If you are interested in Machine Learning, Neural Networks, Deep Learning, Deep Neural Networks (DNN), and Convolution Neural Networks (CNN) with an in-depth and clear understanding, then this course is for you.

Topics are explained in detail. Concepts are developed progressively in a step by step manner. I sometimes spent more than 10 minutes discussing a single slide instead of rushing through it. This should help you to be in sync with the material presented and help you better understand it.

The hands-on examples are selected primarily to make you familiar with some aspects of TensorFlow 2 or other skills that may be very useful if you need to run a large and complex neural network job of your own in the future.

Hand-on examples are available for you to download.

Please watch the first two videos to have a better understanding of the course.

TOPICS COVERED

• What is Machine Learning?

  
  

• Linear Regression

• Steps to Calculate the Parameters

• Linear Regression-Gradient Descent using Mean Squared Error (MSE) Cost Function

  
  

• Logistic Regression: Classification

• Decision Boundary

• Sigmoid Function

• Non-Linear Decision Boundary

• Logistic Regression: Gradient Descent

• Gradient Descent using Mean Squared Error Cost Function

• Problems with MSE Cost Function for Logistic Regression

• In Search for an Alternative Cost-Function

• Entropy and Cross-Entropy

• Cross-Entropy: Cost Function for Logistic Regression

• Gradient Descent with Cross Entropy Cost Function

• Logistic Regression: Multiclass Classification

  
  

• Introduction to Neural Network

• Logical Operators

• Modeling Logical Operators using Perceptron(s)

• Logical Operators using Combination of Perceptron

• Neural Network: More Complex Decision Making

• Biological Neuron

• What is Neuron? Why Is It Called the Neural Network?

• What Is An Image?

• My “Math” CAT. Anatomy of an Image

• Neural Network: Multiclass Classification

• Calculation of Weights of Multilayer Neural Network Using Backpropagation Technique

• How to Update the Weights of Hidden Layers using Cross Entropy Cost Function

  
  

• Hands On

• Google Colab. Setup and Mounting Google Drive (Colab)

• Deep Neural Network (DNN) Based Image Classification Using Google Colab. & TensorFlow (Colab)

  
  

• Introduction to Convolution Neural Networks (CNN)

• CNN Architecture

• Feature Extraction, Filters, Pooling Layer

• Hands On

• CNN Based Image Classification Using Google Colab & TensorFlow (Colab)

  
  

• Methods to Address Overfitting and Underfitting Problems

• Regularization, Data Augmentation, Dropout, Early Stopping

• Hands On

• Diabetes prediction model development (Colab)

• Fixing problems using Regularization, Dropout, and Early Stopping (Colab)

  
  

• Hands On: Various Topics

• Saving Weights and Loading the Saved Weights (Colab)

• How To Split a Long Run Into Multiple Smaller Runs

• Functional API and Transfer Learning (Colab)

• How to Extract the Output From an Intermediate Layer of an Existing Model (Colab), and add additional layers to it to build a new model.