Rajat Monga at AI Frontiers: Deep Learning with TensorFlowAI Frontiers
In this talk at AI Frontiers Conference, Rajat Monga shares about TensorFlow that has enabled cutting-edge machine learning research at the top AI labs in the world. At the same time it has made the technology accessible to a large audience leading to some amazing uses. TensorFlow is used for classification, recommendation, text parsing, sentiment analysis and more. This talk goes over the design that makes it fast, flexible, and easy to use, and describe how we continue to make it better.
Alex Smola at AI Frontiers: Scalable Deep Learning Using MXNetAI Frontiers
In this talk at AI Frontiers Conference, Alex Smola gives a brief overview over the features used to scale deep learning using MXNet. It relies on a mix between declarative and imperative programming to achieve efficiency while also allowing for significant flexibility for the user. It relies on a distributed (key, value) store for synchronization between GPUs and between machines. It also relies on the separation between a highly efficient execution engine and language bindings to achieve a high degree of flexibility between different languages while offering a native feel in each of them. Alex also briefly discusses how Amazon AWS can help deploy deep learning models and outline steps on our future roadmap.
Rajat Monga at AI Frontiers: Deep Learning with TensorFlowAI Frontiers
In this talk at AI Frontiers Conference, Rajat Monga shares about TensorFlow that has enabled cutting-edge machine learning research at the top AI labs in the world. At the same time it has made the technology accessible to a large audience leading to some amazing uses. TensorFlow is used for classification, recommendation, text parsing, sentiment analysis and more. This talk goes over the design that makes it fast, flexible, and easy to use, and describe how we continue to make it better.
Alex Smola at AI Frontiers: Scalable Deep Learning Using MXNetAI Frontiers
In this talk at AI Frontiers Conference, Alex Smola gives a brief overview over the features used to scale deep learning using MXNet. It relies on a mix between declarative and imperative programming to achieve efficiency while also allowing for significant flexibility for the user. It relies on a distributed (key, value) store for synchronization between GPUs and between machines. It also relies on the separation between a highly efficient execution engine and language bindings to achieve a high degree of flexibility between different languages while offering a native feel in each of them. Alex also briefly discusses how Amazon AWS can help deploy deep learning models and outline steps on our future roadmap.
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. During this workshop, members of the Amazon Machine Learning team will provide a short background on Deep Learning focusing on relevant application domains and an introduction to using the powerful and scalable Deep Learning framework, MXNet. At the end of this tutorial you’ll gain hands on experience targeting a variety of applications including computer vision and recommendation engines as well as exposure to how to use preconfigured Deep Learning AMIs and CloudFormation Templates to help speed your development.
An introduction to Google's AI Engine, look deeper into Artificial Networks and Machine Learning. Appreciate how our simplest neural network be codified and be used to data analytics.
Deep Learning: Introduction & Chapter 5 Machine Learning BasicsJason Tsai
Given lecture for Deep Learning 101 study group with Frank Wu on Dec. 9th, 2016.
Reference: https://www.deeplearningbook.org/
Initiated by Taiwan AI Group (https://www.facebook.com/groups/Taiwan.AI.Group/)
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. During this workshop, members of the Amazon Machine Learning team will provide a short background on Deep Learning focusing on relevant application domains and an introduction to using the powerful and scalable Deep Learning framework, MXNet. At the end of this tutorial you’ll gain hands on experience targeting a variety of applications including computer vision and recommendation engines as well as exposure to how to use preconfigured Deep Learning AMIs and CloudFormation Templates to help speed your development.
An introduction to Google's AI Engine, look deeper into Artificial Networks and Machine Learning. Appreciate how our simplest neural network be codified and be used to data analytics.
Deep Learning: Introduction & Chapter 5 Machine Learning BasicsJason Tsai
Given lecture for Deep Learning 101 study group with Frank Wu on Dec. 9th, 2016.
Reference: https://www.deeplearningbook.org/
Initiated by Taiwan AI Group (https://www.facebook.com/groups/Taiwan.AI.Group/)
Advanced Flow Concepts Every Developer Should KnowPeter Caitens
Tim Combridge from Sensible Giraffe and Salesforce Ben presents some important tips that all developers should know when dealing with Flows in Salesforce.
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.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
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.
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.
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.
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
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.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
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.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
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!
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.
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.
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
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.
2. What is machine learning?
if(speed<4){
status=WALKING;
} else if(speed<12){
status=RUNNING;
} else {
status=BIKING;
}
3. Everyday we are using deep-learning
• Apple’s iPhone X uses deep learning for facial
recognition
• Smart Speakers – Amazon Echo, Google Home
• Traffic Predictions – Google Maps
• Videos Surveillance
• Social Media Services – Friend Recommendation
• Email Spam Filters
• Virtual Assistants – Customer Support
• Online Fraud Detection
4. What is Deep Learning?
• Deep Learning
• Supervised, semi-supervised and unsupervised methods.
• Deep Neural Networks
• Neural Networks more than 2 layers
• Sophisticated mathematical modeling
• To extract feature-set automatically
Deep NN... Deep NN...
5. What is
TensorFlow?
• Open source software library by Google
• Originally created for tasks with heavy numerical
computations
• Main Application: Machine Learning & Deep Neural Networks
• C/C++ Backend
6. Why
TensorFlow?
• Based upon Dataflow Graphs
• Faster Compile Times
• Supports CPUs, GPUs and distributed processing
7. What is Tensor?
• Tensor is multi-dimensional
array
• It can be
• zero dimensional as
scalar values
• One dimensional as line
or vector
• Multi-dimensional as
Matrix and so-on
8. Why Deep Learning with TensorFlow?
EXTENSIVE BUILT-IN SUPPORT
FOR DEEP LEARNING
MATHEMATICAL FUNCTIONS
FOR NEURAL NETWORKS
AUTO-DIFFERENTIATION AND
FIRST-RATE OPTIMIZERS
9. 5-step model life cycle
• Define the model.
• Compile the model.
• Fit the model.
• Evaluate the model.
• Make predictions.
...
# define the model
model = ...
...
# compile the model
model.compile(optimizer='sgd', loss='mse')
...
# fit the model
model.fit(X, y, epochs=100, batch_size=32)
...
# evaluate the model
loss = model.evaluate(X, y, verbose=0)
...
# make a prediction
yhat = model.predict(X)
11. Artificial Neural Network (ANN)
• A single perceptron (or neuron)
can be imagined as a Logistic
Regression.
• Artificial Neural Network, or
ANN, is a group of multiple
perceptrons/ neurons at each
layer.
• ANN is also known as a Feed-
Forward Neural.
network because inputs are
processed only in the forward
direction
13. Convolution Neural Network (CNN)
• Convolutional neural networks apply a filter
to an input to create a feature map that
summarizes the presence of detected
features in the input.
• Kernels/Filters are used to extract the
relevant features from the input using the
convolution operation.
14. A convolutional layer
A filter
A CNN is a neural network with some convolutional layers
(and some other layers). A convolutional layer has a number
of filters that does convolutional operation.
15. Convolution
1 0 0 0 0 1
0 1 0 0 1 0
0 0 1 1 0 0
1 0 0 0 1 0
0 1 0 0 1 0
0 0 1 0 1 0
6 x 6 image
1 -1 -1
-1 1 -1
-1 -1 1
Filter 1
-1 1 -1
-1 1 -1
-1 1 -1
Filter 2
…
…
These are the network
parameters to be learned.
Each filter detects a
small pattern (3 x 3).
26. Pooling
• Subsampling pixels will not change the object
Subsampling
bird
bird
We can subsample the pixels to make image smaller
fewer parameters to characterize the image
27. A CNN compresses a fully connected network
in following ways:
• Reducing number of connections
• Shared weights on the edges
• Max pooling further reduces the complexity
28. Max Pooling
1 0 0 0 0 1
0 1 0 0 1 0
0 0 1 1 0 0
1 0 0 0 1 0
0 1 0 0 1 0
0 0 1 0 1 0
6 x 6 image
3 0
13
-1 1
30
2 x 2 image
Each filter
is a channel
New image
but smaller
Conv
Max
Pooling
29. The whole CNN
Convolution
Max Pooling
Convolution
Max Pooling
Can
repeat
many
times
A new image
The number of channels
is the number of filters
Smaller than the original
image
3 0
13
-1 1
30
30. The whole CNN
Fully Connected
Feedforward network
cat dog ……
Convolution
Max Pooling
Convolution
Max Pooling
Flattened
A new image
A new image
32. CNN in TensorFlow using Keras
Convolution
Max Pooling
Convolution
Max Pooling
input
1 -1 -1
-1 1 -1
-1 -1 1
-1 1 -1
-1 1 -1
-1 1 -1
There are
25 3x3
filters.
…
…
Input_shape = ( 28 , 28 , 1)
1: black/white, 3: RGB28 x 28 pixels
3 -1
-3 1
3
33. CNN in Keras
Convolution
Max Pooling
Convolution
Max Pooling
Input
1 x 28 x 28
25 x 26 x 26
25 x 13 x 13
50 x 11 x 11
50 x 5 x 5
How many parameters for
each filter?
How many parameters
for each filter?
9
225=
25x9
34. Only modified the network structure and input
format (vector -> 3-D array)CNN in Keras
Convolution
Max Pooling
Convolution
Max Pooling
Input
1 x 28 x 28
25 x 26 x 26
25 x 13 x 13
50 x 11 x 11
50 x 5 x 5
Flattened
1250
Fully connected
feedforward network
Output
35. Recurrent Neural Network
(RNN)
• Sequence learning is the study of machine learning algorithms designed for
sequential data.
• Language model is one of the most interesting topics that use sequence
labelling.
• Recurrent Neural Network is a generalization of feedforward neural network
that has an internal memory. RNN is recurrent in nature as it performs the
same function for every input of data while the output of the current input
depends on the past one computation.
Applications:
• Sentiment Analysis
• Type ahead in Chat, Messaging & Email
• Language Text translation
• Speech to Text
36. Projects – Google AIY Kits
Google AIY Projects brings do-it-yourself artificial intelligence to your
maker projects using Raspberry PI and AI
• Voice Kit
• Vision Kit
37. Google AIY Projects
• Bird Watching
• Hand Command Recognizer
• Home Surveillance
• Fruits & vegetable freshness
detection
