. 

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Online education is electronically supported learning that relies on the Internet for teacher/student interaction. 

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A short course is a learning programme that gives you combined content or specific skills training in a short period of time. Short courses often lean towards the more practical side of things and have less theory than a university course – this gives you a more hands-on experience within your field of interest.

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Course duration is 24 hours.

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