These slides accompanied a demo of Deeplearning4j, while the meetup explored distributed clustering and various deep learning explanations.
http://www.meetup.com/SF-Neural-Network-Afficianados-Discussion-Group/events/182645252/
Deep-learning is useful in detecting anomalies like fraud, spam and money laundering; identifying similarities to augment search and text analytics; predicting customer lifetime value and churn; recognizing faces and voices.
Deeplearning4j is an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. It includes a distributed deep-learning framework and a normal deep-learning framework; i.e. it runs on a single thread as well. Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure. The distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models.
The framework's neural nets include restricted Boltzmann machines, deep-belief networks, deep autoencoders, convolutional nets and recursive neural tensor networks.
San Francisco Hacker News - Machine Learning for HackersAdam Gibson
This was for the san francisco hacker news meetup in february at engineyard.
This was intended as a basic intro to machine learning for people who wanted to step in to the field.
Video coming shortly.
These slides accompanied a demo of Deeplearning4j at the SF Data Mining Meetup hosted by Trulia.
http://www.meetup.com/Data-Mining/events/212445872/
Deep-learning is useful in detecting identifying similarities to augment search and text analytics; predicting customer lifetime value and churn; and recognizing faces and voices.
Deeplearning4j is an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. It includes a distributed deep-learning framework and a normal deep-learning framework; i.e. it runs on a single thread as well. Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure. The distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models.
The framework's neural nets include restricted Boltzmann machines, deep-belief networks, deep autoencoders, convolutional nets and recursive neural tensor networks.
Finally, Deeplearning4j integrates with GPUs. A stable version was released in October.
Handwritten Recognition using Deep Learning with RPoo Kuan Hoong
R User Group Malaysia Meet Up - Handwritten Recognition using Deep Learning with R
Source code available at: https://github.com/kuanhoong/myRUG_DeepLearning
San Francisco Hacker News - Machine Learning for HackersAdam Gibson
This was for the san francisco hacker news meetup in february at engineyard.
This was intended as a basic intro to machine learning for people who wanted to step in to the field.
Video coming shortly.
These slides accompanied a demo of Deeplearning4j at the SF Data Mining Meetup hosted by Trulia.
http://www.meetup.com/Data-Mining/events/212445872/
Deep-learning is useful in detecting identifying similarities to augment search and text analytics; predicting customer lifetime value and churn; and recognizing faces and voices.
Deeplearning4j is an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. It includes a distributed deep-learning framework and a normal deep-learning framework; i.e. it runs on a single thread as well. Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure. The distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models.
The framework's neural nets include restricted Boltzmann machines, deep-belief networks, deep autoencoders, convolutional nets and recursive neural tensor networks.
Finally, Deeplearning4j integrates with GPUs. A stable version was released in October.
Handwritten Recognition using Deep Learning with RPoo Kuan Hoong
R User Group Malaysia Meet Up - Handwritten Recognition using Deep Learning with R
Source code available at: https://github.com/kuanhoong/myRUG_DeepLearning
From Conventional Machine Learning to Deep Learning and Beyond.pptxChun-Hao Chang
In this slide, Deep Learning are compared with Conventional Learning and the strength of DNN models will be explained.
The target audience are people who have the knowledge of Machine Learning or Data Mining but not familiar with Deep Learning.
We will use 7 emotions namely - We have used 7 emotions namely - 'Angry', 'Disgust'濫, 'Fear', 'Happy', 'Neutral', 'Sad'☹️, 'Surprise' to train and test our algorithm using Convolution Neural Networks.
The presentation briefly answers the questions:
1. What is Machine Learning?
2. Ideas behind Neural Networks?
3. What is Deep Learning? How different is it from NN?
4. Practical examples of applications.

For more information:
https://www.quora.com/How-does-deep-learning-work-and-how-is-it-different-from-normal-neural-networks-and-or-SVM
http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma
https://www.youtube.com/watch?v=n1ViNeWhC24 - presentation by Ng
http://techtalks.tv/talks/deep-learning/58122/ - deep learning tutorial and slides - http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf
Deep learning for NLP - http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial
papers: http://www.cs.toronto.edu/~hinton/science.pdf
http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_ErhanCBV10.pdf
http://arxiv.org/pdf/1206.5538v3.pdf
http://arxiv.org/pdf/1404.7828v4.pdf
More recommendations - https://www.quora.com/What-are-the-best-resources-to-learn-about-deep-learning
"Mainstream access to deep learning technology will greatly impact most industries over the next three to five years."
So what exactly is deep learning? How does it work? And most importantly, why should you even care?
Deep learning is used in the research community and in industry to help solve many big data problems such as computer vision, speech recognition, and natural language processing.
Practical examples include:
-Vehicle, pedestrian and landmark identification for driver assistance
-Image recognition
-Speech recognition and translation
-Natural language processing
-Life sciences
-What You Will Learn
-Understand the intuition behind Artificial Neural Networks
-Apply Artificial Neural Networks in practice
-Understand the intuition behind Convolutional Neural Networks
-Apply Convolutional Neural Networks in practice
-Understand the intuition behind Recurrent Neural Networks
-Apply Recurrent Neural Networks in practice
-Understand the intuition behind Self-Organizing Maps
-Apply Self-Organizing Maps in practice
-Understand the intuition behind Boltzmann Machines
-Apply Boltzmann Machines in practice
-Understand the intuition behind AutoEncoders
-Apply AutoEncoders in practice
“Automatically learning multiple levels of representations of the underlying distribution of the data to be modelled”
Deep learning algorithms have shown superior learning and classification performance.
In areas such as transfer learning, speech and handwritten character recognition, face recognition among others.
(I have referred many articles and experimental results provided by Stanford University)
Deep Learning Projects - Anomaly Detection Using Deep LearningDezyreAcademy
Deep Learning Projects- Learn how to use state-of-the-art deep learning methods and autoencoders for anomaly detection.
Check out other interesting deep learning project ideas here - https://www.dezyre.com/projects/data-science-projects/deep-learning-projects
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.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
This is a deep learning presentation based on Deep Neural Network. It reviews the deep learning concept, related works and specific application areas.It describes a use case scenario of deep learning and highlights the current trends and research issues of deep learning
From Conventional Machine Learning to Deep Learning and Beyond.pptxChun-Hao Chang
In this slide, Deep Learning are compared with Conventional Learning and the strength of DNN models will be explained.
The target audience are people who have the knowledge of Machine Learning or Data Mining but not familiar with Deep Learning.
We will use 7 emotions namely - We have used 7 emotions namely - 'Angry', 'Disgust'濫, 'Fear', 'Happy', 'Neutral', 'Sad'☹️, 'Surprise' to train and test our algorithm using Convolution Neural Networks.
The presentation briefly answers the questions:
1. What is Machine Learning?
2. Ideas behind Neural Networks?
3. What is Deep Learning? How different is it from NN?
4. Practical examples of applications.

For more information:
https://www.quora.com/How-does-deep-learning-work-and-how-is-it-different-from-normal-neural-networks-and-or-SVM
http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma
https://www.youtube.com/watch?v=n1ViNeWhC24 - presentation by Ng
http://techtalks.tv/talks/deep-learning/58122/ - deep learning tutorial and slides - http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf
Deep learning for NLP - http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial
papers: http://www.cs.toronto.edu/~hinton/science.pdf
http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_ErhanCBV10.pdf
http://arxiv.org/pdf/1206.5538v3.pdf
http://arxiv.org/pdf/1404.7828v4.pdf
More recommendations - https://www.quora.com/What-are-the-best-resources-to-learn-about-deep-learning
"Mainstream access to deep learning technology will greatly impact most industries over the next three to five years."
So what exactly is deep learning? How does it work? And most importantly, why should you even care?
Deep learning is used in the research community and in industry to help solve many big data problems such as computer vision, speech recognition, and natural language processing.
Practical examples include:
-Vehicle, pedestrian and landmark identification for driver assistance
-Image recognition
-Speech recognition and translation
-Natural language processing
-Life sciences
-What You Will Learn
-Understand the intuition behind Artificial Neural Networks
-Apply Artificial Neural Networks in practice
-Understand the intuition behind Convolutional Neural Networks
-Apply Convolutional Neural Networks in practice
-Understand the intuition behind Recurrent Neural Networks
-Apply Recurrent Neural Networks in practice
-Understand the intuition behind Self-Organizing Maps
-Apply Self-Organizing Maps in practice
-Understand the intuition behind Boltzmann Machines
-Apply Boltzmann Machines in practice
-Understand the intuition behind AutoEncoders
-Apply AutoEncoders in practice
“Automatically learning multiple levels of representations of the underlying distribution of the data to be modelled”
Deep learning algorithms have shown superior learning and classification performance.
In areas such as transfer learning, speech and handwritten character recognition, face recognition among others.
(I have referred many articles and experimental results provided by Stanford University)
Deep Learning Projects - Anomaly Detection Using Deep LearningDezyreAcademy
Deep Learning Projects- Learn how to use state-of-the-art deep learning methods and autoencoders for anomaly detection.
Check out other interesting deep learning project ideas here - https://www.dezyre.com/projects/data-science-projects/deep-learning-projects
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.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
This is a deep learning presentation based on Deep Neural Network. It reviews the deep learning concept, related works and specific application areas.It describes a use case scenario of deep learning and highlights the current trends and research issues of deep learning
DeepLearning4J and Spark: Successes and Challenges - François Garillotsparktc
At the recent sold-out Spark & Machine Learning Meetup in Brussels, François Garillot of Skymind delivered a lightning talk called DeepLearning4J and Spark: Successes and Challenges.
Specifically, François offered a tour of the DeepLearning4J architecture intermingled with applications. He went over the main blocks of this deep learning solution for the JVM that includes GPU acceleration, a custom n-dimensional array library, a parallelized data-loading swiss army tool, deep learning and reinforcement learning libraries — all with an easy-access interface.
Along the way, he pointed out the strategic points of parallelization of computation across machines and gave insight on where Spark helps — and where it doesn't.
DeepLearning4J and Spark: Successes and Challenges - François Garillotsparktc
At the recent sold-out Spark & Machine Learning Meetup in Brussels, François Garillot of Skymind delivered a lightning talk called DeepLearning4J and Spark: Successes and Challenges.
Specifically, François offered a tour of the DeepLearning4J architecture intermingled with applications. He went over the main blocks of this deep learning solution for the JVM that includes GPU acceleration, a custom n-dimensional array library, a parallelized data-loading swiss army tool, deep learning and reinforcement learning libraries — all with an easy-access interface.
Along the way, he pointed out the strategic points of parallelization of computation across machines and gave insight on where Spark helps — and where it doesn't.
David Kale and Ruben Fizsel from Skymind talk about deep learning for the JVM and enterprise using deeplearning4j (DL4J). Deep learning (nouveau neural nets) have sparked a renaissance in empirical machine learning with breakthroughs in computer vision, speech recognition, and natural language processing. However, many popular deep learning frameworks are targeted to researchers and poorly suited to enterprise settings that use Java-centric big data ecosystems. DL4J bridges the gap, bringing high performance numerical linear algebra libraries and state-of-the-art deep learning functionality to the JVM.
Advanced Spark and Tensorflow Meetup - London - Nov 15, 2016 - Deploy Spark M...Chris Fregly
YouTube Video: https://www.youtube.com/watch?v=RnnweVC7wFc
In this completely 100% Open Source demo-based talk, Chris Fregly from PipelineIO will be addressing an area of machine learning and artificial intelligence that is often overlooked: the real-time, end-user-facing "serving” layer in a hybrid-cloud and on-premise deployment environment using Jupyter, NetflixOSS, Docker, and Kubernetes.
Serving models to end-users in real-time in a highly-scalable, fault-tolerant manner requires not only an understanding of machine learning fundamentals, but also an understanding of distributed systems and scalable microservices.
Chris will combine his work experience from both Databricks and Netflix to present a 100% open source, real-world, hybrid-cloud, on-premise, and NetflixOSS-based production-ready environment to serve your notebook-based Spark ML and TensorFlow AI models with highly-scalable and highly-available robustness.
Speaker Bio
Chris Fregly is a Research Scientist at PipelineIO - a Streaming Analytics and Machine Learning Startup in San Francisco.
Chris is an Apache Spark Contributor, Netflix Open Source Committer, Founder of the Global Advanced Spark and TensorFlow Meetup, and Author of the upcoming book, Advanced Spark, and Creator of the upcoming O'Reilly video series, Scaling TensorFlow Distributed in Production.
Previously, Chris was an engineer at Databricks and Netflix - as well as a Founding Member of the IBM Spark Technology Center in San Francisco.
Neural Networks, Spark MLlib, Deep LearningAsim Jalis
What are neural networks? How to use the neural networks algorithm in Apache Spark MLlib? What is Deep Learning? Presented at Data Science Meetup at Galvanize on 2/17/2016.
For code see IPython/Jupyter/Toree notebook at http://nbviewer.jupyter.org/gist/asimjalis/4f911882a1ab963859ce
Suneel Marthi - Deep Learning with Apache Flink and DL4JFlink Forward
http://flink-forward.org/kb_sessions/deep-learning-with-apache-flink-and-dl4j/
Deep Learning has become very popular over the last few years in areas such as Image Recognition, Fraud Detection, Machine Translation etc. Deep Learning has proved to be very useful in handling unstructured data and extracting value from them. A big challenge with having to build deep learning models was the high cost of training them. With the recent advent of distributed frameworks like Apache Flink, Apache Spark etc.. it’s faster to train Deep Learning models in parallel on modern platform architecture. In this talk, we’ll be showing how to use Apache Flink Streaming with the open source Deep Learning framework, DeepLearning4j to perform large scale deep learning model training. We will show a demo of a Recurrent Neural Net that is trained for language modeling and have it generate text.
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.
Cyberbullying, or humiliating and slandering people through Internet, has been recently noticed as a serious social problem disturbing mental health of Internet users. In Japan, to deal with the problem, voluntary members of Parent-Teacher Association (PTA) manually read through the Web to spot cyberbullying entries. To help PTA members in their uphill task we propose a novel method for automatic detection of malicious contents on the Internet. The method is based on a combinatorial approach resembling brute force search algorithms with application to language classification. The method extracts sophisticated patterns from sentences and uses them in classification. We tested the method on actual data containing cyberbullying provided by Human Rights Center. The results show our method outperformed previous methods. It is also more efficient as it requires minimal human effort.
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “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? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, 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).
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.
Deep learning algorithms have drawn the attention of researchers working in the field of computer vision, speech
recognition, malware detection, pattern recognition and natural language processing. In this paper, we present an overview of
deep learning techniques like Convolutional neural network, deep belief network, Autoencoder, Restricted Boltzmann machine
and recurrent neural network. With this, current work of deep learning algorithms on malware detection is shown with the
help of literature survey. Suggestions for future research are given with full justification. We also showed the experimental
analysis in order to show the importance of deep learning techniques.
A SURVEY ON DEEP LEARNING METHOD USED FOR CHARACTER RECOGNITIONIJCIRAS Journal
The field of Artificial Intelligence is very fashionable today, especially neural networks that work well in various areas such as speech recognition and natural language processing. This Research Article briefly describes how deep learning models work and what different techniques are used in text recognition. It also describes the great progress that has been made in the field of medicine, the analysis of forensic documents, the recognition of license plates, banking, health and the legal industry. The recognition of handwritten characters is one of the research areas in the field of artificial intelligence. The individual character recognition has a higher recognition accuracy than the complete word recognition. The new method for categorizing Freeman strings is presented using four connectivity events and eight connectivity events with a deep learning approach.
This presentation educates you about Neural Network, How artificial neural networks work?, How neural networks learn?, Types of Neural Networks, Advantages and Disadvantages of artificial neural networks and Applications of artificial neural networks.
For more topics stay tuned with Learnbay.
MachinaFiesta: A Vision into Machine Learning 🚀GDSCNiT
🕵️♂️ Embark on an exhilarating journey into the realm of Machine learning and Generative AI with MachinaFiesta! 🚀. Join us for MachinaFiesta, a two-hour event exploring the fascinating world of machine learning and generative AI where you can Vision, Innovate and learn new technologies.
Slide contets:
🎤 Brief introduction to the agenda and speakers of the event
🌐 Get to know the importance and future prospects of machine learning
🧠 Interactive session on core machine learning concepts
🚀 Exploration of cutting-edge generative AI advancements
🤖 Introduction to Gemini, the open-source factual language model
🤔Discussion on Gemini's capabilities and potential applications in research and development
This article aims to classify texts and predict the categories of occurrences, through the study of Artificial Intelligence models, using Machine Learning and Deep Learning for the classification of texts and analysis of predictions, suggesting the best option with the smallest error.
The solution was designed to be implemented in two stages: Machine Learning and Application, according to the diagram below from the Data Science Academy.
Convolutional Neural Networks square measure terribly kind of like n.pdfpoddaranand1
Convolutional Neural Networks square measure terribly kind of like normal Neural Networks
from the previous chapter: they\'re created of neurons that have learnable weights and biases.
every vegetative cell receives some inputs, performs a inner product and optionally follows it
with a non-linearity. the total network still expresses one differentiable score function: from the
raw image pixels on one finish to category scores at the opposite. and that they still have a loss
operate (e.g. SVM/Softmax) on the last (fully-connected) layer and every one the tips/tricks we
have a tendency to developed for learning regular Neural Networks still apply.
So what will change? ConvNet architectures create the express assumption that the inputs square
measure pictures, that permits United States to cypher bound properties into the design. These
then create the forward operate additional economical to implement and immensely cut back the
quantity of parameters within the network.
In machine learning, a deep belief network (DBN) may be a generative graphical model, or or
else a sort of deep neural network, composed of multiple layers of latent variables (\"hidden
units\"), with connections between the layers however not between units at intervals every
layer.[1]
When trained on a group of examples in associate degree unsupervised manner, a DBN will learn
to probabilistically reconstruct its inputs. The layers then act as feature detectors on inputs. when
this learning step, a DBN are often more trained in a very supervised thanks to perform
classification.
DBNs are often viewed as a composition of straightforward, unsupervised networks like
restricted Ludwig Boltzmann machines (RBMs)[1] or autoencoders,[3] wherever every sub-
network\'s hidden layer is the visible layer for future. This conjointly results in a quick, layer-by-
layer unsupervised coaching procedure, wherever contrastive divergence is applied to every sub-
network successively, ranging from the \"lowest\" try of layers (the lowest visible layer being a
coaching set).
The observation, attributable to Yee-Whye Teh, Geoffrey Hinton\'s student, that DBNs are often
trained avariciously, one layer at a time, LED to at least one of the primary effective deep
learning algorithms.
A restricted physicist machine (RBM) may be a generative random artificial neural network that
may learn a likelihood distribution over its set of inputs.
RBMs were at the start fictitious beneath the name reed organ by Paul Smolensky in 1986, and
rose to prominence when Geoffrey Hinton and collaborators fictitious quick learning algorithms
for them within the mid-2000s. RBMs have found applications in spatiality reduction,
classification, cooperative filtering, feature learning and topic modelling. they will be trained in
either supervised or unsupervised ways that, counting on the task.
As their name implies, RBMs ar a variant of physicist machines, with the restriction that their
neurons should kind a bipartite graph: a .
Decision tree knowledge discovery through neural Networks
structure of decision tree and neural networks.
how they work?
Models
working
knowledge discovery
clustering
In this presentation, we walk through what is Deep Learning in General, we see the anatomy of a typical Deep Learning Neural Network, how is it trained, how do we get the inference, optimisation of parameters, and regularising it. Then we dive deep into the Face Recognition technology, different paradigms and aspects of it. How do we train it, how are the features extracted, etc. We talk about the security as well.
Deploying signature verification with deep learningAdam Gibson
Presentation covered building a signature verification system and deploying it to production. This includes resources usage as well as how the model was picked.
Meetup held in Tokyo with Deep learning Otemachi.
Self driving computers active learning workflows with human interpretable ve...Adam Gibson
Human in the loop learning workflows leveraging deep learning to group and cluster data. Also, techniques for accounting for machine learning failures.
Anomaly Detection and Automatic Labeling with Deep LearningAdam Gibson
Adam Gibson demonstrates how to use variational autoencoders to automatically label time series location data. You'll explore the challenge of imbalanced classes and anomaly detection, learn how to leverage deep learning for automatically labeling (and the pitfalls of this), and discover how you can deploy these techniques in your organization.
Recent presentation on deeplearning4j's new features as well as some underused features of the AI framework like arbiter,datavec's transform process and libnd4j.
This talk was on deep learning use cases outside of computer vision. It also covered larger scale patterns of what good deep learning use cases typically look like. We end up on an explanation of anomaly detection and various kinds of anomaly use cases.
Distributed deep rl on spark strata singaporeAdam Gibson
This talk briefly covers deep reinforcemeent learning on spark and the benefits of using large scale commodity compute with gpus for ease of running simulations as well as distributed training for use cases that aren't games such as network intrusion and risk. This talk also briefly mentions rl4j and our work with openai gym.
Deep learning in production with the bestAdam Gibson
Getting deep learning adopted at your company. The current landscape of academia vs industry. Presentation at AI with the best (online conference):
http://ai.withthebest.com/
Strata Beijing - Deep Learning in Production on SparkAdam Gibson
Recent talk at strata beijing - half english half chinese covering use cases of deep learning, deep learning in production and the different components of deeplearning4j.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
2. Deep Learning is a subset of Machine Learning
Machine Learning is a subset of Artificial
Intelligence
AI is nothing more than a collection
of algorithms that repeatedly optimize
themselves.
Deep learning is pattern recognition, a way for
machines to classify what they perceive.
DL is a subset of AI
3. Deep learning algorithms are called neural
nets. They are mathematical models.
They mirror the neurons of the human brain.
In the brain, sets of neurons learn to recognize
certain patterns or phenomena, like faces,
birdcalls or grammatical sequences.
These models have names like:
Restricted Boltzmann Machine
Deep-Belief Net
Convolutional Net
Stacked Denoising Autoencoder
Recursive Neural Tensor Network
Deep learning’s algorithms
4. Deep learning understands numbers, so
anything that can be converted to numbers is
fair game:
Digital media. Anything you can see or here.
DL can analyze sights, sounds and text.
Sensor output. DL can work with data about
temperature, pressure, motion and chemical
composition.
Time-series data. DL handles prices and their
movement over time; e.g. the stock market, real
estate, weather and economic indicators.
What DL can handle
5. Recommendation engines: DL can identify
patterns of human behavior and predict what
you will want to buy.
Anomaly detection: DL can identify signals that
indicate bad outcomes. It can point out fraud in
e-commerce; tumors in X-rays; and loan
applicants likely to default.
Signal processing: Deep learning can tell you
what to expect, whether its customer lifetime
value, how much inventory to stock, or
whether the market on the verge of a flash
crash. It has predictive capacity.
What can you do with it?
6. Faces can be represented by a collection of
images.
Those images have persistent patterns of pixels.
Those pixel patterns are known as features; i.e.
highly granular facial features.
Deep-learning nets learn to identify features in
data, and use them to classify faces as faces and
to label them by name; e.g. John or Sarah.
Nets train themselves by reconstructing faces
from features again and again, and measuring
their work against a benchmark.
Facial recognition
8. Deep learning networks learn from the data
you feed them.
Initial data is known as the training set, and
you know what it’s made of.
The net learns the faces of the training set by
trying to reconstruct them, again and again.
Reconstruction is a process of finding which
facial features are indicative of larger forms.
When a net can rebuild the training set, it is
ready to work with unsupervised data.
How did it do that?
9. Nets measure the difference between what they
produce and a benchmark you set.
They try to minimize that difference.
They do that by altering their own parameters
– the way they treat the data – and testing how
that affects their own results.
This test is known as a “loss function.”
No really, how did it do that?
11. Facebook uses facial recognition to make itself
stickier, and to know more about us.
Government agencies use facial recognition to
secure national borders.
Video game makers use facial recognition to
construct more realistic worlds.
Stores use it to identify customers and track
behavior.
What are faces for?
12. Sentiment analysis is a form of Natural-
Language Processing.
With it, software classifies the affective content
of sentences, their emotional tone, bias and
intensity.
Are they positive or negative about the subject
in question?
This can be very useful in ranking movies,
books, media and just about anything humans
consume.
Including politicians.
Sentiment Analysis & Text
13. By reading sentiment, you read many things.
Corporations can measure customer
satisfaction.
Governments can monitor popular unrest.
Event organizers can track audience
engagement.
Employers can measure job applicant fit.
Celebrities can gauge fame and track scandal.
Who cares what they say?
14. Recurrent neural net
Restricted Boltzmann machine (RBM)
Deep-belief network: A stack of RBMs
Deep Autoencoder: 2 DBNs
Denoising Autoencoder (yay, noise!)
Convolutional net (ConvNet)
Recursive neural tensor network (RNTN)
A Neural Nets Taxonomy
15. Two layers of neuron-like nodes.
The first layer is the visible, or input, layer
The second is the hidden layer, which identifies
features in the input
This simple network is symmetrically
connected.
“Restricted” means there are no visible-visible
or hidden-hidden connections; i.e. all
connections happen *between* layers.
Restricted Boltzmann
Machine (RBMs)
16. A deep-belief net is a stack of RBMs.
Each RBM’s hidden layer becomes the next
RBM’s visible/input layer.
In this manner, a DBN learns more and more
complex features
A machine vision example: 1) Pixels are input;
2) H1 learns an edge or line; 3) H2 learns a
corner or set of lines; 4) H3 learns two groups
of lines forming an object, maybe a face.
The final layer of a DBN classifies feature
groups. It groups them in buckets: e.g. sunset,
elephant, flower.
Deep-belief net (DBN)
17. A deep autoencoder consists of two DBNs.
The first DBN *encodes* the data into a vector
of 10-30 numbers. This is pre-training.
The second DBN decodes the data into its
original state.
Backprop happens solely on the second DBN
This is the fine-tuning stage and it’s carried out
with reconstruction entropy.
Deep autoencoders will reduce any document
or image to a highly compact vector.
Those vectors are useful in search, QA and
information retrieval.
Deep Autoencoder
18. Autoencoders are useful for dimensionality
reduction.
The risk they run is learning the identity
function of the input.
Dropout is one way to address that risk.
Noise is another.
Noise is the stochastic, or random, corruption
of the input.
The machine then learns features despite the
noise. It “denoises” the input.
A stacked denoising encoder is exactly what
you’d think.
Good for unsupervised pre-training, which
initializes the weights.
Denoising Autoencoder
19. ConvNets are a type of RBM.
The difference is they’re asymmetric.
In an RBM, each node in the visible layer
connects to each node in the hidden layer.
In a ConvNet, each node connects to the node
straight ahead of it, and to the two others
immediately to the right and left of it.
This means that ConvNets learn data like
images in patches.
Each piece learned is then woven together in
the whole.
Convolutional Net
20. Recursive nets are top-down, hierarchical nets
rather than feed-forward like DBNs.
RNTNs handle sequence-based classification,
windows of several events, entire scenes rather
than images.
The features themselves are vectors.
A tensor is a multi-dimensional matrix, or
multiple matrices of the same size.
Recursive Neural Tensor Net