1. Welcome on Board FREE CHAPTER

• Welcome
• Course Introduction

2. Getting the Basics Right

• Artificial Neural Networks
• Activation Function
• Bias
• Data
• Applications of Data
• Models
• Loss Functions
• Learning Algorithms and Model Performance

3. Python Crash Course on Basics

• Installing Jupyter Notebook
• Accessing Google Colab
• Python Basics - Data Types
• Python Basics - Containers in Python
• Control Statements Python if…else
• Python Control statements - While and For
• Functions and Classes in Python

4. Python for Data Science - Crash Course

• NumPy Part 1
• NumPy Part 2
• NumPy Part 3
• Pandas in Python - Pandas Series
• Pandas Data Frame
• Cleaning and Examining the data
• Plotting with Matplotlib
• Contour Plots

5. MP Neuron Model

• MP Neuron Introduction
• Intuition of Data
• Loss and Finding Parameters
• Mathematical Intuition

6. MP Neuron in Python

• MP Neuron - Data Import
• Train Test Split
• Modify Data
• MP Neuron in Python
• MP Neuron Class

7. Summary of MP Neuron

• Summary

8. Perceptron

• Perceptron
• Perceptron Model and Its Representation
• Loss Function and Parameter Update
• Why Update Rule Works
• Update Rule in Programs

9. Perceptron in Python

• Perceptron in Python
• Visualize the Accuracy with Epochs

10. Sigmoid Neuron

• Perceptron Limitations
• Sigmoid Neuron Introduction
• Sigmoid Neuron Data
• Sigmoid Intuition
• Manual Fitting of Data
• Gradient Descent
• Program Overview
• Program in Python

11. Sigmoid Neuron Implement with Python

• Download Dataset
• Data Standardization - 1
• Data Standardization - 2
• Class Sigmoid

12. Basic Probability

• Introduction to Probability and Random Variables
• Why Random Variable Is Important
• Random Variable - Types
• Probability Distribution Table
• Why Do We Require Entropy Loss

13. Deep Neural Networks

• Why Deep Neural Networks
• Linear Separation of Data

14. Universal Approximation Theorem

• Understanding Universal Approximation Theorem
• Confirming Universal Approximation Theorem Works
• Going Deep into Neural Networks
• Challenges in Creating Deep Neural Networks from Scratch

15. Deep Learning with TensorFlow 2.x

• Quick Recap on Deep Learning
• Introducing TensorFlow
• Building a Neural Network with TensorFlow
• Create First Neural Network with TensorFlow
• Training the Neural Network
• Training Evaluation
• Summary

16. Activation Functions in Deep Learning Neural Networks

• Activation Functions in Deep Learning Neural Networks - Introduction
• Various Activation Functions
• Summary on Activation Functions
• Common Network Configuration

17. Applying Deep Learning

• Moving from Shallow Learning to Deep Learning
• Keras Basics
• Types of Problems
• ReLU, SoftMax, and Cross Entropy
• Implementing Multi-Class Classification Using Keras
• Regression Problem
• TensorFlow Advanced Tricks - Ways to Create Neural Networks
• TensorFlow - Subclassing Methods