Machine Learning and Deep Learning Using TensorFlow

About Machine Learning and Deep Learning Using TensorFlow

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.