TensorFlow Developer Certificate - Building and Training Neural Network Models using TensorFlow 2.X
This course will teach you how to build, train and evaluate neural network models for classification and regression tasks using TensorFlow 2.X.
What you'll learn
Classification and regression are the two most useful machine learning tasks with a lot of real world applications. In this course, TensorFlow Developer Certificate - Building and Training Neural Network Models using TensorFlow 2.X, you’ll learn to build neural network models for classification and regression tasks using TensorFlow 2.X.
First, you'll start with the basics of machine learning and neural networks.
After that, you'll discover the different evaluation metrics for classification and regression tasks, as well as the problems of overfitting and underfitting, and how to detect and prevent them.
Then, you'll understand a classification model to classify images of handwritten digits and a regression model to predict house prices and finally. Finally, you'll learn to build a binary classifier to classify images of dogs and cats using the concept of transfer learning.
When you’re finished with this course, you’ll have the skills and knowledge of the practical aspects of implementing the models using TensorFlow. From that perspective, this course will have three demos which will contain full implementations of three models from scratch.
Table of contents
- Overview 1m
- Overview 1m
- Basics of Machine Learning 7m
- Basics of Machine Learning 7m
- Introduction to Neural Networks 6m
- Introduction to Neural Networks 6m
- Training Deep Neural Networks 4m
- Training Deep Neural Networks 4m
- Evaluation of Regression Model 3m
- Evaluation of Regression Model 3m
- Evaluation of Classification Model 5m
- Evaluation of Classification Model 5m
- Overfitting and Underfitting 8m
- Overfitting and Underfitting 8m
- Summary 1m
- Summary 2m