Learn to implement binary and multi-class classification using TensorFlow in this comprehensive 2-hour video tutorial. Master essential concepts including working with tensors, data preprocessing, model building, and evaluation. Explore topics such as downloading and cleaning data, finding ideal learning rates, creating correlation matrices, and performing neural network regressions. Gain hands-on experience in normalizing data, separating features and labels, building and compiling models, and optimizing performance through activation functions, fitting, and epochs. Access accompanying code on GitHub and enhance your skills in deep learning and machine learning with TensorFlow.
Overview
Syllabus
Introduction
What do you need
Review
Importing Data
Cleaning Data
Renaming Data
Changing Data Values
Judging Data
Quality Rating
Normalize
normalize Data
Playground
Activations
Relu
Sigmoid
Build the model
Compile the model
Fit the model
Evaluation
Taught by
Derek Banas
