Description
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?My "Math" CAT. Anatomy of an ImageNeural Network: Multiclass ClassificationCalculation of Weights of Multilayer Neural Network Using Backpropagation TechniqueHow to Update the Weights of Hidden Layers using Cross Entropy Cost FunctionHands OnGoogle 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 ArchitectureFeature Extraction, Filters, Pooling LayerHands OnCNN Based Image Classification Using Google Colab & TensorFlow (Colab)Methods to Address Overfitting and Underfitting ProblemsRegularization, Data Augmentation, Dropout, Early StoppingHands OnDiabetes prediction model development (Colab)Fixing problems using Regularization, Dropout, and Early Stopping (Colab)Hands On: Various TopicsSaving Weights and Loading the Saved Weights (Colab)How To Split a Long Run Into Multiple Smaller RunsFunctional 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.
