This document provides an overview of machine learning and deep learning techniques. It defines machine learning as a field that gives computers the ability to learn without being explicitly programmed. It also describes different machine learning algorithms including supervised learning techniques like classification and regression, and unsupervised learning techniques like clustering. Deep learning techniques using neural networks for applications such as computer vision and natural language processing are also overviewed. Resources for implementing machine learning and deep learning in Python are provided.

1. Knowledge Sharing
Machine Learning
for Dummies
Andrews Sobral, Ph.D.
Machine Learning Engineer
ActiveEon
http://andrewssobral.wixsite.com/homewithout mathematics

3. What is Machine Learning (ML)?
❖ “Machine learning is a field of computer science that
gives computers the ability to learn without being
explicitly programmed.” (Wikipedia)

4. Machine Learning vs Traditional Programming

5. AI vs ML vs RL vs DL

6. How a Machine Learning algorithm learns?

7. How a Machine Learning algorithm learns?

8. Types of Machine Learning techniques

9. Types of Machine Learning techniques

10. How Reinforcement Learning works?

11. Reinforcement Learning algorithms
❖ State-of-the-art models:
❖ A2C (ICML’2016)
❖ ACKTR (ArXiv 2017)
❖ DDPG (ArXiv 2015)
❖ DQN (Nature 2015)
❖ PPO (ArXiv 2017)
❖ TRPO (ICML 2015)
❖ OpenAI Baselines:
❖ high-quality implementations of reinforcement learning algorithms
❖ https://github.com/openai/baselines

12. Demo: Using PPO to train AI policies
An agent tries to reach a target (the pink sphere), learning to walk, run, turn, use its momentum
to recover from minor hits, and how to stand up from the ground when it is knocked over.
https://blog.openai.com/openai-baselines-ppo/

13. and more
OpenAI + Dota 2 DeepMind + Starcraft 2
For more info, please see:
https://blog.openai.com/dota-2/
https://deepmind.com/blog/deepmind-and-blizzard-open-starcraft-ii-ai-research-environment/

14. Types of Machine Learning techniques

15. Unsupervised Learning (UL) vs Supervised Learning (SL)

16. Unsupervised Learning (UL) vs Supervised Learning (SL)

17. How Unsupervised Learning works?
Algorithm

18. Unsupervised Learning algorithms
❖ K-means clustering
❖ Partitions data into k distinct clusters based on distance to the centroid of a cluster.
❖ Principal Component Analysis (PCA) [also part of Dimensionality Reduction methods]
❖ Convert a set of observations of possibly correlated variables into a set of values of
linearly uncorrelated variables called principal components.
❖ Gaussian Mixture Models (GMM)
❖ Models clusters as a mixture of multivariate normal density components.
❖ Self-organizing Maps (SOM)
❖ Uses neural networks that learn the topology and distribution of the data.
❖ Hidden Markov Models (HMM)
❖ Uses observed data to recover the sequence of states.
http://scikit-learn.org/stable/modules/clustering.html#clustering
https://fr.mathworks.com/discovery/unsupervised-learning.html
Implementations:

19. Comparison of clustering algorithms
http://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html

20. What is Dimensionality Reduction?
Example of how PCA works
https://lvdmaaten.github.io/drtoolbox/ (33 MATLAB implementations)
http://scikit-learn.org/stable/modules/decomposition.html#decompositions

21. Robust PCA
https://github.com/andrewssobral/lrslibrary
https://www.slideshare.net/andrewssobral/thesis-presentation-robust-lowrank-and-sparse-decomposition-for-moving-object-detection-from-matrices-to-tensors

22. Unsupervised Learning (UL) vs Supervised Learning (SL)

23. How Supervised Learning works?

24. Classification vs Regression
Classification: for categorical response values, where the data can be separated into specific “classes”
Regression: for continuous-response values

25. Supervised Learning algorithms
❖ Common classification algorithms include:
❖ Support vector machines (SVM)
❖ Neural networks
❖ Naïve Bayes classifier
❖ Decision trees
❖ Discriminant analysis
❖ Nearest neighbors (kNN)
❖ Common regression algorithms include:
❖ Linear regression
❖ Nonlinear regression
❖ Generalized linear models
❖ Decision trees
❖ Neural networks
http://scikit-learn.org/stable/supervised_learning.html#supervised-learning
https://fr.mathworks.com/discovery/supervised-learning.html

26. Linear vs Nonlinear problems

27. Ensemble methods
❖ The goal of ensemble methods is to combine the predictions of several base estimators built
with a given learning algorithm in order to improve generalizability / robustness over a
single estimator.
Two families of ensemble methods are usually
distinguished:
In averaging methods, the driving principle is
to build several estimators independently and
then to average their predictions.
Examples: Bagging methods, Forests of
randomized trees, …
By contrast, in boosting methods, base
estimators are built sequentially and one tries
to reduce the bias of the combined estimator.
Examples: AdaBoost, Gradient Tree Boosting,
…

28. Cheat Sheet of ML algorithms

29. What about Deep Learning?

30. Machine Learning vs Deep Learning

31. Deep Neural Networks

32. Neural
Networks
Architecture
https://medium.com/towards-data-
science/neural-network-
architectures-156e5bad51ba

33. Semantic segmentation
Object classification
Object detection
Common architectures for image analysis

34. Performance of DNNs

35. Some applications of DNNs
+ Natural Language Processing
+ Speech Recognition
+ Recommendation Systems
+ and more…

37. 24 Neural Network Adjustements

38. How do I know what architecture to use?
❖ Don’t be a hero.
❖ Take whatever works best.
❖ Download a pretrained model.
❖ Add/delete some parts of it.
❖ Finetune it on your application.

39. Generative Adversarial Networks
https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f

40. Example of GANs output
http://www.shakirm.com/slides/DeepGenModelsTutorial.pdf

41. Example of GANs output

42. GAN variants
https://github.com/znxlwm/pytorch-generative-model-collections

43. Resources for ML
❖ Python Machine Learning by Sebastian Raschka
❖ https://github.com/rasbt/python-machine-learning-book

44. Resources for DL
❖ Keras + Tensorflow (Google)
❖ CNTK (Microsoft)
❖ PyTorch (Facebook)
❖ MXNET (Amazon)
❖ Caffe (Berkley) , Caffe2 (Facebook)
❖ http://deeplearning.net/software_links/
❖ https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software

45. Thank you!
Contact us:
contact@activeeon.com