Machine Learning and Deep Learning Using TensorFlow

If you are interested in Machine Learning, Neural Networks, Deep Learning, Deep Neural Networks (DNNs), and Convolution Neural Networks (CNNs) 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