Time series related problems have traditionally been solved using engineered features obtained by heuristic processes.
https://www.bigdataspain.org/2017/talk/state-of-the-art-time-series-analysis-with-deep-learning
Big Data Spain 2017
November 16th - 17th
Distributed implementation of a lstm on spark and tensorflowEmanuel Di Nardo
Academic project based on developing a LSTM distributing it on Spark and using Tensorflow for numerical operations.
Source code: https://github.com/EmanuelOverflow/LSTM-TensorSpark
Applying your Convolutional Neural NetworksDatabricks
Part 3 of the Deep Learning Fundamentals Series, this session starts with a quick primer on activation functions, learning rates, optimizers, and backpropagation. Then it dives deeper into convolutional neural networks discussing convolutions (including kernels, local connectivity, strides, padding, and activation functions), pooling (or subsampling to reduce the image size), and fully connected layer. The session also provides a high-level overview of some CNN architectures. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
Deep Learning with Apache Spark: an IntroductionEmanuele Bezzi
Presented at Scala Italy 2016 with Andrea Bessi
Neural networks and deep learning have seen a spectacular advance during the last few years and represent now the state of the art in tasks such as image recognition, automated translations and natural language processing.
Unfortunately, most of the high performance deep learning implementations are single-node only, not being therefore particularly scalable.
During this talk, we will demonstrate how Apache Spark, the fast and general engine for large-scale data processing, can be used to train artificial neural networks, thus allowing to achieve high performance and parallel computing at the same time.
Startup.Ml: Using neon for NLP and Localization Applications Intel Nervana
Speaker: Arjun Bansal, co-founder of Nervana Systems
Arjun Bansal’s workshop focused on neon, an open-source python based deep learning framework that has been build from the ground up for speed and ease of use. The workshop highlights how to use neon, build Recurrent Recurrent Neural Networks to generate and analyze text, and build Convolutional Autoencoders to generate images and to localize objects. Arjun also demoed the integration of neon with the Nervana cloud (in private beta) for multi-GPU training of deep networks.
Distributed implementation of a lstm on spark and tensorflowEmanuel Di Nardo
Academic project based on developing a LSTM distributing it on Spark and using Tensorflow for numerical operations.
Source code: https://github.com/EmanuelOverflow/LSTM-TensorSpark
Applying your Convolutional Neural NetworksDatabricks
Part 3 of the Deep Learning Fundamentals Series, this session starts with a quick primer on activation functions, learning rates, optimizers, and backpropagation. Then it dives deeper into convolutional neural networks discussing convolutions (including kernels, local connectivity, strides, padding, and activation functions), pooling (or subsampling to reduce the image size), and fully connected layer. The session also provides a high-level overview of some CNN architectures. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
Deep Learning with Apache Spark: an IntroductionEmanuele Bezzi
Presented at Scala Italy 2016 with Andrea Bessi
Neural networks and deep learning have seen a spectacular advance during the last few years and represent now the state of the art in tasks such as image recognition, automated translations and natural language processing.
Unfortunately, most of the high performance deep learning implementations are single-node only, not being therefore particularly scalable.
During this talk, we will demonstrate how Apache Spark, the fast and general engine for large-scale data processing, can be used to train artificial neural networks, thus allowing to achieve high performance and parallel computing at the same time.
Startup.Ml: Using neon for NLP and Localization Applications Intel Nervana
Speaker: Arjun Bansal, co-founder of Nervana Systems
Arjun Bansal’s workshop focused on neon, an open-source python based deep learning framework that has been build from the ground up for speed and ease of use. The workshop highlights how to use neon, build Recurrent Recurrent Neural Networks to generate and analyze text, and build Convolutional Autoencoders to generate images and to localize objects. Arjun also demoed the integration of neon with the Nervana cloud (in private beta) for multi-GPU training of deep networks.
Introduction to deep learning @ Startup.ML by Andres RodriguezIntel Nervana
Deep learning is unlocking tremendous economic value across various market sectors. Individual data scientists can draw from several open source frameworks and basic hardware resources during the very initial investigative phases but quickly require significant hardware and software resources to build and deploy production models. Intel offers various software and hardware to support a diversity of workloads and user needs. Intel Nervana delivers a competitive deep learning platform to make it easy for data scientists to start from the iterative, investigatory phase and take models all the way to deployment. This platform is designed for speed and scale, and serves as a catalyst for all types of organizations to benefit from the full potential of deep learning. Example of supported applications include but not limited to automotive speech interfaces, image search, language translation, agricultural robotics and genomics, financial document summarization, and finding anomalies in IoT data.
End-to-end speech recognition in Neon presented by Anthony Ndirango and Tyler Lee
Modern automatic speech recognition systems incorporate tremendous amount of expert knowledge and a wide array of machine learning techniques. The promise of deep learning is to strip away much of this complexity in favor of the flexibility of neural networks. We will describe our efforts in implementing end-to-end speech recognition in neon by combining convolutional and recurrent neural networks to create an acoustic model followed by a graph-based decoding scheme. These types of models are trained to go directly from raw waveforms to transcribed speech without requiring any kind of explicit forced alignment. We will also discuss additional challenges that must be overcome to produce state-of-the-art results.
Urs Köster - Convolutional and Recurrent Neural NetworksIntel Nervana
Speaker: Urs Köster, PhD
Urs will join us to dive deep into the field of Deep Learning and focus on Convolutional and Recurrent Neural Networks. The talk will be followed by a workshop highlighting neon™, an open source python based deep learning framework that has been built from the ground up for speed and ease of use.
Brief presentation about keras framework. The propose of this presentation is to give some ideas about how it works and its main functionalities. In addition, is also shown a function to create different models from a config file.
Language translation with Deep Learning (RNN) with TensorFlowS N
The author is going to take you into the realm of Recurrent Neural Network (RNN). He will be training a sequence to sequence model on a dataset of English and French sentences that can translate new (unseen) sentences from English to French.
This will be a walkthrough of an end to end technique to train a Deep RNN model. You will learn to build various components necessary to build a Sequence-to-Sequence model.
You will learn about the fundamentals of Deep Learning, mainly RNN, concepts that will be required in this solution. A familiarity of Deep Learning concepts would be handy, but most of the concepts used in this example will be covered during the demo.
Technologies to be used:
Python, Jupyter, TensorFlow, FloydHub
Source code: https://github.com/syednasar/deeplearning/blob/master/language-translation/dlnd_language_translation.ipynb
...
AWS re:Invent 2016: Using MXNet for Recommendation Modeling at Scale (MAC306)Amazon Web Services
For many companies, recommendation systems solve important machine learning problems. But as recommendation systems grow to millions of users and millions of items, they pose significant challenges when deployed at scale. The user-item matrix can have trillions of entries (or more), most of which are zero. To make common ML techniques practical, sparse data requires special techniques. Learn how to use MXNet to build neural network models for recommendation systems that can scale efficiently to large sparse datasets.
In the past 5 years "Deep Learning" has taken the tech world by storm. Not only has it achieved groundbreaking results in many academic disciplines, it is now used extensively in practical applications for everything from face detection on Facebook to real-time language translation on Skype to identifying drug targets at pharmaceutical companies. This presentation will give an overview of what deep learning is, how it works, how it's being used, and how to get started using it in your own applications.
Anil Thomas dives deep into the field of Deep Learning and focuses on object recognition. This talk will start with a general overview of how to use neon, Convolutional Neural Networks (CNN) and applying neon to an object recognition Kaggle problem. The talk is followed by a workshop highlighting neon, an open source python based deep learning framework that has been built from the ground up for speed and ease of use.
Presentation to the Data Science Association, Machine Learning Forum on 11/7/15. For all presenations visit: http://www.datascienceassn.org/content/2015-11-07-data-science-machine-learning-forum
Alex Smola, Director of Machine Learning, AWS/Amazon, at MLconf SF 2016MLconf
Alex Smola is the Manager of the Cloud Machine Learning Platform at Amazon. Prior to his role at Amazon, Smola was a Professor in the Machine Learning Department of Carnegie Mellon University and cofounder and CEO of Marianas Labs. Prior to that he worked at Google Strategic Technologies, Yahoo Research, and National ICT Australia. Prior to joining CMU, he was professor at UC Berkeley and the Australian National University. Alex obtained his PhD at TU Berlin in 1998. He has published over 200 papers and written or coauthored 5 books.
Abstract summary
Personalization and Scalable Deep Learning with MXNET: User return times and movie preferences are inherently time dependent. In this talk I will show how this can be accomplished efficiently using deep learning by employing an LSTM (Long Short Term Model). Moreover, I will show how to train large scale distributed parallel models using MXNet efficiently. This includes a brief overview of key components of defining networks, of optimization, and a walkthrough of the steps required to allocate machines, and to train a model.
Daniel Shank, Data Scientist, Talla at MLconf SF 2016MLconf
Neural Turing Machines: Perils and Promise: Daniel Shank is a Senior Data Scientist at Talla, a company developing a platform for intelligent information discovery and delivery. His focus is on developing machine learning techniques to handle various business automation tasks, such as scheduling, polls, expert identification, as well as doing work on NLP. Before joining Talla as the company’s first employee in 2015, Daniel worked with TechStars Boston and did consulting work for ThriveHive, a small business focused marketing company in Boston. He studied economics at the University of Chicago.
First steps with Keras 2: A tutorial with ExamplesFelipe
In this presentation, we give a brief introduction to Keras and Neural networks, and use examples to explain how to build and train neural network models using this framework.
Talk given as part of an event by Rio Machine Learning Meetup.
Energy Monitoring With Self-taught Deep NetworkYiqun Hu
This is the presentation of my talk in O'Reilly Strata Data Conference Singapore 2017. It is about how we can extract useful knowledge from unlabelled time series to help energy monitoring applications.
Image Classification Done Simply using Keras and TensorFlow Rajiv Shah
This presentation walks through the process of building an image classifier using Keras with a TensorFlow backend. It will give a basic understanding of image classification and show the techniques used in industry to build image classifiers. The presentation will start with building a simple convolutional network, augmenting the data, using a pretrained network, and finally using transfer learning by modifying the last few layers of a pretrained network. The classification will be based on the classic example of classifying cats and dogs. The code for the presentation can be found at https://github.com/rajshah4/image_keras, and the presentation will discuss how to extend the code to your own pictures to make a custom image classifier.
ESAI-CEU-UCH solution for American Epilepsy Society Seizure Prediction ChallengeFrancisco Zamora-Martinez
Presentation given at Cyient Insights (Hyderabad, India).
This work presents the solution proposed by Universidad CEU Cardenal Herrera (ESAI-CEU-UCH) at Kaggle American Epilepsy Society Seizure Prediction Challenge. The proposed solution was positioned as 4th at Kaggle competition.
Different kind of input features (different preprocessing pipelines) and different statistical models are being proposed. This diversity was motivated to improve model combination result.
It is important to note that any of the proposed systems use test set for calibration. The competition allow to do this model calibration using test set, but doing it will reduce the reproducibility of the results in a real world implementation.
Introduction to deep learning @ Startup.ML by Andres RodriguezIntel Nervana
Deep learning is unlocking tremendous economic value across various market sectors. Individual data scientists can draw from several open source frameworks and basic hardware resources during the very initial investigative phases but quickly require significant hardware and software resources to build and deploy production models. Intel offers various software and hardware to support a diversity of workloads and user needs. Intel Nervana delivers a competitive deep learning platform to make it easy for data scientists to start from the iterative, investigatory phase and take models all the way to deployment. This platform is designed for speed and scale, and serves as a catalyst for all types of organizations to benefit from the full potential of deep learning. Example of supported applications include but not limited to automotive speech interfaces, image search, language translation, agricultural robotics and genomics, financial document summarization, and finding anomalies in IoT data.
End-to-end speech recognition in Neon presented by Anthony Ndirango and Tyler Lee
Modern automatic speech recognition systems incorporate tremendous amount of expert knowledge and a wide array of machine learning techniques. The promise of deep learning is to strip away much of this complexity in favor of the flexibility of neural networks. We will describe our efforts in implementing end-to-end speech recognition in neon by combining convolutional and recurrent neural networks to create an acoustic model followed by a graph-based decoding scheme. These types of models are trained to go directly from raw waveforms to transcribed speech without requiring any kind of explicit forced alignment. We will also discuss additional challenges that must be overcome to produce state-of-the-art results.
Urs Köster - Convolutional and Recurrent Neural NetworksIntel Nervana
Speaker: Urs Köster, PhD
Urs will join us to dive deep into the field of Deep Learning and focus on Convolutional and Recurrent Neural Networks. The talk will be followed by a workshop highlighting neon™, an open source python based deep learning framework that has been built from the ground up for speed and ease of use.
Brief presentation about keras framework. The propose of this presentation is to give some ideas about how it works and its main functionalities. In addition, is also shown a function to create different models from a config file.
Language translation with Deep Learning (RNN) with TensorFlowS N
The author is going to take you into the realm of Recurrent Neural Network (RNN). He will be training a sequence to sequence model on a dataset of English and French sentences that can translate new (unseen) sentences from English to French.
This will be a walkthrough of an end to end technique to train a Deep RNN model. You will learn to build various components necessary to build a Sequence-to-Sequence model.
You will learn about the fundamentals of Deep Learning, mainly RNN, concepts that will be required in this solution. A familiarity of Deep Learning concepts would be handy, but most of the concepts used in this example will be covered during the demo.
Technologies to be used:
Python, Jupyter, TensorFlow, FloydHub
Source code: https://github.com/syednasar/deeplearning/blob/master/language-translation/dlnd_language_translation.ipynb
...
AWS re:Invent 2016: Using MXNet for Recommendation Modeling at Scale (MAC306)Amazon Web Services
For many companies, recommendation systems solve important machine learning problems. But as recommendation systems grow to millions of users and millions of items, they pose significant challenges when deployed at scale. The user-item matrix can have trillions of entries (or more), most of which are zero. To make common ML techniques practical, sparse data requires special techniques. Learn how to use MXNet to build neural network models for recommendation systems that can scale efficiently to large sparse datasets.
In the past 5 years "Deep Learning" has taken the tech world by storm. Not only has it achieved groundbreaking results in many academic disciplines, it is now used extensively in practical applications for everything from face detection on Facebook to real-time language translation on Skype to identifying drug targets at pharmaceutical companies. This presentation will give an overview of what deep learning is, how it works, how it's being used, and how to get started using it in your own applications.
Anil Thomas dives deep into the field of Deep Learning and focuses on object recognition. This talk will start with a general overview of how to use neon, Convolutional Neural Networks (CNN) and applying neon to an object recognition Kaggle problem. The talk is followed by a workshop highlighting neon, an open source python based deep learning framework that has been built from the ground up for speed and ease of use.
Presentation to the Data Science Association, Machine Learning Forum on 11/7/15. For all presenations visit: http://www.datascienceassn.org/content/2015-11-07-data-science-machine-learning-forum
Alex Smola, Director of Machine Learning, AWS/Amazon, at MLconf SF 2016MLconf
Alex Smola is the Manager of the Cloud Machine Learning Platform at Amazon. Prior to his role at Amazon, Smola was a Professor in the Machine Learning Department of Carnegie Mellon University and cofounder and CEO of Marianas Labs. Prior to that he worked at Google Strategic Technologies, Yahoo Research, and National ICT Australia. Prior to joining CMU, he was professor at UC Berkeley and the Australian National University. Alex obtained his PhD at TU Berlin in 1998. He has published over 200 papers and written or coauthored 5 books.
Abstract summary
Personalization and Scalable Deep Learning with MXNET: User return times and movie preferences are inherently time dependent. In this talk I will show how this can be accomplished efficiently using deep learning by employing an LSTM (Long Short Term Model). Moreover, I will show how to train large scale distributed parallel models using MXNet efficiently. This includes a brief overview of key components of defining networks, of optimization, and a walkthrough of the steps required to allocate machines, and to train a model.
Daniel Shank, Data Scientist, Talla at MLconf SF 2016MLconf
Neural Turing Machines: Perils and Promise: Daniel Shank is a Senior Data Scientist at Talla, a company developing a platform for intelligent information discovery and delivery. His focus is on developing machine learning techniques to handle various business automation tasks, such as scheduling, polls, expert identification, as well as doing work on NLP. Before joining Talla as the company’s first employee in 2015, Daniel worked with TechStars Boston and did consulting work for ThriveHive, a small business focused marketing company in Boston. He studied economics at the University of Chicago.
First steps with Keras 2: A tutorial with ExamplesFelipe
In this presentation, we give a brief introduction to Keras and Neural networks, and use examples to explain how to build and train neural network models using this framework.
Talk given as part of an event by Rio Machine Learning Meetup.
Energy Monitoring With Self-taught Deep NetworkYiqun Hu
This is the presentation of my talk in O'Reilly Strata Data Conference Singapore 2017. It is about how we can extract useful knowledge from unlabelled time series to help energy monitoring applications.
Image Classification Done Simply using Keras and TensorFlow Rajiv Shah
This presentation walks through the process of building an image classifier using Keras with a TensorFlow backend. It will give a basic understanding of image classification and show the techniques used in industry to build image classifiers. The presentation will start with building a simple convolutional network, augmenting the data, using a pretrained network, and finally using transfer learning by modifying the last few layers of a pretrained network. The classification will be based on the classic example of classifying cats and dogs. The code for the presentation can be found at https://github.com/rajshah4/image_keras, and the presentation will discuss how to extend the code to your own pictures to make a custom image classifier.
ESAI-CEU-UCH solution for American Epilepsy Society Seizure Prediction ChallengeFrancisco Zamora-Martinez
Presentation given at Cyient Insights (Hyderabad, India).
This work presents the solution proposed by Universidad CEU Cardenal Herrera (ESAI-CEU-UCH) at Kaggle American Epilepsy Society Seizure Prediction Challenge. The proposed solution was positioned as 4th at Kaggle competition.
Different kind of input features (different preprocessing pipelines) and different statistical models are being proposed. This diversity was motivated to improve model combination result.
It is important to note that any of the proposed systems use test set for calibration. The competition allow to do this model calibration using test set, but doing it will reduce the reproducibility of the results in a real world implementation.
This talk was presented in Startup Master Class 2017 - http://aaiitkblr.org/smc/ 2017 @ Christ College Bangalore. Hosted by IIT Kanpur Alumni Association and co-presented by IIT KGP Alumni Association, IITACB, PanIIT, IIMA and IIMB alumni.
My co-presenter was Biswa Gourav Singh. And contributor was Navin Manaswi.
http://dataconomy.com/2017/04/history-neural-networks/ - timeline for neural networks
Deep Learning Fundamentals Workshop
This hands-on workshop will provide an introduction to deep learning to the participants who are already aware of data science and machine learning techniques but have not worked on deep learning. The course will cover the different types of network architectures that make the foundations of deep learning.
Following topics will be covered:
1. What is deep learning and what are the use cases of it?
2. Introduction to Feed Forward Neural Networks including the hands-on session
3. Building an Image Classifier using Convolutional Natural Networks
4. Applying Recurrent Neural Network and LSTM Network for text classification
5. How to build your own deep learning projects?
An overview of Deep Learning With Neural Networks. Use cases of Deep learning and it's development. Basic introduction tp the layers of Neural Networks.
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
Big Data, Big Quality? by Irene Gonzálvez at Big Data Spain 2017Big Data Spain
Insights can only be as good as the data. The data quality domain is enormously large, so you need to understand your company pain points to know what to focus on first.
https://www.bigdataspain.org/2017/talk/big-data-big-quality
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Scaling a backend for a big data and blockchain environment by Rafael Ríos at...Big Data Spain
2gether is a financial platform based on Blockchain, Big Data and Artificial Intelligence that allows interaction between users and third-party services in a single interface.
https://www.bigdataspain.org/2017/talk/scaling-a-backend-for-a-big-data-and-blockchain-environment
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Disaster Recovery for Big Data by Carlos Izquierdo at Big Data Spain 2017Big Data Spain
All modern Big Data solutions, like Hadoop, Kafka or the rest of the ecosystem tools, are designed as distributed processes and as such include some sort of redundancy for High Availability.
https://www.bigdataspain.org/2017/talk/disaster-recovery-for-big-data
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Presentation: Boost Hadoop and Spark with in-memory technologies by Akmal Cha...Big Data Spain
In this presentation, attendees will see how to speed up existing Hadoop and Spark deployments by just making Apache Ignite responsible for RAM utilization. No code modifications, no new architecture from scratch!
https://www.bigdataspain.org/2017/talk/boost-hadoop-and-spark-with-in-memory-technologies
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Data science for lazy people, Automated Machine Learning by Diego Hueltes at ...Big Data Spain
The power of this new set of tools for Data Science. Is really easy to start applying these technics in your current workflow.
https://www.bigdataspain.org/2017/talk/data-science-for-lazy-people-automated-machine-learning
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Training Deep Learning Models on Multiple GPUs in the Cloud by Enrique Otero ...Big Data Spain
GPUs on the cloud as Infrastructure as a Service (IaaS) seem a commodity. However to efficiently distribute deep learning tasks on several GPUs is challenging.
https://www.bigdataspain.org/2017/talk/training-deep-learning-models-on-multiple-gpus-in-the-cloud
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Unbalanced data: Same algorithms different techniques by Eric Martín at Big D...Big Data Spain
Unbalanced data is a specific data configuration that appears commonly in nature. Applying machine learning techniques to this kind of data is a difficult process, usually addressed by unbalanced reduction techniques.
https://www.bigdataspain.org/2017/talk/unbalanced-data-same-algorithms-different-techniques
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Trading at market speed with the latest Kafka features by Iñigo González at B...Big Data Spain
Not long ago only banks and hedge funds could afford doing automated and High Frequency Trading, that is, the ability to send buy commodities in microseconds intervals.
https://www.bigdataspain.org/2017/talk/trading-at-market-speed-with-the-latest-kafka-features
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Unified Stream Processing at Scale with Apache Samza by Jake Maes at Big Data...Big Data Spain
The shift to stream processing at LinkedIn has accelerated over the past few years. We now have over 200 Samza applications in production processing more than 260B events per day.
https://www.bigdataspain.org/2017/talk/apache-samza-jake-maes
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
The Analytic Platform behind IBM’s Watson Data Platform by Luciano Resende a...Big Data Spain
IBM has built a “Data Science Experience” cloud service that exposes Notebook services at web scale.
https://www.bigdataspain.org/2017/talk/the-analytic-platform-behind-ibms-watson-data-platform
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Artificial Intelligence and Data-centric businesses by Óscar Méndez at Big Da...Big Data Spain
Artificial Intelligence and Data-centric businesses.
https://www.bigdataspain.org/2017/talk/tbc
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Why big data didn’t end causal inference by Totte Harinen at Big Data Spain 2017Big Data Spain
Ten years ago there were rumours of the death of causal inference. Big data was supposed to enable us to rely on purely correlational data to predict and control the world.
https://www.bigdataspain.org/2017/talk/why-big-data-didnt-end-causal-inference
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Meme Index. Analyzing fads and sensations on the Internet by Miguel Romero at...Big Data Spain
The Meme of the Internet Index will be the new normal to analyze and predict facts and sensations which go around the Internet.
https://www.bigdataspain.org/2017/talk/meme-index-analyzing-fads-and-sensations-on-the-internet
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Vehicle Big Data that Drives Smart City Advancement by Mike Branch at Big Dat...Big Data Spain
Geotab is a leader in the expanding world of Internet of Things (IoT) and telematics industry with Big Data.
https://www.bigdataspain.org/2017/talk/vehicle-big-data-that-drives-smart-city-advancement
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
End of the Myth: Ultra-Scalable Transactional Management by Ricardo Jiménez-P...Big Data Spain
The talk will focus on explaining why operational databases do not scale due to limitations in legacy transactional management.
https://www.bigdataspain.org/2017/talk/end-of-the-myth-ultra-scalable-transactional-management
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Attacking Machine Learning used in AntiVirus with Reinforcement by Rubén Mart...Big Data Spain
In recent years Machine Learning (ML) and especially Deep Learning (DL) have achieved great success in many areas such as visual recognition, NLP or even aiding in medical research.
https://www.bigdataspain.org/2017/talk/attacking-machine-learning-used-in-antivirus-with-reinforcement
Big Data Spain 2017
16th - 17th Kinépolis Madrid
More people, less banking: Blockchain by Salvador Casquero at Big Data Spain ...Big Data Spain
Primary function of banking sector is promoting economic activity; which means “commerce”, exchanging what someone produces-has for something that someone consumes-desires.
https://www.bigdataspain.org/2017/talk/more-people-less-banking-blockchain
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Make the elephant fly, once again by Sourygna Luangsay at Big Data Spain 2017Big Data Spain
Bol.com has been an early Hadoop user: since 2008 where it was first built for a recommendation algorithm.
https://www.bigdataspain.org/2017/talk/make-the-elephant-fly-once-again
Big Data Spain 2017
16th - 17th Kinépolis Madrid
Feature selection for Big Data: advances and challenges by Verónica Bolón-Can...Big Data Spain
In an era of growing data complexity and volume and the advent of Big Data, feature selection has a key role to play in helping reduce high-dimensionality in machine learning problems.
https://www.bigdataspain.org/2017/talk/feature-selection-for-big-data-advances-and-challenges
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
State of the art time-series analysis with deep learning by Javier Ordóñez at Big Data Spain 2017
1.
2. State of the art time-
series analysis with
deep learning
3. Who am I?
Francisco Javier
Ordóñez
Lead Data Scientist
javier.ordonez@stylesage.c
o
http://stylesage.
co
4. What is this about?
Approach for time series analysis using deep neural nets
What are we going to see:
Brief introduction
Deep learning concepts
Model
Use case
Core ref:
“Deep convolutional and lstm recurrent neural networks for multimodal wearable activity
recognition” FJ Ordóñez, et. al
5. Time series classification Time series forecasting
ECG anomaly detection Energy demand prediction
Human activity recognition Stock market prediction
Time series
A time series is a sequence of regular time-ordered observations
e.g. stock prices, weather readings, smartphone sensor data, health
monitoring data
“Traditional” approaches for time series analysis are based on autoregressive
models
-Challenges: Tackle feature design, usually a single signal involved, etc
8. Model that learns by the example
●using many examples
●defined as series of hierarchically connected functions
(layers)
●can be very complex (deep!)
Artificial neural
nets
9. Model that learns by the example
●using many examples
●defined as series of hierarchically connected functions
(layers)
●can be very complex (deep!)
Input Hidden layer Output
Artificial neural
nets
10. What does it know?
●composed by units (neurons), distributed in layers, which
control whether the data flow should continue (activation
level)
●controlled by “weights” and nonlinear functions
Artificial neural
nets
Input Hidden layer Output
11. How does it learn?
●correcting the errors
●backpropagation!, the weights are adjusted and readjusted,
layer by layer, until the network can have the fewest
possible errors
Artificial neural
nets
Input Hidden layer Output
12. Case: image processing
●Classical problem: MNIST dataset
○It’s the “Hello World” of image
processing
●Recognition of handwritten numbers
●Training - 60,000 pictures to learn the
relation picture-label
14. ●Convolutional nets are less dense = less number of
weights
●Focus on local patterns, assuming that neighboring
variables are locally correlated
- Images - Pixels that are close
●One simple operation is repeated over and over several
times starting with the raw input data.
●They work very well. State of the art results in different
fields
Convnets
22. Convnets:
signals●Same principles:
○Operations applied in a hierarchy
○Each filter will define a feature
map
○As many features maps as filters
○Each filter captures a pattern
●Result is another sequence/signal
○Transformed by the operations
3rd
layer
2nd
layer
1st
24. Memory cells which can maintain its state over time, and non-linear
gating units which regulate the information flow into and out of the
cell
Long short-term
memory
“Generating Sequences With Recurrent Neural Networks”
25. LSTM: Layers
“Recurrent Neural Network Regularization” Zaremba, W.
●Also in a hierarchy. Output of
layer l is the input of layer
l+1
●Can model more complex
time relations
27. DeepConvLSTM
Deep framework based on convolutional and LSTM recurrent
units
●The convolutional layers are feature extractors and provide abstract
representations of the input data in feature maps.
●The recurrent layers model the temporal dynamics of the activation of the
feature maps
https://github.com/sussexwearlab/DeepConvLST
M
28. DeepConvLSTM
●Architecture
○How many layers
○How many nodes/filters
○Which type
●Data
○Batches size
○Size of filters
○Number of steps the
memory cells will learn
●Training:
○Regularization
○Learning rate
○Gradient expressions
○Init policy
Parameters are learnt automatically, but the
hyperparameters??
29. ●Architecture
○Layers:
Conv(64)−Conv(64)−Conv(64)−Conv(64)−LSTM(128)−LSTM(128)
○Type: ReLUs units for conv layers
●Data
○Batches size: 100 (careful with the GPU memory)
○Size of filters: 5 samples
○Number of steps the memory cells will learn: 24 samples
●Training
○Regularization: Dropout in the conv layers
○Learning rate: Small (0.0001)
○Gradient expressions: RMSProp. Usually a good choice for
RNN
DeepConvLSTM:
hyperparams
36. F-score
●Considers all errors equally important
●Combines precision and recall
●Value between 0 and 1
●The higher the F-score the better the
model
Metrics
Loss
●Measures of the number of errors
●Value aimed to optimize during the
learning process
●Value between 0 and 1
●The lower the loss, the better a model
1
0
f-score
1
0
40. Summary
Automatic feature learning. A convolutional filter captures a
specific salient pattern and would act as a feature detector
Core ref:
“Deep convolutional and lstm recurrent neural networks for multimodal wearable activity
recognition” FJ Ordóñez, et. al
We have to deal with the hyperparameters.
“Learning to learn by gradient descent by gradient descent”
Andrychowicz. M.
Recurrent layers can learn the temporal dynamics of such
features
State of the art performance with restrained nets (~1M
params). Capable of real time processing