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 COVEREDWhat is Machine Learning.

Linear RegressionSteps to Calculate the ParametersLinear Regression-Gradient Descent using Mean Squared Error (MSE) Cost FunctionLogistic Regression: ClassificationDecision BoundarySigmoid FunctionNon-Linear Decision BoundaryLogistic Regression: Gradient DescentGradient Descent using Mean Squared Error Cost FunctionProblems with MSE Cost Function for Logistic RegressionIn Search for an Alternative Cost-FunctionEntropy and Cross-EntropyCross-Entropy: Cost Function for Logistic RegressionGradient Descent with Cross Entropy Cost FunctionLogistic Regression: Multiclass ClassificationIntroduction to Neural NetworkLogical OperatorsModeling Logical Operators using Perceptron(s)Logical Operators using Combination of PerceptronNeural Network: More Complex Decision MakingBiological NeuronWhat is Neuron. Why Is It Called the Neural Network. What Is An Image.