1.
Nuestras
locaciones

2.
Nuestros
Panelistas
Marvin Abisrror
Data Scientist, CoE en Belatrix
Jans Álvarez
Marketing Analyst

3.
Agenda
• Context of ML and DL in Artificial Intelligence.
• Machine Learning.
• Deep Learning.
• Artificial Neural Network.
• Convolutional Neural Networks.
• Applications in ML and DL.
¿QUESTIONS?
#MachineLearningBSF

4.
Context of ML and DL

5.
¿QUESTIONS?
#MachineLearningBSF
Artificial Intelligence

6.
¿QUESTIONS?
#MachineLearningBSF
Artificial Intelligence
Machine Learning

7.
¿QUESTIONS?
#MachineLearningBSF
Artificial Intelligence
Machine Learning
Big Data

8.
¿QUESTIONS?
#MachineLearningBSF
Artificial Intelligence
Machine Learning
Big Data
Deep Learning

9.
Approaches of Machine Learning ¿QUESTIONS?
#MachineLearningBSF
MACHINE LEARNING
SUPERVISED REINFORCEMENTUNSUPERVISED

10.
Supervised Learning - Linear Regression ¿QUESTIONS?
#MachineLearningBSF

11.
Linear Regression ¿QUESTIONS?
#MachineLearningBSF

12.
Linear Regression ¿QUESTIONS?
#MachineLearningBSF

13.
Linear Regression ¿QUESTIONS?
#MachineLearningBSF

14.
Gradient Descent ¿QUESTIONS?
#MachineLearningBSF

15.
Gradient Descent ¿QUESTIONS?
#MachineLearningBSF

16.
Gradient Descent ¿QUESTIONS?
#MachineLearningBSF

17.
Supervised Learning - Classification ¿QUESTIONS?
#MachineLearningBSF

18.
Supervised Learning - Classification ¿QUESTIONS?
#MachineLearningBSF

19.
Unsupervised Learning: Clustering ¿QUESTIONS?
#MachineLearningBSF

20.
Unsupervised Learning - Recommended
Systems
¿QUESTIONS?
#MachineLearningBSF

21.
Unsupervised Learning - Recommended
Systems (Matrix Factorization)
¿QUESTIONS?
#MachineLearningBSF

22.
Reinforcement Learning ¿QUESTIONS?
#MachineLearningBSF
Agent
Environment
ActionState Reward

23.
DeepMind - AlphaGO ¿QUESTIONS?
#MachineLearningBSF

24.
Deep Learning

25.
Fully Connected Neural Network
¿QUESTIONS?
#MachineLearningBSF

26.
Perceptron - Single Layer
Feedforward Neural Network
¿QUESTIONS?
#MachineLearningBSF
X1
X2
1
∑
W1
b
h f(h) yW2

27.
Mathematically ¿QUESTIONS?
#MachineLearningBSF

28.
Neural Networks ¿QUESTIONS?
#MachineLearningBSF

29.
Convolutional Neural Network
(CNN)
¿QUESTIONS?
#MachineLearningBSF

30.
Convolutional Neural Network
(CNN)
¿QUESTIONS?
#MachineLearningBSF

31.
Convolutional Neural Network
(CNN)
¿QUESTIONS?
#MachineLearningBSF

32.
Convolutional Neural Network (CNN) ¿QUESTIONS?
#MachineLearningBSF

33.
Would you cross this Bridge? ¿QUESTIONS?
#MachineLearningBSF

34.
DEMO

35.
Examples of ML and DL
https://github.com/Marvinsky/machine_learning_vs_deep_learning

36.
Bibliography
• Dmytro Mishkin and Nikolay Sergievskiy and Jiri Matas. Systematic evaluation of {CNN} advances on the ImageNet. (2016).
https://arxiv.org/pdf/1606.02228.pdf
• Andrej Karpathy and Justin Johnson and Fei{-}Fei Li. Visualizing and Understanding Recurrent Networks. (2015).
https://arxiv.org/pdf/1506.02078.pdf
• Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, Alexei A. Efros. Generative Visual Manipulation on the Natural Image Manifold.
(2016). https://arxiv.org/pdf/1609.03552.pdf
• Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros. Unpaired Image-to-Image Translation using Cycle-Consistent
Adversarial Networks. (2017). https://arxiv.org/pdf/1703.10593.pdf
• Xudong Mao, Qing Li, Haoran Xie, Raymond Y.K. Lau, Zhen Wang, and Stephen Paul Smolley. Least Squares Generative Adversarial
Networks. (2017). https://arxiv.org/pdf/1611.04076.pdf
• Tianqi Chen, Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. (2016). https://arxiv.org/pdf/1603.02754.pdf
• Hado van Hasselt, Arthur Guez, David Silver. Deep Reinforcement Learning with Double Q-learning.
(2015).https://arxiv.org/pdf/1509.06461.pdf
¿QUESTIONS?
#MachineLearningBSF

37.
Bibliography
• Yoshua Bengio. Practical recommendations for gradient-based training of deep architectures. (2012).
https://arxiv.org/pdf/1206.5533.pdf
• Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov. Dropout: a simple way to prevent neural
networks from overfitting. (2014). https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf
• Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio.
Generative Adversarial Networks. (2014). https://arxiv.org/pdf/1406.2661.pdf
• Xiaohan Jin, Ye Qi, Shangxuan Wu. CycleGAN Face-off. (2017). https://arxiv.org/pdf/1712.03451.pdf
• Sergey Ioffe and Christian Szegedy. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate
Shift. (2015). https://arxiv.org/pdf/1502.03167.pdf
• Alec Radford, Luke Metz, Soumith Chintala. Unsupervised Representation Learning with Deep Convolutional Generative
Adversarial Networks. (2015). https://arxiv.org/pdf/1511.06434.pdf
¿QUESTIONS?
#MachineLearningBSF

38.
Preguntas

39.
¡Muchas Gracias!
www.belatrixsf.com