Machine Learning and Deep Learning Using TensorFlow

About this course

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 FunctionNon-Linear Decision Boundary

Logistic Regression: Gradient DescentGradient 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-EntropyCross-Entropy: Cost Function for Logistic Regression

Gradient Descent with Cross Entropy Cost Function

Logistic Regression: Multiclass Classification

Introduction to Neural NetworkLogical Operators

Modeling Logical Operators using Perceptron(s)Logical Operators using Combination of Perceptron

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