The document discusses using recurrent neural networks, specifically long short-term memory networks (LSTMs), to perform pattern matching on financial time series data to identify chart patterns. It proposes an approach using multi-dimensional inputs of price and indicator time series without hand-crafted features. The model would be trained on examples collected by experts to output a confidence level for pattern matches. Experiments showed LSTM models can reasonably fit training and testing data. Future work includes improving the base model, incorporating reinforcement learning, and generating trading signals.
Anima Anandkumar at AI Frontiers : Modern ML : Deep, distributed, Multi-dimen...AI Frontiers
As the data and models scale, it becomes necessary to have multiple processing units for both training and inference. SignSGD is a gradient compression algorithm that only transmits the sign of the stochastic gradients during distributed training. This algorithm uses 32 times less communication per iteration than distributed SGD. We show that signSGD obtains free lunch both in theory and practice: no loss in accuracy while yielding speedups. Pushing the current boundaries of deep learning also requires using multiple dimensions and modalities. These can be encoded into tensors, which are natural extensions of matrices. These functionalities are available in the Tensorly package with multiple backend interfaces for large-scale deep learning.
An introduction to Machine Learning (and a little bit of Deep Learning)Thomas da Silva Paula
25-min talk about Machine Learning and a little bit of Deep Learning. Starts with some basic definitions (Supervised and Unsupervised Learning). Then, neural networks basic functionality is explained, ending up in Deep Learning and Convolutional Neural Networks.
Machine Learning Meetup that happened in Porto Alegre, Brazil.
Deep Learning with Python: Getting started and getting from ideas to insights in minutes.
PyData Seattle 2015
Alex Korbonits (@korbonits)
This presentation was given July 25, 2015 at the PyData Seattle conference hosted by PyData and NumFocus.
Deep Learning: Chapter 11 Practical MethodologyJason Tsai
Lecture for Deep Learning 101 study group to be held on June 9th, 2017.
Reference book: https://www.deeplearningbook.org/
Past video archives: https://goo.gl/hxermB
Initiated by Taiwan AI Group (https://www.facebook.com/groups/Taiwan.AI.Group/)
Anima Anandkumar at AI Frontiers : Modern ML : Deep, distributed, Multi-dimen...AI Frontiers
As the data and models scale, it becomes necessary to have multiple processing units for both training and inference. SignSGD is a gradient compression algorithm that only transmits the sign of the stochastic gradients during distributed training. This algorithm uses 32 times less communication per iteration than distributed SGD. We show that signSGD obtains free lunch both in theory and practice: no loss in accuracy while yielding speedups. Pushing the current boundaries of deep learning also requires using multiple dimensions and modalities. These can be encoded into tensors, which are natural extensions of matrices. These functionalities are available in the Tensorly package with multiple backend interfaces for large-scale deep learning.
An introduction to Machine Learning (and a little bit of Deep Learning)Thomas da Silva Paula
25-min talk about Machine Learning and a little bit of Deep Learning. Starts with some basic definitions (Supervised and Unsupervised Learning). Then, neural networks basic functionality is explained, ending up in Deep Learning and Convolutional Neural Networks.
Machine Learning Meetup that happened in Porto Alegre, Brazil.
Deep Learning with Python: Getting started and getting from ideas to insights in minutes.
PyData Seattle 2015
Alex Korbonits (@korbonits)
This presentation was given July 25, 2015 at the PyData Seattle conference hosted by PyData and NumFocus.
Deep Learning: Chapter 11 Practical MethodologyJason Tsai
Lecture for Deep Learning 101 study group to be held on June 9th, 2017.
Reference book: https://www.deeplearningbook.org/
Past video archives: https://goo.gl/hxermB
Initiated by Taiwan AI Group (https://www.facebook.com/groups/Taiwan.AI.Group/)
Zaikun Xu from the Università della Svizzera Italiana presented this deck at the 2016 Switzerland HPC Conference.
“In the past decade, deep learning as a life-changing technology, has gained a huge success on various tasks, including image recognition, speech recognition, machine translation, etc. Pio- neered by several research groups, Geoffrey Hinton (U Toronto), Yoshua Benjio (U Montreal), Yann LeCun(NYU), Juergen Schmiduhuber (IDSIA, Switzerland), Deep learning is a renaissance of neural network in the Big data era.
Neural network is a learning algorithm that consists of input layer, hidden layers and output layers, where each circle represents a neural and the each arrow connection associates with a weight. The way neural network learns is based on how different between the output of output layer and the ground truth, following by calculating the gradients of this discrepancy w.r.b to the weights and adjust the weight accordingly. Ideally, it will find weights that maps input X to target y with error as lower as possible.”
Watch the video presentation: http://insidehpc.com/2016/03/deep-learning/
See more talks in the Swiss Conference Video Gallery: http://insidehpc.com/2016-swiss-hpc-conference/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Deep Learning is the area of machine learning and one of the most talked about trends in business and computer science today.
In this talk, I will give a review of Deep Learning explaining what it is, what kinds of tasks it can do today, and what it probably could do in the future.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
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
...
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
Using Deep Learning to do Real-Time Scoring in Practical Applications - 2015-...Greg Makowski
This talk covers 4 configurations of deep learning to solve different types of application needs. Also, strategies for speed up and real-time scoring are discussed.
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Slides from Portland Machine Learning meetup, April 13th.
Abstract: You've heard all the cool tech companies are using them, but what are Convolutional Neural Networks (CNNs) good for and what is convolution anyway? For that matter, what is a Neural Network? This talk will include a look at some applications of CNNs, an explanation of how CNNs work, and what the different layers in a CNN do. There's no explicit background required so if you have no idea what a neural network is that's ok.
Using Deep Learning to do Real-Time Scoring in Practical ApplicationsGreg Makowski
http://www.meetup.com/SF-Bay-ACM/events/227480571/
(see also YouTube for a recording of the presentation)
The talk will cover a brief review of neural network basics and the following types of neural network deep learning:
* autocorrelational - unsupervised learning for extracting features. He will describe how additional layers build complexity in the feature extraction.
* convolutional - how to detect shift invariant patterns in various data sources. Horizontal shift invariant detection applies to signals like speech recognition or IoT data. Horizontal and vertical shift invariance applies to images or videos, for faces or self driving cars
* discuss details of applying deep net systems for continuous or real time scoring
* reinforcement learning or Q Learning - such as learning how to play Atari video games
* continuous space word models - such as word2vec, skipgram training, NLP understanding and translation
Picked-up lists of GAN variants which provided insights to the community. (GANs-Improved GANs-DCGAN-Unrolled GAN-InfoGAN-f-GAN-EBGAN-WGAN)
After short introduction to GANs, we look through the remaining difficulties of standard GANs and their temporary solutions (Improved GANs). By following the slides, we can see the other solutions which tried to resolve the problems in various ways, e.g. careful architecture selection (DCGAN), slight change in update (Unrolled GAN), additional constraint (InfoGAN), generalization of the loss function using various divergence (f-GAN), providing new framework of energy based model (EBGAN), another step of generalization of the loss function (WGAN).
A simplified way of approaching machine learning and deep learning from the ground up. The case for deep learning and an attempt to develop intuition for how/why it works. Advantages, state-of-the-art, and trends.
Presented at NYU Center for Genomics for NY Deep Learning Meetup
Parallel Recurrent Neural Network Architectures for Feature-rich Session-base...Balázs Hidasi
Slides for my RecSys 2016 talk on integrating image and textual information into session based recommendations using novel parallel RNN architectures.
Link to the paper: http://www.hidasi.eu/en/publications.html#p_rnn_recsys16
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/02/introducing-machine-learning-and-how-to-teach-machines-to-see-a-presentation-from-tryolabs/
Facundo Parodi, Research and Machine Learning Engineer at Tryolabs, presents the “Introduction to Machine Learning and How to Teach Machines to See” tutorial at the September 2020 Embedded Vision Summit.
What is machine learning? How can machines distinguish a cat from a dog in an image? What’s the magic behind convolutional neural networks? These are some of the questions Parodi answers in this introductory talk on machine learning in computer vision.
Parodi introduces machine learning and explores the different types of problems it can solve. He explains the main components of practical machine learning, from data gathering and training to deployment. He then focuses on deep learning as an important machine learning technique and provides an introduction to convolutional neural networks and how they can be used to solve image classification problems. Parodi will also touches on recent advancements in deep learning and how they have revolutionized the entire field of computer vision.
GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series dataYuki Hayashi
Since most of previous studies about pattern recognition on FX time series data were done by handcrafted features which were not easy to program and lacking flexibility. We propose the LSTM based deep neural network and its training method which helps traders to duplicate their strategies by just feeding what they see on the chart as training samples for AI.
Zaikun Xu from the Università della Svizzera Italiana presented this deck at the 2016 Switzerland HPC Conference.
“In the past decade, deep learning as a life-changing technology, has gained a huge success on various tasks, including image recognition, speech recognition, machine translation, etc. Pio- neered by several research groups, Geoffrey Hinton (U Toronto), Yoshua Benjio (U Montreal), Yann LeCun(NYU), Juergen Schmiduhuber (IDSIA, Switzerland), Deep learning is a renaissance of neural network in the Big data era.
Neural network is a learning algorithm that consists of input layer, hidden layers and output layers, where each circle represents a neural and the each arrow connection associates with a weight. The way neural network learns is based on how different between the output of output layer and the ground truth, following by calculating the gradients of this discrepancy w.r.b to the weights and adjust the weight accordingly. Ideally, it will find weights that maps input X to target y with error as lower as possible.”
Watch the video presentation: http://insidehpc.com/2016/03/deep-learning/
See more talks in the Swiss Conference Video Gallery: http://insidehpc.com/2016-swiss-hpc-conference/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Deep Learning is the area of machine learning and one of the most talked about trends in business and computer science today.
In this talk, I will give a review of Deep Learning explaining what it is, what kinds of tasks it can do today, and what it probably could do in the future.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
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
...
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
Using Deep Learning to do Real-Time Scoring in Practical Applications - 2015-...Greg Makowski
This talk covers 4 configurations of deep learning to solve different types of application needs. Also, strategies for speed up and real-time scoring are discussed.
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Slides from Portland Machine Learning meetup, April 13th.
Abstract: You've heard all the cool tech companies are using them, but what are Convolutional Neural Networks (CNNs) good for and what is convolution anyway? For that matter, what is a Neural Network? This talk will include a look at some applications of CNNs, an explanation of how CNNs work, and what the different layers in a CNN do. There's no explicit background required so if you have no idea what a neural network is that's ok.
Using Deep Learning to do Real-Time Scoring in Practical ApplicationsGreg Makowski
http://www.meetup.com/SF-Bay-ACM/events/227480571/
(see also YouTube for a recording of the presentation)
The talk will cover a brief review of neural network basics and the following types of neural network deep learning:
* autocorrelational - unsupervised learning for extracting features. He will describe how additional layers build complexity in the feature extraction.
* convolutional - how to detect shift invariant patterns in various data sources. Horizontal shift invariant detection applies to signals like speech recognition or IoT data. Horizontal and vertical shift invariance applies to images or videos, for faces or self driving cars
* discuss details of applying deep net systems for continuous or real time scoring
* reinforcement learning or Q Learning - such as learning how to play Atari video games
* continuous space word models - such as word2vec, skipgram training, NLP understanding and translation
Picked-up lists of GAN variants which provided insights to the community. (GANs-Improved GANs-DCGAN-Unrolled GAN-InfoGAN-f-GAN-EBGAN-WGAN)
After short introduction to GANs, we look through the remaining difficulties of standard GANs and their temporary solutions (Improved GANs). By following the slides, we can see the other solutions which tried to resolve the problems in various ways, e.g. careful architecture selection (DCGAN), slight change in update (Unrolled GAN), additional constraint (InfoGAN), generalization of the loss function using various divergence (f-GAN), providing new framework of energy based model (EBGAN), another step of generalization of the loss function (WGAN).
A simplified way of approaching machine learning and deep learning from the ground up. The case for deep learning and an attempt to develop intuition for how/why it works. Advantages, state-of-the-art, and trends.
Presented at NYU Center for Genomics for NY Deep Learning Meetup
Parallel Recurrent Neural Network Architectures for Feature-rich Session-base...Balázs Hidasi
Slides for my RecSys 2016 talk on integrating image and textual information into session based recommendations using novel parallel RNN architectures.
Link to the paper: http://www.hidasi.eu/en/publications.html#p_rnn_recsys16
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/02/introducing-machine-learning-and-how-to-teach-machines-to-see-a-presentation-from-tryolabs/
Facundo Parodi, Research and Machine Learning Engineer at Tryolabs, presents the “Introduction to Machine Learning and How to Teach Machines to See” tutorial at the September 2020 Embedded Vision Summit.
What is machine learning? How can machines distinguish a cat from a dog in an image? What’s the magic behind convolutional neural networks? These are some of the questions Parodi answers in this introductory talk on machine learning in computer vision.
Parodi introduces machine learning and explores the different types of problems it can solve. He explains the main components of practical machine learning, from data gathering and training to deployment. He then focuses on deep learning as an important machine learning technique and provides an introduction to convolutional neural networks and how they can be used to solve image classification problems. Parodi will also touches on recent advancements in deep learning and how they have revolutionized the entire field of computer vision.
GTC Japan 2015 - Experiments to apply Deep Learning to Forex time series dataYuki Hayashi
Since most of previous studies about pattern recognition on FX time series data were done by handcrafted features which were not easy to program and lacking flexibility. We propose the LSTM based deep neural network and its training method which helps traders to duplicate their strategies by just feeding what they see on the chart as training samples for AI.
Stock Market Prediction using Hidden Markov Models and Investor sentimentPatrick Nicolas
This presentation describes hidden Markov Models to predict financial markets indices using the weekly sentiment survey from the American Association of Individual Investors.
The first section describes the hidden Markov model (HMM), followed by selection of features (investors' sentiment) and labeled data (S&P 500 index).
The second section dives into HMMs for continuous observations and detection of regime shifts/structural breaks using an auto-regressive Markov chain
The last section is devoted to alternative models to HMM.
Financial forecastings using neural networks pptPuneet Gupta
The aim of the project is to predict the interest rates,bond yield variation and stock market prices using neural networks and make a comparative study of different pre-processing techniques viz Fast Fourier Transform and Hilbert Huang Transform.
this ppt needs other two also..
エヌビディアが加速するディープラーニング~進化するニューラルネットワークとその開発方法について~NVIDIA Japan
NVIDIA Deep Learning Institute
2016年4月27日のNVIDIA Deep Learning Day 2016 Springの資料です。
エヌビディア合同会社 プラットフォームビジネス本部
ディープラーニングソリューションアーキテクト 兼
CUDA エンジニア 村上 真奈
[概要]
ディープラーニングは近年、画像認識の分野で、その高い認識精度から大変注目を集めている技術です。音声認識や自動運転など画像認識の分野以外への応用が進んでおり大変期待されています。本セッションは、日々新しい構造のモデルが提案され進化しているディープラーニングの概要とGPUが必要とされている理由について簡単に説明します。
その後に、実際にディープラーニングの開発のイメージを持って戴けるように、いくつかの代表的なディープラーニングのフレームワークを使い、デモしながら各フレームワークの特徴を解説します。ディープラーニングの最新の状況が知りたい、実際の開発の際にどのフレームワークを使うべきか知りたい、開発を始める前に開発のイメージを持ちたいという方に最適です。
TensorFrames: Google Tensorflow on Apache SparkDatabricks
Presentation at Bay Area Spark Meetup by Databricks Software Engineer and Spark committer Tim Hunter.
This presentation covers how you can use TensorFrames with Tensorflow to distributed computing on GPU.
Deep Learning Use Cases - Data Science Pop-up SeattleDomino Data Lab
Companies like Google, Microsoft, Amazon and Facebook are in fierce competition for teams that can build deep-learning applications. Because of deep learning's general usefulness in pattern recognition, those applications are surprisingly diverse, ranging from image recognition to machine translation. This talk will explore deep learning use cases for the major data types -- image, sound, text and time series -- as they're emerging in the private sector. Presented by Chris Nicholson, Co-Founder and CEO at Skymind.
Applying Deep Learning to Enhance Momentum Trading Strategies in StocksLawrence Takeuchi
Contact author: larrytakeuchi@gmail.com
Abstract
We use an autoencoder composed of stacked restricted Boltzmann machines to extract
features from the history of individual stock prices. Our model is able to discover an enhanced version of the momentum effect in stocks without extensive hand-engineering of input features and deliver an annualized return of 45.93% over the 1990-2009 test period
versus 10.53% for basic momentum.
Traditional Machine Learning and Deep Learning on OpenPOWER/POWER systemsGanesan Narayanasamy
This presentation gave deep dive into various machine learning and deep learning algorithms followed by an overview of the hardware and software technologies for democratization of AI including OpenPOWER/POWER9 solutions.
Extending Flink for anomaly detection with Hierarchical Temporal Memory (HTM). Presented at Bay Area Apache Flink Meetup, in San Jose on June 27, 2016.
https://github.com/htm-community/flink-htm
This talk provides a critical view on employing machine learning / deep learning methods in algorithmic trading. We highlight the particular challenges that we meet in this domain along with approaches to tackle some of these challenges in practice. Even though experience has shown that algorithmic trading using advanced machine learning can be successful, the crucial issue remains that predictive patterns utilizing market inefficiencies quickly become void as soon as competing market participants use them too. The conclusion is that the crucial advantage is – and has always been – to know more and to be faster than competitors.
Our Speaker: Dr. Ulrich Bodenhofer
MSc (applied math, Johannes Kepler University, Linz, Austria, 1996)
PhD (applied math, Johannes Kepler University, Linz, Austria, 1998)
Since June 2018: Chief Artificial Intelligence Officer at QUOMATIC.AI (Linz, Austria)
How to Use Artificial Intelligence by Microsoft Product ManagerProduct School
The talk focused on the Fundamentals of Product Management, leveraging the speaker's personal experiences in the AI field. It covered core Product Manager topics such as managing customer needs, business goals & technology feasibility, the holy trinity of the Product Manager discipline, delve into data analyses, rapid experimentation, and execution, and finally, explored the challenges of customer privacy, bias, and inclusivity in AI products.
AI in the Enterprise: Past, Present & Future - StampedeCon AI Summit 2017StampedeCon
Artificial Intelligence has entered a renaissance thanks to rapid progress in domains as diverse as self-driving cars, intelligent assistants, and game play. Underlying this progress is Deep Learning – driven by significant improvements in Graphic Processing Units and computational models inspired by the human brain that excel at capturing structures hidden in massive complex datasets. These techniques have been pioneered at research universities and digital giants but mainstream enterprises are starting to apply them as open source tools and improved hardware become available. Learn how AI is impacting analytics today and in the future.
Learn how AI is affecting the enterprise including applications like fraud detection, mobile personalization, predicting failures for IoT and text analysis to improve call center interactions. We look at how practical examples of assessing the opportunity for AI, phased adoption, and lessons going from research, to prototype, to scaled production deployment.
This is the first lecture of the AI course offered by me at PES University, Bangalore. In this presentation we discuss the different definitions of AI, the notion of Intelligent Agents, distinguish an AI program from a complex program such as those that solve complex calculus problems (see the integration example) and look at the role of Machine Learning and Deep Learning in the context of AI. We also go over the course scope and logistics.
This is the first lecture on Applied Machine Learning. The course focuses on the emerging and modern aspects of this subject such as Deep Learning, Recurrent and Recursive Neural Networks (RNN), Long Short Term Memory (LSTM), Convolution Neural Networks (CNN), Hidden Markov Models (HMM). It deals with several application areas such as Natural Language Processing, Image Understanding etc. This presentation provides the landscape.
The Transformation of HPC: Simulation and Cognitive Methods in the Era of Big...inside-BigData.com
In this Deck from the 2018 Swiss HPC Conference, Dave Turek from IBM presents: The Transformation of HPC: Simulation and Cognitive Methods in the Era of Big Data.
"There is a shift underway where HPC is beginning to be addressed with novel techniques and technologies including cognitive and analytic approaches to HPC problems and the arrival of the first quantum systems. This talk will showcase how IBM is merging cognitive, analytics, and quantum with classic simulation and modeling to create a new path for computational science."
Watch the video: https://wp.me/p3RLHQ-ik7
Learn more: http://ibm.com
and
http://www.hpcadvisorycouncil.com/events/2018/swiss-workshop/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
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
Machine Learning Essentials Demystified part1 | Big Data DemystifiedOmid Vahdaty
Machine Learning Essentials Abstract:
Machine Learning (ML) is one of the hottest topics in the IT world today. But what is it really all about?
In this session we will talk about what ML actually is and in which cases it is useful.
We will talk about a few common algorithms for creating ML models and demonstrate their use with Python. We will also take a peek at Deep Learning (DL) and Artificial Neural Networks and explain how they work (without too much math) and demonstrate DL model with Python.
The target audience are developers, data engineers and DBAs that do not have prior experience with ML and want to know how it actually works.
Bitcoin Price Predictions and Machine Learning: Some New Ideas and Resultsintotheblock
We have continued our work experimenting with cutting edge machine learning models for price predictions in the crypto space and have learned a lot of new things.
During this session we covered:
- The challenges of crypto-asset prediction models
- New trends and ideas we are excited about
- Some techs to follow
- A brief note about DeFi and crypto-quant models
Find out more at https://app.intotheblock.com/
Deep Learning-Based Opinion Mining for Bitcoin Price Prediction with Joyesh ...Databricks
Sentiment values have been analyzed in relation to myriad commodities. Since its inception, Bitcoin (BTC) has been a very speculative cryptocurrency majorly influenced by sentiment on various communication platforms. Recent research has proven a close correlation between sentiments and cryptocurrency value. Social media platforms are a gold mine for opinionated data, which proves useful in trends based analysis. The advent of deep learning has helped enhance the feats in opinion mining from static metric based analysis to lexical analysis to context based mining, where in the latter, sentiments are purely based on context extracted using advanced Natural Language Processing techniques.
We focus on data collected using Twitter and Reddit channels, perform ETL using Apache Spark, and then mine opinions using deep learning based NLP techniques to functionally associate BTC historical price data with sentiments portrayed with time, and further effectively predict the future prices with acceptable accuracy.
Streaming data presents new challenges for statistics and machine learning on extremely large data sets. Tools such as Apache Storm, a stream processing framework, can power range of data analytics but lack advanced statistical capabilities. These slides are from the Apache.con talk, which discussed developing streaming algorithms with the flexibility of both Storm and R, a statistical programming language.
At the talk I dicsussed issues of why and how to use Storm and R to develop streaming algorithms; in particular I focused on:
• Streaming algorithms
• Online machine learning algorithms
• Use cases showing how to process hundreds of millions of events a day in (near) real time
See: https://apacheconna2015.sched.org/event/09f5a1cc372860b008bce09e15a034c4#.VUf7wxOUd5o
Similar to Capitalico / Chart Pattern Matching in Financial Trading Using RNN (20)
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
Strategies for Successful Data Migration Tools.pptxvarshanayak241
Data migration is a complex but essential task for organizations aiming to modernize their IT infrastructure and leverage new technologies. By understanding common challenges and implementing these strategies, businesses can achieve a successful migration with minimal disruption. Data Migration Tool like Ask On Data play a pivotal role in this journey, offering features that streamline the process, ensure data integrity, and maintain security. With the right approach and tools, organizations can turn the challenge of data migration into an opportunity for growth and innovation.
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTier1 app
Even though at surface level ‘java.lang.OutOfMemoryError’ appears as one single error; underlyingly there are 9 types of OutOfMemoryError. Each type of OutOfMemoryError has different causes, diagnosis approaches and solutions. This session equips you with the knowledge, tools, and techniques needed to troubleshoot and conquer OutOfMemoryError in all its forms, ensuring smoother, more efficient Java applications.
How Recreation Management Software Can Streamline Your Operations.pptxwottaspaceseo
Recreation management software streamlines operations by automating key tasks such as scheduling, registration, and payment processing, reducing manual workload and errors. It provides centralized management of facilities, classes, and events, ensuring efficient resource allocation and facility usage. The software offers user-friendly online portals for easy access to bookings and program information, enhancing customer experience. Real-time reporting and data analytics deliver insights into attendance and preferences, aiding in strategic decision-making. Additionally, effective communication tools keep participants and staff informed with timely updates. Overall, recreation management software enhances efficiency, improves service delivery, and boosts customer satisfaction.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
Your Digital Assistant.
Making complex approach simple. Straightforward process saves time. No more waiting to connect with people that matter to you. Safety first is not a cliché - Securely protect information in cloud storage to prevent any third party from accessing data.
Would you rather make your visitors feel burdened by making them wait? Or choose VizMan for a stress-free experience? VizMan is an automated visitor management system that works for any industries not limited to factories, societies, government institutes, and warehouses. A new age contactless way of logging information of visitors, employees, packages, and vehicles. VizMan is a digital logbook so it deters unnecessary use of paper or space since there is no requirement of bundles of registers that is left to collect dust in a corner of a room. Visitor’s essential details, helps in scheduling meetings for visitors and employees, and assists in supervising the attendance of the employees. With VizMan, visitors don’t need to wait for hours in long queues. VizMan handles visitors with the value they deserve because we know time is important to you.
Feasible Features
One Subscription, Four Modules – Admin, Employee, Receptionist, and Gatekeeper ensures confidentiality and prevents data from being manipulated
User Friendly – can be easily used on Android, iOS, and Web Interface
Multiple Accessibility – Log in through any device from any place at any time
One app for all industries – a Visitor Management System that works for any organisation.
Stress-free Sign-up
Visitor is registered and checked-in by the Receptionist
Host gets a notification, where they opt to Approve the meeting
Host notifies the Receptionist of the end of the meeting
Visitor is checked-out by the Receptionist
Host enters notes and remarks of the meeting
Customizable Components
Scheduling Meetings – Host can invite visitors for meetings and also approve, reject and reschedule meetings
Single/Bulk invites – Invitations can be sent individually to a visitor or collectively to many visitors
VIP Visitors – Additional security of data for VIP visitors to avoid misuse of information
Courier Management – Keeps a check on deliveries like commodities being delivered in and out of establishments
Alerts & Notifications – Get notified on SMS, email, and application
Parking Management – Manage availability of parking space
Individual log-in – Every user has their own log-in id
Visitor/Meeting Analytics – Evaluate notes and remarks of the meeting stored in the system
Visitor Management System is a secure and user friendly database manager that records, filters, tracks the visitors to your organization.
"Secure Your Premises with VizMan (VMS) – Get It Now"
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
Prosigns: Transforming Business with Tailored Technology Solutions
Capitalico / Chart Pattern Matching in Financial Trading Using RNN
1. 1
Make you trade ideas into AI.
Start free. On mobile.
Hitoshi Harada
CTO
hitoshi@alpacadb.com
http://alpaca.ai
Chart Pattern Matching in Financial Trading Using RNN
http://www.capitalico.com
4. • Fuzzy Pattern Recognition for everyone
• Generalization (no hand crafted features)
• Multiple time series (OHLC price + indicators)
• Time scale, value scale, distortion
James N.K. Liu *, Raymond W.M. Kwong : Automatic extraction and identification of chart patterns towards financial forecast, 2006
Problem And Needs - Fuzzy Pattern Recognition
Zhe Zhang, Jian Jiang, Xiaoyan Liu, Ricky Lau, Huaiqing Wang, Rui Zhang:
A Real Time Hybrid Pattern Matching Scheme for Stock Time Series, 2010
4
5. How To Solve The Problem? 5
SPEECH RECOGNITION WITH DEEP RECURRENT NEURAL NETWORKS,
Hinton, et al. 2013
Capitalico
“ah” “p” “down trend”
7. • Train by what you see & judge
• No programming nor
conditional setting, but
purely from charts like
traders do
• Multi-dimensional input
• Not only the single time-
series data of price
movement but also various
indicators altogether
Our Approach - Fuzzy Pattern Recognition without Programming 7
8. • Network
• Input:
• N-dim Fully Connected Layer
• LSTM Layer x 2 or 4 ( x250 units )
• Fully Connected Layer ( x250 units )
• Dropout
• Sigmoid
• Output:
• 1-dim confidence level
• Training
• Align with fixed number of candles
• Mean squared error for loss
• AdaDelta for optimizer
• BPTT through aligned length
• Data
• 1k+ samples collected by experts
• about hundred instances for each strategy
Input
LSTM
LSTM
Fully Connected
Output
Fully Connected
Time
Experiments Deep Learning Based Approach 8
Sigmoid
Input
LSTM
LSTM
Fully Connected
Output
Fully Connected
Sigmoid
Input
LSTM
LSTM
Fully Connected
Output
Fully Connected
Sigmoid
9. x-axis: time (1.0=entry point)
blue: training data / orange: testing data
Experiments Fitting Reasonably
y-axis:confidence
9
11. Dropout
• Dropout vs # of training samples
• Bigger Mini-Batches by looping samples
• Made it Adaptive depending on importance
11
dropout enabled (x: iteration count, y: loss)
dropout w/ bigger mini-batches (x: iteration count, y: loss)
12. Forget Gate Bias (Learning To Forget: Continual Prediction With Lstm, Felix Et Al.) 12
13. Trial And Error To Speed Up Training 13
• Dynamic Dropout
• Dynamic Batchsize
• Multi-GPU Training
• Other Frameworks like Keras
• GRU
• IRNN
• Lot more…
14. 14
• Previous studies have limitations to difficulty of feature crafting.
• LSTM based deep neural network fits well with individual patterns.
• LSTM-variant doesn’t make much difference, but forget-gate bias,
normalization, preprocessing, and modeling etc. matter
• Build better base model by pre-training
• Reinforcement Learning using profit and risk preference
• Visualize and rationalize LSTM decision making
• Generative Model
Conclusion & Future Work 14
16. • Ken-ichi Kainijo and Tetsuji Tanigawa:
Stock Price Pattern Recognition - A Recurrent Neural Network Approach -, 1990
• S Hochreiter, J Schmidhuber:
Long short-term memory, 1997
• FA Gers, J Schmidhuber, F Cummins:
Learning to forget: Continual prediction with LSTM, 2000
• James N.K. Liu *, Raymond W.M. Kwong:
Automatic extraction and identification of chart patterns towards financial forecast, 2006
• X Guo, X Liang, X Li:
A stock pattern recognition algorithm based on neural networks, 2007
• Z Zhang, J Jiang, X Liu, R Lau, H Wang:
A real time hybrid pattern matching scheme for stock time series, 2010
• A Graves, A Mohamed, G Hinton:
Speech recognition with deep recurrent neural networks, 2013
• A Graves, N Jaitly:
A Mohamed, Hybrid speech recognition with deep bidirectional LSTM, 2013
• Tara N. Sainath, Oriol Vinyals, Andrew Senior, Has¸im Sak:
CONVOLUTIONAL, LONG SHORT-TERM MEMORY, FULLY CONNECTED DEEP NEURAL NETWORKS
References 16
17. Need For Gpu And Distributed Computation 17
• Model Training
• Takes around 10 minutes on a single GPU core
• Requires 2GB of GPU RAM
• Backtesting
• Calculate various metrics over the historical data
• Livetesting
• Thousands of models need to monitor live candles and update the state of LSTM
18. Need For Distributed Computation 18
DB
Postgresql
Redis
etcd
Load
Balancer
WEB
Flask
tesla k80
WORKER
Live
Market Watch
Market Data
Historical
Real time
Queue
Celery
Trading
Algos =
~10MB
x1-10K