Who is this course for?

Course Syllabus

    1. Course Objectives

    2. Resources for the Course

    1. 1 Intro to DL

    2. 2 Understanding Deep Learning

    3. 3 What is a Neuron

    1. 4 Activation Functions

    2. 5 Step Function

    3. 6 Linear Function

    4. 7 Sigmoid Function

    5. 8 TanH Function

    6. 9 ReLU

    1. 10 BP + FP

    2. 11 Gradient Descent

    1. 12.1 ANN Intuition

    2. 12.2 ANN Code

    3. 12.3 ANN HPO

    4. Exercise: ANN

    1. 13 CNN - What is CNN

    2. 13 CNN - Steps in CNN

    3. 14 CNN - CNN Architecture Explained

    4. 15 CNN - Image Augmentation

    5. 16 CNN - Batch size vs iterations vs epochs

    6. 17 CNN - Code Implementation of CNN

    7. 18 CNN - Model Summary _ Parameters

    8. 19 CNN - Hands on XRAY

    9. Exercise: CNN

    1. 20.1 RNN Basics

    2. 20.2 Types of RNN

    3. 20.3 RNN VG+EG

    4. Exercise: Neural Networks

    1. 21.1 LSTM

    2. 21.2 LSTM code

    1. NLP1

    2. NLP2-1

    3. NLP2-2

    4. NLP2-3

    5. NLP2-4

    6. NLP2-5

    7. NLP2-6

    8. NLP2-7

    9. Exercise: NLP

    10. Exercise: Sentiment Analysis

    1. DL Quiz

    2. NLP Quiz

    3. Deep Learning Project

    4. Computer Vision Project

    5. Natural Language Processing

    6. Course Outcomes

    7. Here's what next!
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