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.
•What is Machine Learning?
•Steps to Calculate the Parameters
•Linear Regression-Gradient Descent using Mean Squared Error (MSE) Cost Function
•Logistic Regression: Classification
•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
•Modeling Logical Operators using Perceptron(s)
•Logical Operators using Combination of Perceptron
•Neural Network: More Complex Decision Making
•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
•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)
•Feature Extraction, Filters, Pooling Layer
•CNN Based Image Classification Using Google Colab & TensorFlow (Colab)
•Methods to Address Overfitting and Underfitting Problems
•Regularization, Data Augmentation, Dropout, Early Stopping
•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.