docker.tar.gz

Pytorch

GANS

This course is an accumulation of well-grounded knowledge and experience in deep learning. It provides you with the basic concepts you need in order to start working with and training various machine learning models. 

You will cover both basic and intermediate concepts including but not limited to: convolutional neural networks, recurrent neural networks, generative adversarial networks as well as transformers.

After completing this course, you will have a comprehensive understanding of the fundamental architectural components of deep learning. Whether you’re a data and computer scientist, computer and big data engineer, solution architect, or software engineer, you will benefit from this course.

Introduction to Deep Learning & Neural Networks

## What is a linear classifier?

Suppose we want to build a machine learning model to classify the following points into two categories based on their color. It is very easy to see that we can find a single point that separates them perfectly. The goal of our model is to find this point.

# What is a linear classifier?

Suppose we want to build a machine learning model to classify the following points into two categories based on their color. It is very easy to see that we can find a single point that separates them perfectly. The goal of our model is to find this point.

Explore linear classifiers, their principles, and their training process.

Learn Deep Learning

Neural Networks

Training Neural Networks

Convolutional Neural Networks

Recurrent Neural Networks

Autoencoders

Generative Adversarial Networks

Attention and Transformers

Graph Neural Networks

Conclusion

Final Quiz

Linear Classifiers

What is a linear classifier?