Ai - A Practical Approach
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Exploring Artificial Intelligence and in practice, with several network architectures implemented on the fly in a live coding session. In this talk we went through Shallow / Intermediate / Deep / Convolutional and Residual Networks. Showing the differences in hyperparameters tuning, which to use and when. In addition, also how they came to life though the mind of computer scientists like Geoffrey Hinton, Yoshua Bengio, Xavier Glorot and Adam Coates. The code presented in this talk can be found here: https://github.com/ekholabs/DLinK
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It covers the following topics: Architecture; Accessibility; Modularisation; Implied Readability; Restricted Readability; Fat JARs; and Java Images.
Al the source code, inclusive a extensive documentation, can be found here: https://github.com/ekholabs/postcode-modules
Microservices with Spring Cloud
That presentation covers some aspects of Spring Cloud and Netflix OSS projects, with a working demo using Java 8 and the the goodies that Spring offers. The source code of the demo can be found here -> https://github.com/ekholabs/bookstore-microservices
Machine intelligence
This talk is divided in 3 parts: inspiration to study; the business aspects of Artificial Intelligence; and its technical aspects, with a practical demo and technical explanations about Overfitting, NN, CNN, PCA and feature extraction. The code used during the demo will soon be on Github.
Embracing Reactive Streams with Java 9 and Spring 5
This talk is about going back to the basics, when Reactive Programming was not a buzz and not used in the Enterprise Arena. There will be moments when you will regret being here, but then we will present you with the Beauty of the Seventies. This talk intends to make you travel through time, experiencing how things were done back then. From the Beauty of the Seventies we will move straight to the GoF Observer pattern, which has been implemented over and over again with Publish / Subscriber mechanisms. That will depict the foundations of what we have seen with Functional Programming, Reactive Programming, FRP and Reactive Streams. Although all that sounds interesting, it’s not our last stop. We still have to go over Java 9 and Spring Web Reactive Framework. It embraces Reactive Streams and Reactor, by defying the challenge of switching from imperative-style to non-blocking logic. It might be seem by some as shiny and new, but it’s based on a well established idea that has been helping us for more than 40 years.
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Improving Machine Learning
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During the hour, we’ll take you through:
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Overview
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DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
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Ai - A Practical Approach
- 1. Artificial Intelligence A Practical Approach
- 2. Who am I? Software & Machine Learning Engineer; City.AI Ambassador; IBM Watson AI XPRIZE contestant; Kaggler; Guest attendee at AI for Good Global Summit at the UN; X-Men geek; family man and father of 5 (3 kids and 2 cats). @wilderrodrigues
- 3. Machine Learning without a Ph.D.
- 4. What is it About? Source: World Wide Web Foundation, June 2017
- 5. Points to Focus On Number and Types of Layers; Initialisation; Regularisation; Cost Functions; Optimisation; Data Augmentation.
- 6. Shallow Neural Networks Random Initialisation Mean Squared Error Stochastic Gradient Descent Sigmoid
- 7. Initialisation and Loss Function Zero initialisation ☹ W1 = np.zeros((layers_dims[l], layers_dims[l - 1])) Random initialisation 😐 W1 = np.random.randn(layers_di ms[l], layers_dims[l - 1]) * 10 Mean Squared Error ☹ 1/m.sum(Y_hat, Y)**2
- 8. Xavier Glorot L2 regularisation Cross Entropy Adam Tanh Intermediate Neural Networks
- 9. Initialisation and Loss Function Xavier Glorot 😄 W1 = np.random.randn(layers[l], layers[l - 1]) * np.sqrt(2 / (layers[l - 1] + layers[l])) L2 regularisation ☺ (ƛ/2.m).sum(W**2) Adam Exponentially Weighted Averages Vt = βVt-1 + (1 - β)𝛳t RMSProp Cross Entropy 😄 1/m.sum(Y.log(a)+(1-Y).log(1-a))
- 10. Deep Neural Networks He Cross Entropy Dropout Adam ReLU
- 11. Initialisation and Cost Function He 😄 W1 = np.random.randn(layers[l], layers[l - 1]) * np.sqrt(2 / layers[l - 1]) Dropout 😄 ❌ ✅
- 12. Convolutional Networks Xavier Glorot Layers: Convolutional Layers Max Pooling Layers Fully Connected Layers Cross Entropy Dropout Adam ReLU
- 13. Convolutional Networks * = 6x6x3 3x3x16 4x4x16 4x4x16 2x2x16 2x2x16 * =
- 14. Residual Networks Xavier Glorot Layers: Convolutional Layers Bottleneck Layers Max Pooling Layers Fully Connected Layers Cross Entropy Dropout Adam ReLU
- 15. Residual Networks using Inception 5x5 Same 32 28x28x192 28x28x32 28x28x192 * 5x5x32 = 120m 1x1 16 1x1x192 28x28x192 28x28x16 5x5 Same 32 28x28x32 28x28x16 * 192 = 2.4m 28x28x32 * 5x5x16 = 10m
- 16. Capsule Networks The is no pose (translational and rotational) relationship between simpler features. Successive convolutional or max pooling layers to reduce spacial size.
- 17. Capsule Networks
- 18. Resources and References https://github.com/ekholabs/DLinK Machine Learning: Andrew Ng, Stanford University, Coursera. Neural Networks for Machine Learning: Geoffrey Hinton, University of Toronto, Coursera. Computational Neuroscience: Rajesh Rao & Adrienne Fairhall, Washington University, Coursera. Neural Networks and Deep Learning: Andrew Ng, DeepLearning.ai, Coursera. Structuring Machine Learning Projects: Andrew Ng, DeepLearning.ai, Coursera. Improving Deep Neural Networks with Hyperparameter Tuning, Regularisation and Optimisation: Andrew Ng, DeepLearning.ai, Coursera. Convolutional Neural Networks: Andrew Ng, DeepLearning.ai, Coursera. Calculus I: Jim Fowler, Ohio State University, Coursera.
- 19. Thank you!