Introduction to Machine Learning with H2O - Jo-Fai (Joe) Chow, H2O
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In this talk, I will give you an overview of our company (H2O.ai), our open-source machine learning platform (H2O) as well as our new projects (e.g. Deep Water and Steam). This will be useful for attendees who are not familiar with H2O.
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=== Please download slides if blurred! ===
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Generative modeling can be used for problems beyond just generation, such as anomaly detection, determining factors of variation in datasets, domain adaptation between not-aligned datasets, and building better embeddings for supervised learning tasks. Generative models can model the underlying distribution of data to check if a point belongs to that distribution or create new points from the distribution. They can learn low-dimensional manifolds on which real-world high-dimensional data like images lie. This allows generative models to be applied to challenges like filtering, style transfer, and improving embeddings to boost performance on downstream tasks.
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"Continual Learning" (CL) is indeed a fast emerging topic in AI concerning the ability to efficiently improve the performance of a deep model over time, dealing with a long (and possibly unlimited) sequence of data/tasks. In this workshop, after a brief introduction of the topic, we’ll implement different Continual Learning strategies and assess them on common vision benchmarks. We’ll conclude the workshop with a look at possible real world applications of CL.
Vincenzo Lomonaco is a Deep Learning PhD student at the University of Bologna and founder of ContinualAI.org. He is also the PhD students representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses “Machine Learning” and “Computer Architectures” in the same department. Previously, he was a Machine Learning software engineer at IDL in-line Devices and a Master Student at the University of Bologna where he graduated cum laude in 2015 with the dissertation “Deep Learning for Computer Vision: a Comparison Between CNNs and HTMs on Object Recognition Tasks".
3D Point Cloud analysis using Deep Learning
Processing 3D images has many use cases. For example, to improve autonomous car driving, to enable digital conversions of old factory buildings, to enable augmented reality solutions for medical surgeries, etc. Also 3D images help in 3D modeling and safety evaluation of products.
3D image processing brings enormous benefits but also amplifies computing cost. The size of the point cloud, the number of points, sparse and irregular point cloud, and the adverse impact of the light reflections, (partial) occlusions, etc., make it difficult for engineers to process point clouds.
Moving from using hand crafted features to using deep learning techniques to semantically segment the images, to classify objects, to detect objects, to detect actions in 3D videos, etc., we have come a long way in 3D image processing.
3D Point Cloud image processing is increasingly used to solve Industry 4.0 use cases to help architects, builders and product managers. I will share some of the innovations that are helping the progress of 3D point cloud processing. I will share the practical implementation issues we faced while developing deep learning models to make sense of 3D Point Clouds.
Attendees: Beginners and Intermediate skilled in Image Processing and 3D Point Clouds
Profile of the speaker:
SK Reddy is the Chief Product Officer AI in Hexagon (www.hexagon.com). He is an AI and ML expert and a successful twice startup entrepreneur. He is an AI startup advisor too. Also he is a frequent speaker in conferences and is an AI blogger.
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3. Key aspects of the Deep Time-to-Failure approach include using censored and uncensored training data, consuming raw time series as input
50 Shades of Text - Leveraging Natural Language Processing (NLP), Alessandro ...
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"How Pirelli uses Domino and Plotly for Smart Manufacturing" by Alberto Arrig...
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Bio:
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He holds a PhD in biostatistics from the University of Milan Bicocca and prior to joining Pirelli was a staff data scientist at the National Institute of Molecular Genetics (Milan), as well as a Fulbright student at the Santa Clara University and visiting PhD student at Pacific Biosciences (Menlo Park, CA).
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
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HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
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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Introduction to Machine Learning with H2O - Jo-Fai (Joe) Chow, H2O
- 1. Introduction to Machine Learning with H2O Jo-fai (Joe) Chow Data Scientist joe@h2o.ai @matlabulous Data Science Milan Politecnico di Milano 10th October, 2016
- 2. About Me: Civil Engineer → Data Scientist • 2005 - 2015 • Water Engineer o Consultant for Utilities o Industrial PhD • Water Engineering + Machine Learning • Discovered H2O in 2014! • 2015 - Present • Data Scientist o Virgin Media (UK) o Domino Data Lab (US) o H2O.ai (US) 2 Why? Long story – see bit.ly/joe_h2o_talk2
- 3. Agenda • First Talk (25 mins) o About H2O.ai o Demo • A Simple Classification Task • H2O’s Web Interface o Why H2O? • Our Community • Our Customers o What’s Next? • New H2O Features • Second Talk (25 mins) o H2O for IoT • Predictive Maintenance • Anomaly Detection • H2O’s R Interface • Third Talk (25 mins) o Deep Water o Demo • H2O + mxnet on GPU • H2O’s Python Interface 3
- 4. About H2O.ai
- 5. About H2O.ai • H2O.ai, the Company o Team: 80 (70 shown) o Founded in 2012 o HQ: Mountain View, California • H2O, the Platform o Open Source (Apache 2.0) o Algorithms written in Java • Fast, distributed and scalable o Multiple interfaces to suit different users • Web, R, Python, Java, Scala, REST/JSON o Works with desktop/laptop, cloud, Spark and Hadoop Joe
- 7. Current Algorithm Overview 7 Joe’s Strata Hadoop London Talk bit.ly/joe_h2o_talk4 Today’s Demos Joe’s LondonR Talk bit.ly/joe_h2o_talk3
- 9. H2O’s Mission 9 Making Machine Learning Accessible to Everyone Photo credit: Virgin Media
- 10. H2O Web Interface Demo
- 11. A Typical Machine Learning Task • Demo o Dataset – MNIST • LeCun et al. (1999) • Hand-written Digits o Import & Explore Data o Build & Evaluate Models o Make Predictions 11Photo credit: http://www.opendeep.org/v0.0.5/docs/tutorial-classifying-handwritten-mnist-images
- 12. MNIST Hand-Written Digits • 784 Inputs o 28 x 28 = 784 pixels • 1 Output o 0, 1, 2, 3, 4, 5, 6, 7, 8 or 9 o Classification • Files o Train (60k Records) o Test (10k) • Links o https://s3.amazonaws.com/h2o-public-test- data/bigdata/laptop/mnist/train.csv.gz o https://s3.amazonaws.com/h2o-public-test- data/bigdata/laptop/mnist/test.csv.gz 12 Photo credit: https://ml4a.github.io/ml4a/neural_networks/
- 13. H2O Flow (Web Interface) Demo • Download and unzip jar from www.h2o.ai • In terminal: o java -jar h2o.jar • Web browser: o localhost:54321 13
- 14. H2O Live Demo
- 15. More H2O Flow Examples 15
- 16. Other H2O Interfaces • R • Python • docs.h2o.ai 16 Key Resources
- 17. More Advanced Topics • Advanced Features o Hyperparameters Tuning o Model Stacking o Saving/Loading Models o Export Plain Old Java Object (POJO) • Key Resources o docs.h2o.ai • Joe’s Previous H2O Talks o bit.ly/joe_h2o_talk3 o bit.ly/h2o_budapest_1 o bit.ly/h2o_paris_1 17
- 18. Why H2O?
- 19. 19
- 20. Szilard Pafka – Chief Data Scientist at Epoch • Sziland’s talks / blog posts about H2O: o ML Benchmark o Intro to ML with H2O o H2O Scoring o Tweets 20
- 21. Szilard Pafka – Why H2O? 21 • Szilard’s Summary Slide
- 23. H2O Community Support 23 Google forum – h2osteam community.h2o.ai Please try
- 24. #AroundTheWorldWithH2Oai 24 Strata Hadoop London PyData Amsterdam useR! 2016 Stanford satRdays Budapest London Kaggle Meetup Chelsea FC Paris ML Meetup Big Data London
- 26. H2O Usage in Italy 26 www.h2o.ai/community
- 27. 27
- 29. H2O in Action 29 Thank you Data Science Milan – May 19, 2016 Bringing Deep Learning into production - Paolo Platter, AgileLab http://www.slideshare.net/ds_mi/bringing-deep-learning-into-production-paolo-platter-agilelab
- 30. What’s Next?
- 31. H2O is Evolving • H2O Open Tour NYC YouTube Playlist o Advanced data munging o Visual ML o Deep Water (3rd talk) o Sparkling Water • PySparkling & RSparkling o Steam 31 Next time?
- 32. H2O’s Mission 32 Making Machine Learning Accessible to Everyone Photo credit: Virgin Media
- 33. End of First Talk – Thanks! 33 • Data Science Milan • Gianmario Spacagna • Politecnico di Milano • Resources o bit.ly/h2o_milan_1 o www.h2o.ai o docs.h2o.ai • Contact o joe@h2o.ai o @matlabulous o github.com/woobe
- 34. Extra Slides (H2O Flow Demo Screenshots – just in case)
- 35. 35 Upload the file without decompressing it first
- 36. 36 Change the data type of “label” from “Numeric” to “Enum” (categorical)
- 37. 37 Note: Size in Memory Click on individual labels to explore data
- 38. 38
- 39. 39 Split the full dataset into training (80% = 48k records) and validation (20% = 12k) – a common machine learning practice
- 40. 40 Click and select parameters for model training
- 41. 41 Users have full access to all available parameters – fine-tune model training process For example, I am using rectifier with dropout as the activation to train the model for 20 epochs with classes balancing Leaving other settings as default
- 42. 42 Training the model with estimated remaining time – users can stop the process early if they want to
- 43. 43 Performance (logloss) on validation set Performance (logloss) on training set
- 44. 44 Confusion Matrix on Training Set (48k Records) About 2% Error Confusion Matrix on Validation Set (12k Records) About 4% Error
- 45. 45 Using the model for prediction on test set
- 46. 46 Confusion Matrix on Test Set (10k Records) About 4% Error (similar to validation)
- 47. 47 Full prediction outputs including individual probabilities and predicted label