Using Deep Learning in ML.NET

Explore how ML.NET abstracts deep learning complexities using easy-to-use APIs and integrates with TensorFlow and PyTorch. Learn to build image classification models without designing neural networks, and understand training with smaller datasets. This lesson helps you apply ML.NET for deep learning tasks efficiently and leverage external models.

We'll cover the following...

Deep learning setup example
Deep learning tasks supported by ML.NET

In ML.NET, deep learning functionality is abstracted away by easy-to-use APIs, so developers don’t have to build and configure their own ANNs. We don’t need to decide how many nodes we need to place in the input layer. We don’t have to worry about the depth and width of the hidden layers either.

ML.NET achieves this by utilizing other open-source ML technology. By default, it uses either TensorFlow or PyTorch, depending on the task type. This is how it works under the hood:

The model is built by using TensorFlow or PyTorch.
ML.NET provides a method to access the model for consumption. ...

1.Introduction to ML.NET

2.Machine Learning Fundamentals

3.Selecting a Problem for Machine Learning

4.Built-In Supervised Learning Tasks in ML.NET

5.Built-In Unsupervised Learning Tasks in ML.NET

6.Deep Learning and Neural Networks

7.Automating Machine Learning Tasks with AutoML

8.Saving and Consuming Machine Learning Models

Mini Project

9.Wrapping Up

Project

10.Appendix

Using Deep Learning in ML.NET