Understanding computer vision with Deep LearningShubhWadekar
Topics covered in the Webinar
1. Overview of Machine Learning
2. Basics of Deep Learning
3. What is computer vision and its use-cases?
4. Various algorithms used in Computer Vision (mostly CNN)
5. Live hands-on demo of either Auto Cameraman or Face recognition system
6. What next?
Presented by Sandeep Giri
www.cloudxlab.com
Deep learning goes beyond the traditional machine learning of big data and analytics. In this session, we will review the AWS offering, Amazon Machine Learning, and the AWS GPU-intensive family of servers that run native machine learning and deep-learning algorithms. We will also cover some basic deep-learning algorithms using open source software. Session sponsored by Day1 Solutions.
Deep learning is making news across the country as one of the most promising techniques in machine learning research. However, these methods are complex to implement, finicky to tune, and state-of-the-art accuracy is only achieved by a few experts in the field. In this session, we give a beginner-friendly explanation of deep learning using neural networks—what it is, what it does, and how; and introduce the concept of deep features, which allows you to obtain great performance with reduced running times and data set sizes. We then show how these methods can easily be deployed on GPU instances (G2) on Amazon EC2.
AWS re:Invent 2016: Using MXNet for Recommendation Modeling at Scale (MAC306)Amazon Web Services
For many companies, recommendation systems solve important machine learning problems. But as recommendation systems grow to millions of users and millions of items, they pose significant challenges when deployed at scale. The user-item matrix can have trillions of entries (or more), most of which are zero. To make common ML techniques practical, sparse data requires special techniques. Learn how to use MXNet to build neural network models for recommendation systems that can scale efficiently to large sparse datasets.
Understanding computer vision with Deep LearningShubhWadekar
Topics covered in the Webinar
1. Overview of Machine Learning
2. Basics of Deep Learning
3. What is computer vision and its use-cases?
4. Various algorithms used in Computer Vision (mostly CNN)
5. Live hands-on demo of either Auto Cameraman or Face recognition system
6. What next?
Presented by Sandeep Giri
www.cloudxlab.com
Deep learning goes beyond the traditional machine learning of big data and analytics. In this session, we will review the AWS offering, Amazon Machine Learning, and the AWS GPU-intensive family of servers that run native machine learning and deep-learning algorithms. We will also cover some basic deep-learning algorithms using open source software. Session sponsored by Day1 Solutions.
Deep learning is making news across the country as one of the most promising techniques in machine learning research. However, these methods are complex to implement, finicky to tune, and state-of-the-art accuracy is only achieved by a few experts in the field. In this session, we give a beginner-friendly explanation of deep learning using neural networks—what it is, what it does, and how; and introduce the concept of deep features, which allows you to obtain great performance with reduced running times and data set sizes. We then show how these methods can easily be deployed on GPU instances (G2) on Amazon EC2.
AWS re:Invent 2016: Using MXNet for Recommendation Modeling at Scale (MAC306)Amazon Web Services
For many companies, recommendation systems solve important machine learning problems. But as recommendation systems grow to millions of users and millions of items, they pose significant challenges when deployed at scale. The user-item matrix can have trillions of entries (or more), most of which are zero. To make common ML techniques practical, sparse data requires special techniques. Learn how to use MXNet to build neural network models for recommendation systems that can scale efficiently to large sparse datasets.
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, we will provide a short background on Deep Learning focusing on relevant application domains and an introduction to the powerful and scalable Deep Learning framework, Apache MXNet. At the end of this tutorial you’ll be able to train your own deep neural network, fine tune existing state of the art models for image and object recognition. We’ll also deep dive on setting up your deep learning infrastructure on AWS and model deployment on AWS Lambda.
The Search for a New Visual Search Beyond Language - StampedeCon AI Summit 2017StampedeCon
Words are no longer sufficient in delivering the search results users are looking for, particularly in relation to image search. Text and languages pose many challenges in describing visual details and providing the necessary context for optimal results. Machine Learning technology opens a new world of search innovation that has yet to be applied by businesses.
In this session, Mike Ranzinger of Shutterstock will share a technical presentation detailing his research on composition aware search. He will also demonstrate how the research led to the launch of AI technology allowing users to more precisely find the image they need within Shutterstock’s collection of more than 150 million images. While the company released a number of AI search enabled tools in 2016, this new technology allows users to search for items in an image and specify where they should be located within the image. The research identifies the networks that localize and describe regions of an image as well as the relationships between things. The goal of this research was to improve the future of search using visual data, contextual search functions, and AI. A combination of multiple machine learning technologies led to this breakthrough.
This is a 2 hours overview on the deep learning status as for Q1 2017.
Starting with some basic concepts, continue to basic networks topologies , tools, HW/Accelerators and finally Intel's take on the the different fronts.
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
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.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/auvizsystems/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Nagesh Gupta, CEO and Founder of Auviz Systems, presents the "Trade-offs in Implementing Deep Neural Networks on FPGAs" tutorial at the May 2015 Embedded Vision Summit.
Video and images are a key part of Internet traffic—think of all the data generated by social networking sites such as Facebook and Instagram—and this trend continues to grow. Extracting usable information from video and images is thus a growing requirement in the data center. For example, object and face recognition are valuable for a wide range of uses, from social applications to security applications. Deep neural networks are currently the most popular form of convolutional neural networks (CNN) used in data centers for such applications. 3D convolutions are a core part of CNNs. Nagesh presents alternative implementations of 3D convolutions on FPGAs, and discusses trade-offs among them.
Squeezing Deep Learning Into Mobile PhonesAnirudh Koul
A practical talk by Anirudh Koul aimed at how to run Deep Neural Networks to run on memory and energy constrained devices like smart phones. Highlights some frameworks and best practices.
This presentation focuses on Deep Learning (DL) concepts, such as neural networks, backprop, activation functions, and Convolutional Neural Networks. You'll also learn how to incorporate Deep Learning in Android applications. Basic knowledge of matrices is helpful for this session, which is targeted primarily to beginners.
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
Evolving a Medical Image Similarity SearchSujit Pal
Slides for talk at Haystack Conference 2018. Covers evolution of an Image Similarity Search Proof of Concept built to identify similar medical images. Discusses various image vectorizing techniques that were considered in order to convert images into searchable entities, an evaluation strategy to rank these techniques, as well as various indexing strategies to allow searching for similar images at scale.
Introduction to Deep Learning with Will ConstableIntel Nervana
Deep Residual Nets, Activity recognition in videos, and Q&A systems using neon and the Nervana Cloud
Will Constable will start with an introduction to the field of Deep Learning, neon and the Nervana Cloud. The presentation will be followed by an interactive workshop using neon. neon is an open-source Python based Deep Learning framework that has been built from the ground up for speed, scalability and ease of use.
How to use transfer learning to bootstrap image classification and question a...Wee Hyong Tok
#theaiconf SFO 2018
Session by Danielle Dean, WeeHyong Tok
Transfer learning enables you to use pretrained deep neural networks trained on various large datasets (ImageNet, CIFAR, WikiQA, SQUAD, and more) and adapt them for various deep learning tasks (e.g., image classification, question answering, and more).
Wee Hyong Tok and Danielle Dean share the basics of transfer learning and demonstrate how to use the technique to bootstrap the building of custom image classifiers and custom question-answering (QA) models. You’ll learn how to use the pretrained CNNs available in various model libraries to custom build a convolution neural network for your use case. In addition, you’ll discover how to use transfer learning for question-answering tasks, with models trained on large QA datasets (WikiQA, SQUAD, and more), and adapt them for new question-answering tasks.
https://conferences.oreilly.com/artificial-intelligence/ai-ca/public/schedule/detail/68527
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, we will provide a short background on Deep Learning focusing on relevant application domains and an introduction to the powerful and scalable Deep Learning framework, Apache MXNet. At the end of this tutorial you’ll be able to train your own deep neural network, fine tune existing state of the art models for image and object recognition. We’ll also deep dive on setting up your deep learning infrastructure on AWS and model deployment on AWS Lambda.
The Search for a New Visual Search Beyond Language - StampedeCon AI Summit 2017StampedeCon
Words are no longer sufficient in delivering the search results users are looking for, particularly in relation to image search. Text and languages pose many challenges in describing visual details and providing the necessary context for optimal results. Machine Learning technology opens a new world of search innovation that has yet to be applied by businesses.
In this session, Mike Ranzinger of Shutterstock will share a technical presentation detailing his research on composition aware search. He will also demonstrate how the research led to the launch of AI technology allowing users to more precisely find the image they need within Shutterstock’s collection of more than 150 million images. While the company released a number of AI search enabled tools in 2016, this new technology allows users to search for items in an image and specify where they should be located within the image. The research identifies the networks that localize and describe regions of an image as well as the relationships between things. The goal of this research was to improve the future of search using visual data, contextual search functions, and AI. A combination of multiple machine learning technologies led to this breakthrough.
This is a 2 hours overview on the deep learning status as for Q1 2017.
Starting with some basic concepts, continue to basic networks topologies , tools, HW/Accelerators and finally Intel's take on the the different fronts.
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
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.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/auvizsystems/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Nagesh Gupta, CEO and Founder of Auviz Systems, presents the "Trade-offs in Implementing Deep Neural Networks on FPGAs" tutorial at the May 2015 Embedded Vision Summit.
Video and images are a key part of Internet traffic—think of all the data generated by social networking sites such as Facebook and Instagram—and this trend continues to grow. Extracting usable information from video and images is thus a growing requirement in the data center. For example, object and face recognition are valuable for a wide range of uses, from social applications to security applications. Deep neural networks are currently the most popular form of convolutional neural networks (CNN) used in data centers for such applications. 3D convolutions are a core part of CNNs. Nagesh presents alternative implementations of 3D convolutions on FPGAs, and discusses trade-offs among them.
Squeezing Deep Learning Into Mobile PhonesAnirudh Koul
A practical talk by Anirudh Koul aimed at how to run Deep Neural Networks to run on memory and energy constrained devices like smart phones. Highlights some frameworks and best practices.
This presentation focuses on Deep Learning (DL) concepts, such as neural networks, backprop, activation functions, and Convolutional Neural Networks. You'll also learn how to incorporate Deep Learning in Android applications. Basic knowledge of matrices is helpful for this session, which is targeted primarily to beginners.
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
Evolving a Medical Image Similarity SearchSujit Pal
Slides for talk at Haystack Conference 2018. Covers evolution of an Image Similarity Search Proof of Concept built to identify similar medical images. Discusses various image vectorizing techniques that were considered in order to convert images into searchable entities, an evaluation strategy to rank these techniques, as well as various indexing strategies to allow searching for similar images at scale.
Introduction to Deep Learning with Will ConstableIntel Nervana
Deep Residual Nets, Activity recognition in videos, and Q&A systems using neon and the Nervana Cloud
Will Constable will start with an introduction to the field of Deep Learning, neon and the Nervana Cloud. The presentation will be followed by an interactive workshop using neon. neon is an open-source Python based Deep Learning framework that has been built from the ground up for speed, scalability and ease of use.
How to use transfer learning to bootstrap image classification and question a...Wee Hyong Tok
#theaiconf SFO 2018
Session by Danielle Dean, WeeHyong Tok
Transfer learning enables you to use pretrained deep neural networks trained on various large datasets (ImageNet, CIFAR, WikiQA, SQUAD, and more) and adapt them for various deep learning tasks (e.g., image classification, question answering, and more).
Wee Hyong Tok and Danielle Dean share the basics of transfer learning and demonstrate how to use the technique to bootstrap the building of custom image classifiers and custom question-answering (QA) models. You’ll learn how to use the pretrained CNNs available in various model libraries to custom build a convolution neural network for your use case. In addition, you’ll discover how to use transfer learning for question-answering tasks, with models trained on large QA datasets (WikiQA, SQUAD, and more), and adapt them for new question-answering tasks.
https://conferences.oreilly.com/artificial-intelligence/ai-ca/public/schedule/detail/68527
Transfer learning enables you to use pretrained deep neural networks trained on various large datasets (ImageNet, CIFAR, WikiQA, SQUAD, and more) and adapt them for various deep learning tasks (e.g., image classification, question answering, and more).
Wee Hyong Tok and Danielle Dean share the basics of transfer learning and demonstrate how to use the technique to bootstrap the building of custom image classifiers and custom question-answering (QA) models. You’ll learn how to use the pretrained CNNs available in various model libraries to custom build a convolution neural network for your use case. In addition, you’ll discover how to use transfer learning for question-answering tasks, with models trained on large QA datasets (WikiQA, SQUAD, and more), and adapt them for new question-answering tasks.
Topics include:
An introduction to convolution neural networks and question-answering problems
Using pretrained CNNs and the last fully connected layer as a featurizer (Once the features are extracted, any existing classifier can be used for image classification, using the extracted features as inputs.)
Fine-tuning the pretrained models and adapting them for the new images
Using pretrained QA models trained on large QA datasets (WikiQA, SQUAD) and applying transfer learning for QA tasks
The Frontier of Deep Learning in 2020 and BeyondNUS-ISS
This talk will be a summary of the recent advances in deep learning research, current trends in the industry, and the opportunities that lie ahead.
We will discuss topics in research such as:
Transformers, GPT-3, BERT
Neural Architecture Search, Evolutionary Search
Distillation, self-learning
NeRF
Self-Attention
Also shifting industry trends such as:
The move to free data
Rising importance of 3D vision
Using synthetic data (Sim2Real)
Mobile vision & Federated Learning
Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
The Data Science Process - Do we need it and how to apply?Ivo Andreev
Machine learning is not black magic but a discipline that involves statistics, data science, analysis and hard work. From searching patterns and data preparation through applying and optimizing algorithms to obtaining usable predictions, one would need background and appropriate tools.
But do we need it, when there is already available AI as a service solution out there? Do we need to try hard with artificial neural networks? And if we decide to do so, what tools would be a safe bet?
In this session we will go through real world examples, mention key tools from Microsoft and open source world to do data science and machine learning and most importantly - we will provide a workflow and some best practices.
Lessons Learned from Building Machine Learning Software at NetflixJustin Basilico
Talk from Software Engineering for Machine Learning Workshop (SW4ML) at the Neural Information Processing Systems (NIPS) 2014 conference in Montreal, Canada on 2014-12-13.
Abstract:
Building a real system that incorporates machine learning as a part can be a difficult effort, both in terms of the algorithmic and engineering challenges involved. In this talk I will focus on the engineering side and discuss some of the practical issues we’ve encountered in developing real machine learning systems at Netflix and some of the lessons we’ve learned over time. I will describe our approach for building machine learning systems and how it comes from a desire to balance many different, and sometimes conflicting, requirements such as handling large volumes of data, choosing and adapting good algorithms, keeping recommendations fresh and accurate, remaining responsive to user actions, and also being flexible to accommodate research and experimentation. I will focus on what it takes to put machine learning into a real system that works in a feedback loop with our users and how that imposes different requirements and a different focus than doing machine learning only within a lab environment. I will address the particular software engineering challenges that we’ve faced in running our algorithms at scale in the cloud. I will also mention some simple design patterns that we’ve fond to be useful across a wide variety of machine-learned systems.
Data Workflows for Machine Learning - Seattle DAMLPaco Nathan
First public meetup at Twitter Seattle, for Seattle DAML:
http://www.meetup.com/Seattle-DAML/events/159043422/
We compare/contrast several open source frameworks which have emerged for Machine Learning workflows, including KNIME, IPython Notebook and related Py libraries, Cascading, Cascalog, Scalding, Summingbird, Spark/MLbase, MBrace on .NET, etc. The analysis develops several points for "best of breed" and what features would be great to see across the board for many frameworks... leading up to a "scorecard" to help evaluate different alternatives. We also review the PMML standard for migrating predictive models, e.g., from SAS to Hadoop.
If there is one crucial thing in building ML models, this would be the data preparation. That is the process of transforming raw data to a state where machine learning algorithms could be run to disclose insights and make predictions. Data preparation involves analysis, depends on the nature of the problem and the particular algorithms. As far as there are knowledge and experience involved, there is no such thing as automation, which makes the role of the data scientist the key to success.
ML is trendy and Microsoft already have more than 10 services to support ML. So we will focus on tools like Azure ML Workbench and Python for data preparation, review some common tricks to approach data and experiment in Azure ML Studio.
Building machine learning muscle in your team & transitioning to make them do machine learning at scale. We also discuss about Spark & other relevant technologies.
Big Data Analytics (ML, DL, AI) hands-onDony Riyanto
Ini adalah slide tambahan dari materi pengenalan Big Data Analytics (di file berikutnya), yang mengajak kita mulai hands-on dengan beberapa hal terkait Machine/Deep Learning, Big Data (batch/streaming), dan AI menggunakan Tensor Flow
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit-baidu
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Dr. Ren Wu, former distinguished scientist at Baidu's Institute of Deep Learning (IDL), presents the keynote talk, "Enabling Ubiquitous Visual Intelligence Through Deep Learning," at the May 2015 Embedded Vision Summit.
Deep learning techniques have been making headlines lately in computer vision research. Using techniques inspired by the human brain, deep learning employs massive replication of simple algorithms which learn to distinguish objects through training on vast numbers of examples. Neural networks trained in this way are gaining the ability to recognize objects as accurately as humans.
Some experts believe that deep learning will transform the field of vision, enabling the widespread deployment of visual intelligence in many types of systems and applications. But there are many practical problems to be solved before this goal can be reached. For example, how can we create the massive sets of real-world images required to train neural networks? And given their massive computational requirements, how can we deploy neural networks into applications like mobile and wearable devices with tight cost and power consumption constraints?
In this talk, Ren shares an insider’s perspective on these and other critical questions related to the practical use of neural networks for vision, based on the pioneering work being conducted by his former team at Baidu.
Note 1: Regarding the ImageNet results included in this presentation, the organizers of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) have said: “Because of the violation of the regulations of the test server, these results may not be directly comparable to results obtained and reported by other teams.” (http://www.image-net.org/challenges/LSVRC/announcement-June-2-2015)
Note 2: The presenter, Ren Wu, has told the Embedded Vision Alliance that “There was some ambiguity with the rules. According to the ‘official’ interpretation of the rules, there should be no more than 52 submissions within a half year. For us, we achieved the reported results after 200 tests total within a half year. We believe there is no way to obtain any measurable gains, nor did we try to obtain any gains, from an 'extra' hundred tests as our networks have billions of parameters and are trained by tens of billions of training samples.”
Machine Learning in 2016: Live Q&A with Carlos GuestrinTuri, Inc.
Live webinar session with Carlos Guestrin, Dato CEO and Amazon Professor of Machine Learning at University of Washington. Carlos reviewed 2015 highlights, previewed the Dato roadmap, and answered real-time questions from participants about use cases, algorithms, and resources.
Tutorial for Machine Learning 101 (an all-day tutorial at Strata + Hadoop World, New York City, 2015)
The course is designed to introduce machine learning via real applications like building a recommender image analysis using deep learning.
In this talk we cover deployment of machine learning models.
Overview of Machine Learning and Feature EngineeringTuri, Inc.
Machine Learning 101 Tutorial at Strata NYC, Sep 2015
Overview of machine learning models and features. Visualization of feature space and feature engineering methods.
Scalable tabular (SFrame, SArray) and graph (SGraph) data-structures built for out-of-core data analysis.
The SFrame package provides the complete implementation of:
SFrame
SArray
SGraph
The C++ SDK surface area (gl_sframe, gl_sarray, gl_sgraph)
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
1. Deep learning
made doubly easy with
reusable deep features
Carlos Guestrin
Dato, CEO
University of Washington, Amazon Prof. of ML
2. Successful apps
in 2015 must be
intelligent
Machine
learning
key to next-gen apps
• Recommenders
• Fraud detection
• Ad targeting
• Financial models
• Personalized medicine
• Churn prediction
• Smart UX
(video & text)
• Personal assistants
• IoT
• Socials nets
• …Last decade:
Data management
Now:
Intelligent apps
?
Last 5 years:
Traditional analytics
3. The ML pipeline circa 2013
DATA
ML
Algorithm
My curve is
better than
your curve
Write a
paper
4. 2015: Production ML pipeline
DATA
YourWebServiceor
IntelligentApp
ML
Algorithm
Data
cleaning
&
feature
eng
Offline
eval &
Parameter
search
Deploy
model
Data engineering Data intelligence Deployment
Using deep learning
Goal: Platform to help implement, manage, optimize entire pipeline
7. 7
Simple example: Spam filtering
• A user walks into an email…
- Will she thinks its spam?
• What’s the probability email is spam?
Text of email
User info
Source info
Input: x
MODEL
Yes!
No
Output:
Probability of y
8. 8
Feature engineering:
the painful black art of transforming raw inputs
into useful inputs for ML algorithm
• E.g., important words, stemming text, complex
transformation of inputs,…
MODEL
Yes!
No
Output:
Probability of y
Feature
extraction
Features: Φ(x)
Text of email
User info
Source info
Input: x
10. 10
Linear classifiers
• Most common classifier
- Logistic regression
- SVMs
- …
• Decision correspond to
hyperplane:
- Line in high dimensional
space
w0 + w1 x1 + w2 x2 > 0 w0 + w1 x1 + w2 x2 < 0
11. 11
Graph representation of classifier:
useful for defining neural networks
x
1
x
2
x
d
y
…
1
w2 w0 + w1 x1 + w2 x2 + … + wd xd
> 0, output 1
< 0, output 0
Input Output
12. 12
What can a linear classifier represent
x1 OR x2 x1 AND x2
x
1
x
2
1
y
-0.5
1
1
x
1
x
2
1
y
-1.5
1
1
13. 13
What can’t a simple linear classifier represent?
XOR
the counterexample
to everything
Need non-linear features
14. Solving the XOR problem: Adding a layer
XOR = x1 AND NOT x2 OR NOT x1 AND x2
z
1
-0.5
1
-1
z1 z2
z
2
-0.5
-1
1
x
1
x
2
1
y
1 -0.5
1
1
Thresholded to 0 or 1
15. 15
A neural network
• Layers and layers and layers of linear models and non-linear
transformation
• Around for about 50 years
- Fell in “disfavor” in 90s
• In last few years, big resurgence
- Impressive accuracy on a several benchmark problems
- Powered by huge datasets, GPUs, & modeling/learning alg
improvements
x
1
x
2
1
z
1
z
2
1
y
17. 17
Image features
• Features = local detectors
- Combined to make prediction
- (in reality, features are more low-level)
Face!
Eye
Eye
Nose
Mouth
18. 18
Many hand create features exist…
Computer$vision$features$
SIFT$ Spin$image$
HoG$ RIFT$
Textons$ GLOH$
Slide$Credit:$Honglak$Lee$
19. 19
Standard image classification approach
Input
Computer$vision$features$
SIFT$ Spin$image$
HoG$ RIFT$
Textons$ GLOH$
Slide$Credit:$Honglak$Lee$
Extract features Use simple classifier
e.g., logistic regression, SVMs
Car?
20. 20
Many hand create features exist…
Computer$vision$features$
SIFT$ Spin$image$
HoG$ RIFT$
Textons$ GLOH$
Slide$Credit:$Honglak$Lee$
… but very painful to design
21. 21
Use neural network to learn features
Each layer learns features, at different levels of abstraction
Y LeCun
MA Ranzato
Deep Learning = Learning Hierarchical Representations
It's deep if it has more than one stage of non-linear feature
transformation
Trainable
Classifier
Low-Level
Feature
Mid-Level
Feature
High-Level
Feature
Feature visualization of convolutional net trained on ImageNet from [ Zeiler & Fergus 2013]
22. 22
Many tricks needed to work well…
• Different types of layers, connections,… needed for high accuracy
Krizhevsky et al.
‘12
30. Deep learning score card
Pros
• Enables learning of features rather
than hand tuning
• Impressive performance gains on
- Computer vision
- Speech recognition
- Some text analysis
• Potential for much more impact
Cons
31. Deep learning workflow
Lots of
labeled data
Training set
Validation set
80%
20%
Learn deep
neural net
model
Validate
32. Deep learning score card
Pros
• Enables learning of features rather
than hand tuning
• Impressive performance gains on
- Computer vision
- Speech recognition
- Some text analysis
• Potential for much more impact
Cons
• Computationally really expensive
• Requires a lot of data for high
accuracy
• Extremely hard to tune
- Choice of architecture
- Parameter types
- Hyperparameters
- Learning algorithm
- …
• Computational + so many choices =
incredibly hard to tune
34. 40
Change image classification approach?
Input
Computer$vision$features$
SIFT$ Spin$image$
HoG$ RIFT$
Textons$ GLOH$
Slide$Credit:$Honglak$Lee$
Extract features Use simple classifier
e.g., logistic regression, SVMs
Car?
Can we learn features
from data,
even when
we don’t have
data or time?
35. 41
Transfer learning:
Use data from one domain to help learn on another
Lots of data:
Learn
neural net
Great
accuracy on
cat v. dogvs.
Some data: Neural net as
feature extractor
+
Simple classifier
Great accuracy on
101 categories
Old idea, explored for deep learning by Donahue et al. ’14
36. 42
What’s learned in a neural net
Neural net trained for Task 1: cat vs. dog
Very specific to Task 1
Should be ignored for other tasks
More generic
Can be used as feature extractor
vs.
37. 43
Transfer learning in more detail…
Neural net trained for Task 1: cat vs. dog
Very specific to Task 1
Should be ignored for other tasks
More generic
Can be used as feature extractor
Keep weights fixed!
For Task 2, predicting 101 categories, learn only end part
Use simple classifier
e.g., logistic regression, SVMs
Class
?
38. 44
Careful where you cut…
Last few layers tend to be too specific
Y LeCun
MA Ranzato
Deep Learning = Learning Hierarchical Representations
It's deep if it has more than one stage of non-linear feature
transformation
Trainable
Classifier
Low-Level
Feature
Mid-Level
Feature
High-Level
Feature
Feature visualization of convolutional net trained on ImageNet from [ Zeiler & Fergus 2013]
Too specific for
car detectionUse these!
39. Transfer learning with deep features
Training set
Validation set
80%
20%
Learn
simple
model
Some
labeled data
Extract
features with
neural net
trained on
different task
Validate
Deploy in
production
42. Simple text classification with bag of words
aardvark 0
about 2
all 2
Africa 1
apple 0
anxious 0
...
gas 1
...
oil 1
…
Zaire 0
Use simple classifier
e.g., logistic regression, SVMs
Class
?
One “feature” per word
43. Word2Vec: Neural network for finding high
dimensional representation per word Mikolov et al. ‘13
Skip-gram Model: From a word, predict nearby words in sentence
Awesome learning talk at
Strata
deep
300 dim
representation
300 dim
representation
300 dim
representation
300 dim
representation
300 dim
representation
300 dim
representation
Neural net
Viewed as deep
features
44. 50
Related words placed nearby high dim space
Projecting 300 dim space into 2 dim with PCA (Mikolov et al. ’13)
45. Classifier:
e.g., logistic regression, SVMs with
300 x number_of_words parameters
Class
?
Embed each
word into
300 dim
space
Text classification with word embeddings
aardvark 0
about 2
all 2
Africa 1
apple 0
anxious 0
...
gas 1
...
oil 1
…
Zaire 0
49. 55
DATA
ML
Algorithm
Deployment?
• Write spec, other team
implements in ‘production’ language
o 6-12 months
o Stale/irrelevant model/approach
o 2 teams maintaining 2 systems
Custom
Model
Data Engineers, Data
Architects, DevOps,
App Developers
App
A
P
I
Data Scientist
50. ML deployment requirements
56
Easy to
integrate
Rest API
Scalable
Fault tolerant
Flexible
Any model,
any Python
App
A
P
I
A
P
I
C
A
C
H
E
A
P
I
C
A
C
H
E
A
P
I
C
A
C
H
E
LB
GLC
Model
GLC
Model
GLC
Model
Dato
Models
Dato
Models
Dato
Models
A
P
I
C
A
C
H
E
A
P
I
C
A
C
H
E
A
P
I
C
A
C
H
E
LB
GLC
Model
GLC
Model
GLC
Model
Python
Python
Python
51. 57
Do-It-Yourself
• Web Service layer:
- Tornado, Flask, Keen, Django, …
• Caching layer:
- Redis, Cassandra, Memcached,
DynamoDb, MySQL, …
• Logs:
- Logback, LogStash, Splunk, Loggly, …
• Metrics:
- AWS CloudWatch, Mixpanel, Librato, …
A
P
I
C
A
C
H
E
A
P
I
C
A
C
H
E
A
P
I
C
A
C
H
E
LB
GLC
Model
GLC
Model
GLC
Model
Python
Python
Python
App
52. 58
… or use Dato Predictive Services
YourWebServiceor
IntelligentApp
ML Model
Dato
Predictive Services
CachingLayer
Predictive
ObjectServer
Serves predictions in a robust, scalable, incremental fashion
Better
ML Model
Serve any model: GraphLab Create, scikit-learn, Python, …
53. • Out-of-core computation
• Tools for feature engineering
• Rich data type support
• Models built for scale
• App-oriented toolkits
• Advanced ML & Extensible
• Deploy models as low-latency REST services
• Same code for distributed computation
• Elastically scale up or out with one command
• Job monitoring & model management
• Deploy existing Python code & models
• Run on AWS EC2 or Hadoop Yarn
SGraph
Create Engine
SFrameCanvas
Machine Learning Toolkits SDK
GraphLab Create Dato Distributed Dato Predictive Services
Predictive Engine
REST Client Direct
Model Mgmt
Distributed Engine
DirectJob Client
Job Mgmt
Dato Platform
55. Deep learning made easy with deep features
Deep learning: exciting ML development
Slow, lots of tuning,
needs lots of data
Deep features: reuse deep models for new domains
Needs less data Faster training times Much simpler tuning
Can still achieve excellent performance
Editor's Notes
So I got started with ML by taking a class. Data -> to ML algo, and then generate a plot.
Of course this isn’t how actual applications are written, but this is often where customers are starting when approaching taking ML to production.