Course curriculum

  • 1

    Introduction

    • Intro to Instructor.mp4
    • Introduction to Course
  • 2

    Basics of Deep Learning

    • Problem to Solve Part 1
    • Problem to Solve Part 2
    • Problem to Solve Part 3
    • Linear Equation
    • Linear Equation Vectorized
    • 3D Feature Space
    • N Dimensional Space
    • Theory of Perceptron
    • Implementing Basic Perceptron
    • Logical Gates for Perceptrons
    • Perceptron Training Part 1
    • Perceptron Training Part 2
    • Learning Rate
    • Perceptron Training Part 3
    • Perceptron Algorithm
    • Coading Perceptron Algo (Data Reading & Visualization)
    • Coading Perceptron Algo (Perceptron Step)
    • Coading Perceptron Algo (Training Perceptron)
    • Coading Perceptron Algo (Visualizing the Results)
    • Problem with Linear Solutions
    • Solution to Problem
    • Error Functions
    • Discrete vs Continuous Error Function
    • Sigmoid Function
    • Multi-Class Problem
    • Problem of Negative Scores
    • Need of Softmax
    • Coding Softmax
    • One Hot Encoding
    • Maximum Likelihood Part 1
    • Maximum Likelihood Part 2
    • Cross Entropy
    • Cross Entropy Formulation
    • Multi Class Cross Entropy
    • Cross Entropy Implementation
    • Sigmoid Function Implementation
    • Output Function Implementation
  • 3

    Deep Learning

    • Introduction to Gradient Decent
    • Convex Functions
    • Use of Derivatives
    • How Gradient Decent Works
    • Gradient Step
    • Logistic Regression Algorithm
    • Data Visualization and Reading
    • Updating Weights in Python
    • Implementing Logistic Regression
    • Visualization and Results
    • Gradient Decent vs Perceptron
    • Linear to Non Linear Boundaries
    • Combining Probabilities
    • Weighted Sums
    • Neural Network Architecture
    • Layers and DEEP Networks
    • Multi Class Classification
    • Basics of Feed Forward
    • Feed Forward for DEEP Net
    • Deep Learning Algo Overview
    • Basics of Back Propagation
    • Updating Weights
    • Chain Rule for BackPropagation
    • Sigma Prime
    • Data Analysis NN Implementation
    • One Hot Encoding (NN Implementation)
    • Scaling the Data (NN Implementation)
    • Splitting the Data (NN Implementation)
    • Helper Functions (NN Implementation)
    • Training (NN Implementation)
    • Testing (NN Implementation)
  • 4

    Optimizations

    • Underfitting vs Overfitting
    • Early Stopping
    • Quiz
    • Solution & Regularization
    • L1 & L2 Regularization
    • Dropout
    • Local Minima Problem
    • Random Restart Solution
    • Vanishing Gradient Problem
    • Other Activation Functions
  • 5

    Final Project

    • Final Project Part 1
    • Final Project Part 2
    • Final Project Part 3
    • Final Project Part 4
    • Final Project Part 5