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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Courses and Certificate Fees
| Certificate Availability |
|---|
| no |
The Syllabus
- Guided entry for students who have not taken the first course in the series
- Notational conventions
- Basic ideas: linear regression, classification
- Recipe for Machine Learning
- Neural Networks Overview
- Coding Neural Networks: Tensorflow, Keras
- Practical Colab
- A neural network is a Universal Function Approximator
- Convolutional Neural Networks (CNN): Introduction
- CNN: Multiple input/output features
- CNN: Space and time
- Recurrent Neural Networks (RNN): Introduction
- RNN Overview
- Generating text with an RNN
- Back propagation
- Vanishing and exploding gradients
- Initializing and maintaining weights
- Improving trainability
- How big should my Neural Network be ?
- Interpretation: Preview
- Transfer Learning
- Tensors, Matrix Gradients
- Gradients of an RNN
- RNN Gradients that vanish and explode
- Residual connections
- Neural Programming
- LSTM
- Attention: introduction
- Neural Language Processing (NLP)
- Interpretation: what is going on inside a Neural Network
- Attention
- Adversarial examples
- Final words
Instructors
Articles
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23 Dec'24, 11:20 AM
23 Dec'24, 09:30 AM
23 Dec'24, 09:29 AM
23 Dec'24, 09:12 AM
23 Dec'24, 09:08 AM
19 Dec'24, 09:48 AM
19 Dec'24, 06:44 AM
17 Dec'24, 12:40 PM
17 Dec'24, 04:01 AM
17 Dec'24, 03:43 AM
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