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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
1 Sep'23, 12:44 PM
1 Sep'23, 12:27 PM
1 Sep'23, 12:02 PM
1 Sep'23, 11:26 AM
1 Sep'23, 09:57 AM
1 Sep'23, 08:48 AM
1 Sep'23, 07:11 AM
31 Aug'23, 02:07 PM
31 Aug'23, 12:10 PM
26 Aug'23, 11:14 AM
21 Aug'23, 07:18 AM
18 Aug'23, 11:51 AM
6 Jul'23, 07:27 AM
21 Jun'23, 12:18 PM
13 Jun'23, 09:54 AM
4 Apr'23, 11:39 AM
27 Mar'23, 07:53 AM
22 Mar'23, 12:57 PM
22 Mar'23, 11:00 AM
1 Sep'23, 12:44 PM
1 Sep'23, 12:27 PM
1 Sep'23, 12:02 PM
1 Sep'23, 11:26 AM
1 Sep'23, 09:57 AM
1 Sep'23, 08:48 AM
1 Sep'23, 07:11 AM
31 Aug'23, 02:07 PM
31 Aug'23, 12:10 PM
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