Machine Learing (ML)

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

For beginners Machine Learning by Google and Machine Learning by scikit-learn will be a good take-off.
To start understanding most of the Machine Learning algorithms, you must get the basic understanding of Calculus and Linear Algebra:

When there is a clear understanding how Deep Learning is working nor used on data.
Supervised Learning, Unsupervised Learning and Reinforcement Learning are understandable as well.

Requirements

  # (required to have programming knowledge)
  # 1 - open a command prompt, in this folder.
  # 2 - paste line below & press enter.
  pip3 install -r "./requirements.txt"

`Deep Learning`

Usage Type	Model Type	tensorflow	pytorch	numpy
`Artificial Neural Networks`
	`Perceptron`	⬜️	⬜️	✅
	`Feed Forward`	✅	⬜️	✅
	`Deep Feed Forward`	✅	✅	✅
	`Radial Basis Network`	⬜️	✅	✅
`Recurrent Neural Networks`
	`Recurrent Neural Network`	✅	✅	✅
	`Long Short Term Memory`	✅	✅	⬜️
	`Gated Recurrent Unit`	✅	✅	⬜️
`Auto Encoders`
	`Auto Encoder`	✅	✅	✅
	`Denoising Autoencoder`	✅	✅	✅
	`Generative Adversarial Network`	✅	✅	✅
	`Sparse Autoencoder`	✅	✅	⬜️
	`Variational Autoencoder`	✅	✅	⬜️
`Convolution Neural Networks`
	`Deep Convolutional Network`	✅	✅	✅
	`Deconvolutional Network`	✅	✅	⬜️
	`Deep Convolutional Inverse Graphics 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`	⬜️	✅	⬜️
	`Neural Tuning Machine`	⬜️	✅	⬜️
	`Support Vector Machine`	⬜️	⬜️	✅

`Supervised Learning`

Usage Type	Model Type	numpy
`Classification`
	`Binary Classification`	✅
	`Imbalanced Classification`	✅
	`Multi Class Classification`	✅
	`Multi Label Classification`	✅
`Regression`
	`Cox Regression`	✅
	`Elastic Net Regression`	✅
	`Lasso Regression`	✅
	`Linear Regression`	✅
	`Logistic Regression`	✅
	`Negative Binomial Regression`	✅
	`Ordinal Regression`	✅
	`Partial Least Squares Regression`	✅
	`Poisson Regression`	✅
	`Polynomial Regression`	✅
	`Principal Components Regression`	✅
	`Quantile Regression`	✅
	`Ridge Regression`	✅
	`Support Vector Regression`	✅

`Unsupervised Learning`

Usage Type	Model Type	sample	numpy
`Clustering`
	`Affinity Propagation`	✅	✅
	`Agglomerative Clustering`	✅	✅
	`BIRCH`	✅	⬜️
	`DBSCAN`	✅	✅
	`Gaussian Mixture`	✅	✅
	`K-Means`	✅	✅
	`Mean Shift`	✅	✅
	`OPTICS`	✅	✅
	`Spectral Clustering`	✅	✅
`Dimensionality Reduction`
	`Latent Semantic Analysis`	✅	⬜️
	`Non Negative Matrix Factorization`	✅	⬜️
	`Principal Component Analysis`	✅	✅
	`T-Distributed Stochastic Neighbor Embedding`	✅	⬜️
	`Uniform Manifold Approximation And Projection`	✅	⬜️

`Reinforcement Learning`

Model Type	sample
`Q-Learning`	✅
`Deep Q-Learning`	✅
`Double Deep Q-Learning`	✅
`Actor Critic Method`	✅
`Deep Deterministic Policy Gradient`	✅
`Proximal Policy Optimization`	✅

Usefull Resources:

Kind Regards,
Niek Tuytel :)

niektuytel / machine_learning Goto Github PK

machine_learning's Introduction

Machine Learing (ML)

Requirements

`Deep Learning`

`Supervised Learning`

`Unsupervised Learning`

`Reinforcement Learning`

Usefull Resources:

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