README.md

Deep Learning (DL)

Deep learning is a subset of machine learning and can been used for unsupervised nor supervised problems. in Artificial Intelligence that has networks capable of learning unsupervised nor supervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network. Deep Learning has evolved hand-in-hand with the digital era, which has brought about an explosion of data in all forms and from every region of the world. This data, known simply as big data, is drawn from sources like social media, internet search engines, e-commerce platforms, and online cinemas, among others. This enormous amount of data is readily accessible and can be shared through fintech applications like cloud computing. However, the data, which normally is unstructured, is so vast that it could take decades for humans to comprehend it and extract relevant information. Companies realize the incredible potential that can result from unraveling this wealth of information and are increasingly adapting to AI systems for automated support.

Subsets of Deep Learning:

best way is to learn is from TOP to BOTTOM.

• Artificial Neural Networks
  • Perceptron
  • Feed Forward
  • Radial Basis Network
  • Deep Feed Forward

• Recurrent Neural Networks
  • Recurrent Neural Network
  • Long Short Term Memory
  • Gated Recurrent Unit

• Auto Encoders
  • Auto Encoder
  • Denoising Auto Encoder
  • Sparse Auto Encoder
  • Variational Auto Encoder
  • Generative Adversarial Network

• Convolution Neural Networks
  • Deep Convolutional Network
  • Deep Convolutional Inverse Graphics Network
  • Deconvolutional Network

• Stochastic Neural Networks
  • Deep Belief Network
  • Restricted Boltzmann Machine

• Reservoir Computing
  • Liquid State Machine
  • Extreme Learning Machine
  • Echo State Network

• Ungrouped Networks
  • Deep Residual Network
  • Kohonen Network
  • Support Vector Machine
  • Neural Tuning Machine

Informative Resources:

• https://www.deeplearningwizard.com/
• https://pub.towardsai.net/whirlwind-tour-of-rnns-a11effb7808f
• https://machinelearningmastery.com/keras-functional-api-deep-learning/
• http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
• https://jramapuram.github.io/ramblings/rnn-backrpop/
• why deep learning exists nor been used?
• https://www.youtube.com/watch?v=IN2XmBhILt4&list=RDCMUCtYLUTtgS3k1Fg4y5tAhLbw&index=1
• https://pytorch.org/tutorials/beginner/pytorch_with_examples.html
• https://paperswithcode.com/method/denoising-autoencoder
• https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-algorithm
• https://bhashkarkunal.medium.com/pytorch-tutorial-from-basic-to-advance-level-a-numpy-replacement-and-deep-learning-framework-that-a3c8dcf9a9d4
• https://uvadlc-notebooks.readthedocs.io/
• https://junyanz.github.io/CycleGAN/?source=techstories.org