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

## How does LSTM work? 

LSTM (Long Short Term Memory) cells are the most commonly used RNN cell nowadays. Don’t be scared of the math! We will slowly clarify every term by inspecting every equation separately.

LSTM is the most popular RNN because it has a special memory component denoted by $c$ in the equations. It represents long-term memory.

Here is a sketch overview:

# How does LSTM work? 

LSTM (Long Short Term Memory) cells are the most commonly used RNN cell nowadays. Don’t be scared of the math! We will slowly clarify every term by inspecting every equation separately.

LSTM is the most popular RNN because it has a special memory component denoted by $c$ in the equations. It represents long-term memory.

Here is a sketch overview:

Dive into the mathematics and intuition of  Long Short Term Memory Cells (LSTM).

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

LSTM: Long Short Term Memory Cells

How does LSTM work?