Gain insights into basic and intermediate deep learning concepts, including CNNs, RNNs, GANs, and transformers. Delve into fundamental architectures to enhance your machine learning model training skills.

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

Activation functions are non-linear functions that determine the outputs of neurons. As we already discussed, each neuron accepts a set of inputs, multiplies them by the weights, sums them up, and adds a bias. 

$$ z= w_1*x_1 +w_2*x_2 + w_3*x+ bo $$

Because that will result in a linear transformation, we then pass neurons through a non-linear function $f$ so we can capture non-linear patterns between our data.

$$ a= f( w_1*x_1 +w_2*x_2 + w_3*x+3 + bo) $$

Over the years, many functions have been proposed, each one with its strengths and weaknesses. In this lesson, we will discuss the most common ones.


## Sigmoid

$$ f(x) = \frac{1}{1+e^{-x}} $$

Activation functions are non-linear functions that determine the outputs of neurons. As we already discussed, each neuron accepts a set of inputs, multiplies them by the weights, sums them up, and adds a bias. 

$$ z= w_1*x_1 +w_2*x_2 + w_3*x+ bo $$

Because that will result in a linear transformation, we then pass neurons through a non-linear function $f$ so we can capture non-linear patterns between our data.

$$ a= f( w_1*x_1 +w_2*x_2 + w_3*x+3 + bo) $$

Over the years, many functions have been proposed, each one with its strengths and weaknesses. In this lesson, we will discuss the most common ones.


# Sigmoid

$$ f(x) = \frac{1}{1+e^{-x}} $$

Learn about the most popular activation functions for deep learning.

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

Activation Functions