Downloaded 185 times
Uploaded byBelatrix Software
Machine Learning vs. Deep Learning
The document outlines a presentation agenda led by data scientist Marvin Abisrror and marketing analyst Jans Álvarez, focusing on machine learning (ML) and deep learning (DL) within artificial intelligence (AI). Key topics include various ML approaches such as supervised and unsupervised learning, reinforcement learning, and the applications and techniques of deep learning like convolutional neural networks. A bibliography is provided with references to academic papers related to the discussed concepts.
More Related Content
Similar to Machine Learning vs. Deep Learning
More from Belatrix Software
Machine Learning vs. Deep Learning
- 2.
- 3. Nuestros Panelistas Marvin Abisrror Data Scientist,CoE en Belatrix Jans Álvarez Marketing Analyst
- 4. Agenda • Context ofML and DL in Artificial Intelligence. • Machine Learning. • Deep Learning. • Artificial Neural Network. • Convolutional Neural Networks. • Applications in ML and DL. ¿QUESTIONS? #MachineLearningBSF
- 5. Context of MLand DL
- 6.
- 7.
- 8.
- 9.
- 10. Approaches of MachineLearning ¿QUESTIONS? #MachineLearningBSF MACHINE LEARNING SUPERVISED REINFORCEMENTUNSUPERVISED
- 11. Supervised Learning -Linear Regression ¿QUESTIONS? #MachineLearningBSF
- 12.
- 13.
- 14.
- 15.
- 16.
- 17.
- 18. Supervised Learning -Classification ¿QUESTIONS? #MachineLearningBSF
- 19. Supervised Learning -Classification ¿QUESTIONS? #MachineLearningBSF
- 20. Unsupervised Learning: Clustering¿QUESTIONS? #MachineLearningBSF
- 21. Unsupervised Learning -Recommended Systems ¿QUESTIONS? #MachineLearningBSF
- 22. Unsupervised Learning -Recommended Systems (Matrix Factorization) ¿QUESTIONS? #MachineLearningBSF
- 23.
- 24. DeepMind - AlphaGO¿QUESTIONS? #MachineLearningBSF
- 25.
- 26. Fully Connected NeuralNetwork ¿QUESTIONS? #MachineLearningBSF
- 27. Perceptron - SingleLayer Feedforward Neural Network ¿QUESTIONS? #MachineLearningBSF X1 X2 1 ∑ W1 b h f(h) yW2
- 28.
- 29.
- 30.
- 31.
- 32.
- 33. Convolutional Neural Network(CNN) ¿QUESTIONS? #MachineLearningBSF
- 34. Would you crossthis Bridge? ¿QUESTIONS? #MachineLearningBSF
- 35.
- 36. Examples of MLand DL https://github.com/Marvinsky/machine_learning_vs_deep_learning
- 37. Bibliography • Dmytro Mishkinand 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
- 38. 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
- 39.
- 40.