Universal Deep Learning Mastery – 2024 Edition with Updated

Course Curriculum

Chapter 1: Welcome

Lecture 1: Introduction to Deep Learning 101

Lecture 2: Access Source Code

Chapter 2: Getting basics right

Lecture 1: Artificial Neural Networks

Lecture 2: Activation Function

Lecture 3: Bias

Lecture 4: Data

Lecture 5: Applications of Data

Lecture 6: Models

Lecture 7: Loss Functions

Lecture 8: Learning Algorithms & Model Performance

Chapter 3: Python Crash Course on Basics

Lecture 1: Getting System Ready – Jupyter Notebook

Lecture 2: Accessing Google Colab Notebook

Lecture 3: Download Materials

Lecture 4: Python Basics – Data Types

Lecture 5: Python Basics – Containers in Python

Lecture 6: Control Statements Python if..else

Lecture 7: Python Control statments – While and For

Lecture 8: Functions & Classes in Python

Chapter 4: Python for Data Science Crash Course

Lecture 1: Numpy Part 1

Lecture 2: Numpy Part 2

Lecture 3: Numpy Part 3

Lecture 4: Pandas in Python – Pandas Series

Lecture 5: Pandas Data Frame

Lecture 6: Pandas Data frame – cleaning & Examining the data

Lecture 7: Plotting with Matplotlib

Lecture 8: Contour Plots

Chapter 5: MP Neuron Model

Lecture 1: MP Neuron Introduction

Lecture 2: Intuition of data

Lecture 3: Loss & finding parameters

Lecture 4: Mathematical Intuition

Chapter 6: MP Neuron in Python

Lecture 1: Download Materials

Lecture 2: MP Neuron – Data import

Lecture 3: Train Test Split

Lecture 4: Modify Data

Lecture 5: MP Neuron in Python

Lecture 6: MP Neuron Class

Lecture 7: Assignment for MP Neuron in Python

Chapter 7: Summary of MP Neuron

Lecture 1: Summary of MP Neuron

Chapter 8: Perceptron

Lecture 1: Perceptron

Lecture 2: Perceptron Model and its representation

Lecture 3: Loss function & Parameter Update

Lecture 4: Why Update Rule Works

Lecture 5: Update Rule in Programs

Chapter 9: Perceptron in Python

Lecture 1: Download Materials

Lecture 2: Perceptron in Python

Lecture 3: Visualize the Accuracy with epochs

Lecture 4: Perceptron Assignment

Chapter 10: Sigmoid Neuron

Lecture 1: Download Materials

Lecture 2: Percepron Limitations

Lecture 3: Sigmoid Neuron Introduction

Lecture 4: Sigmoid Neuron Data

Lecture 5: Sigmoid Intuition

Lecture 6: Manual fitting of data

Lecture 7: Gradient descent

Lecture 8: Program overview

Lecture 9: Program in Python

Chapter 11: Sigmoid Neuron Implement with Python

Lecture 1: Download Materials

Lecture 2: Download Dataset

Lecture 3: Data Standardization -1

Lecture 4: Data Standardization – 2

Lecture 5: Class Sigmoid

Lecture 6: Sigmoid Assignment

Chapter 12: Basic Probability

Lecture 1: Introduction to Probability and Random Variables

Lecture 2: Why Random Variable is important

Lecture 3: Random Variable – Types

Lecture 4: Probability Distribution Table

Lecture 5: Why do we require Entropy Loss

Chapter 13: Why Deep Neural Networks – Intuition

Lecture 1: Download Materials

Lecture 2: Why Deep Neural Networks

Lecture 3: Linear Separation of Data

Chapter 14: Universal Approximation Theorem – Deep Learning Foundation

Lecture 1: Understanding Universal Approximation Theorem

Lecture 2: Confirming Universal Approximation Theorem Works

Lecture 3: Going deep into Neural Networks

Lecture 4: Challenges in Creating Deep Neural Networks from Scratch