Deep learning is a branch of machine learning that uses artificial neural networks to perform complex calculations on large datasets. It mimics the structure and function of the human brain and trains machines by learning from examples. Deep learning is widely used by industries that deal with complex problems, such as health care, eCommerce, entertainment, and advertising.
This post explores the basic types of artificial neural networks and how they work to enable deep learning algorithms.
https://www.ai-contentlab.com/2023/11/learning-is-branch-of-machine-learning.html
Dear Abdulkader Helwan ,
Convolutional Neural Networks (CNNs)
Long Short Term Memory Networks (LSTMs)
Recurrent Neural Networks (RNNs)
Generative Adversarial Networks (GANs)
Radial Basis Function Networks (RBFNs)
Multilayer Perceptrons (MLPs)
Self Organizing Maps (SOMs)
https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-algorithm
Regards,
Shafagat
Deep Neural Networks (DNN) as basic building block in all
Next as others already replied, I think these are a MUST know (because of wide area of applications):
Convolutional Neural Networks (CNNs)
Long Short Term Memory Networks (LSTMs)
Recurrent Neural Networks (RNNs)
Generative Adversarial Networks (GANs)
Autoencoders
And others are optional but good to know as you can make your life easier. Although at work you might be able to perform all tasks by knowing 2 or 4 of them. It is just that in some cases one method is better than another so knowing them all can improve quality of the model. And in advanced cases you might want to combine multiple methods into one.
Abdulkader Helwan, thank you for this post, it is very interesting, and the collection of the algorithm is very useful.
Shafagat Mahmudova the tutorial is fascinating, thanks a lot.
- Badges
- Science topic