TensorFlow is an open source software library for machine learning developed by Google. It provides primitives for defining functions on tensors and automatically computing their derivatives. TensorFlow represents computations as data flow graphs with nodes representing operations and edges representing tensors. It is widely used for neural networks and deep learning tasks like image classification, language processing, and speech recognition. TensorFlow is portable, scalable, and has a large community and support for deployment compared to other frameworks. It works by constructing a computational graph during modeling, and then executing operations by pushing data through the graph.
What is TensorFlow? | Introduction to TensorFlow | TensorFlow Tutorial For Be...Simplilearn
This presentation on TensorFlow will help you in understanding what exactly is TensorFlow and how it is used in Deep Learning. TensorFlow is a software library developed by Google for the purposes of conducting machine learning and deep neural network research. In this tutorial, you will learn the fundamentals of TensorFlow concepts, functions, and operations required to implement deep learning algorithms and leverage data like never before. This TensorFlow tutorial is ideal for beginners who want to pursue a career in Deep Learning. Now, let us deep dive into this TensorFlow tutorial and understand what TensorFlow actually is and how to use it.
Below topics are explained in this TensorFlow presentation:
1. What is Deep Learning?
2. Top Deep Learning Libraries
3. Why TensorFlow?
4. What is TensorFlow?
5. What are Tensors?
6. What is a Data Flow Graph?
7. Program Elements in TensorFlow
8. Use case implementation using TensorFlow
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence
Learn more at: https://www.simplilearn.com
The release of TensorFlow 2.0 comes with a significant number of improvements over its 1.x version, all with a focus on ease of usability and a better user experience. We will give an overview of what TensorFlow 2.0 is and discuss how to get started building models from scratch using TensorFlow 2.0’s high-level api, Keras. We will walk through an example step-by-step in Python of how to build an image classifier. We will then showcase how to leverage a transfer learning to make building a model even easier! With transfer learning, we can leverage other pretrained models such as ImageNet to drastically speed up the training time of our model. TensorFlow 2.0 makes this incredibly simple to do.
Develop a fundamental overview of Google TensorFlow, one of the most widely adopted technologies for advanced deep learning and neural network applications. Understand the core concepts of artificial intelligence, deep learning and machine learning and the applications of TensorFlow in these areas.
The deck also introduces the Spotle.ai masterclass in Advanced Deep Learning With Tensorflow and Keras.
Transformer modality is an established architecture in natural language processing that utilizes a framework of self-attention with a deep learning approach.
This presentation was delivered under the mentorship of Mr. Mukunthan Tharmakulasingam (University of Surrey, UK), as a part of the ScholarX program from Sustainable Education Foundation.
What is TensorFlow? | Introduction to TensorFlow | TensorFlow Tutorial For Be...Simplilearn
This presentation on TensorFlow will help you in understanding what exactly is TensorFlow and how it is used in Deep Learning. TensorFlow is a software library developed by Google for the purposes of conducting machine learning and deep neural network research. In this tutorial, you will learn the fundamentals of TensorFlow concepts, functions, and operations required to implement deep learning algorithms and leverage data like never before. This TensorFlow tutorial is ideal for beginners who want to pursue a career in Deep Learning. Now, let us deep dive into this TensorFlow tutorial and understand what TensorFlow actually is and how to use it.
Below topics are explained in this TensorFlow presentation:
1. What is Deep Learning?
2. Top Deep Learning Libraries
3. Why TensorFlow?
4. What is TensorFlow?
5. What are Tensors?
6. What is a Data Flow Graph?
7. Program Elements in TensorFlow
8. Use case implementation using TensorFlow
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence
Learn more at: https://www.simplilearn.com
The release of TensorFlow 2.0 comes with a significant number of improvements over its 1.x version, all with a focus on ease of usability and a better user experience. We will give an overview of what TensorFlow 2.0 is and discuss how to get started building models from scratch using TensorFlow 2.0’s high-level api, Keras. We will walk through an example step-by-step in Python of how to build an image classifier. We will then showcase how to leverage a transfer learning to make building a model even easier! With transfer learning, we can leverage other pretrained models such as ImageNet to drastically speed up the training time of our model. TensorFlow 2.0 makes this incredibly simple to do.
Develop a fundamental overview of Google TensorFlow, one of the most widely adopted technologies for advanced deep learning and neural network applications. Understand the core concepts of artificial intelligence, deep learning and machine learning and the applications of TensorFlow in these areas.
The deck also introduces the Spotle.ai masterclass in Advanced Deep Learning With Tensorflow and Keras.
Transformer modality is an established architecture in natural language processing that utilizes a framework of self-attention with a deep learning approach.
This presentation was delivered under the mentorship of Mr. Mukunthan Tharmakulasingam (University of Surrey, UK), as a part of the ScholarX program from Sustainable Education Foundation.
TensorFlow에 대한 분석 내용
- TensorFlow?
- 배경
- DistBelief
- Tutorial - Logistic regression
- TensorFlow - 내부적으로는
- Tutorial - CNN, RNN
- Benchmarks
- 다른 오픈 소스들
- TensorFlow를 고려한다면
- 설치
- 참고 자료
TensorFlow Tutorial | Deep Learning With TensorFlow | TensorFlow Tutorial For...Simplilearn
This presentation on TensorFlow will help you understand what is Deep Learning and it's libraries, why use TensorFlow, what is TensorFlow, how to build a computational graph, programming using elements in TensorFlow, what are Recurrent Neural Networks along with a use case implementation on TensorFlow. TensorFlow is a software library developed by Google for the purposes of conducting machine learning and deep neural network research. In this video, you will learn the fundamentals of TensorFlow concepts, functions and operations required to implement deep learning algorithms and leverage data like never before. Now let's get started in mastering the concept of Deep Learning using TensorFlow.
Below topics are explained in this TensorFlow presentation:
1. What is Deep Learning?
2. Top Deep Learning libraries?
3. Why use TensorFlow?
4. What is TensorFlow?
5. Building a computational graph
6. Programming elements in TensorFlow
7. Introducing Recurrent Neural Networks
8. Use case implementation of RNN using TensorFlow
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence
Learn more at: https://www.simplilearn.com
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
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.
Introduction to Deep Learning, Keras, and TensorFlowSri Ambati
This meetup was recorded in San Francisco on Jan 9, 2019.
Video recording of the session can be viewed here: https://youtu.be/yG1UJEzpJ64
Description:
This fast-paced session starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next, we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful. If time permits, we'll look at the UAT, CLT, and the Fixed Point Theorem. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)
Oswald's Bio:
Oswald Campesato is an education junkie: a former Ph.D. Candidate in Mathematics (ABD), with multiple Master's and 2 Bachelor's degrees. In a previous career, he worked in South America, Italy, and the French Riviera, which enabled him to travel to 70 countries throughout the world.
He has worked in American and Japanese corporations and start-ups, as C/C++ and Java developer to CTO. He works in the web and mobile space, conducts training sessions in Android, Java, Angular 2, and ReactJS, and he writes graphics code for fun. He's comfortable in four languages and aspires to become proficient in Japanese, ideally sometime in the next two decades. He enjoys collaborating with people who share his passion for learning the latest cool stuff, and he's currently working on his 15th book, which is about Angular 2.
PyTorch vs TensorFlow: The Force Is Strong With Which One? | Which One You Sh...Edureka!
( ** Deep Learning Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka comparison PPT of "PyTorch vs TensorFlow" provides you with a detailed comparison between the top 2 Python Deep Learning Frameworks.
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Brief introduction on attention mechanism and its application in neural machine translation, especially in transformer, where attention was used to remove RNNs completely from NMT.
The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.
A fast-paced introduction to TensorFlow 2 about some important new features (such as generators and the @tf.function decorator) and TF 1.x functionality that's been removed from TF 2 (yes, tf.Session() has retired).
Some concise code samples are presented to illustrate how to use new features of TensorFlow 2.
Introduction To TensorFlow | Deep Learning Using TensorFlow | TensorFlow Tuto...Edureka!
In this TensorFlow tutorial, you will be learning all the basics of TensorFlow and how to create a Deep Learning Model. It includes the following topics:
1. Deep Learning vs Machine Learning
2. What is TensorFlow?
3. TensorFlow Use-Case
Intro - End to end ML with Kubeflow @ SignalConf 2018Holden Karau
There are many great tools for training machine learning tools, ranging from sci-kit to Apache Spark, and tensorflow. However many of these systems largely leave open the question how to use our models outside of the batch world (like in a reactive application). Different options exist for persisting the results and using them for live training, and we will explore the trade-offs of the different formats and their corresponding serving/prediction layers.
TonY: Native support of TensorFlow on HadoopAnthony Hsu
Anthony Hsu, Jonathan Hung, and Keqiu Hu offer an overview of TensorFlow on YARN (TonY), a framework to natively run TensorFlow on Hadoop. TonY enables running TensorFlow distributed training as a new type of Hadoop application. Its native Hadoop connector, together with other features, aims to run TensorFlow jobs as reliably and flexibly as other applications on Hadoop.
Video: https://youtu.be/sIfnsU-5jHM
TensorFlow에 대한 분석 내용
- TensorFlow?
- 배경
- DistBelief
- Tutorial - Logistic regression
- TensorFlow - 내부적으로는
- Tutorial - CNN, RNN
- Benchmarks
- 다른 오픈 소스들
- TensorFlow를 고려한다면
- 설치
- 참고 자료
TensorFlow Tutorial | Deep Learning With TensorFlow | TensorFlow Tutorial For...Simplilearn
This presentation on TensorFlow will help you understand what is Deep Learning and it's libraries, why use TensorFlow, what is TensorFlow, how to build a computational graph, programming using elements in TensorFlow, what are Recurrent Neural Networks along with a use case implementation on TensorFlow. TensorFlow is a software library developed by Google for the purposes of conducting machine learning and deep neural network research. In this video, you will learn the fundamentals of TensorFlow concepts, functions and operations required to implement deep learning algorithms and leverage data like never before. Now let's get started in mastering the concept of Deep Learning using TensorFlow.
Below topics are explained in this TensorFlow presentation:
1. What is Deep Learning?
2. Top Deep Learning libraries?
3. Why use TensorFlow?
4. What is TensorFlow?
5. Building a computational graph
6. Programming elements in TensorFlow
7. Introducing Recurrent Neural Networks
8. Use case implementation of RNN using TensorFlow
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence
Learn more at: https://www.simplilearn.com
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
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.
Introduction to Deep Learning, Keras, and TensorFlowSri Ambati
This meetup was recorded in San Francisco on Jan 9, 2019.
Video recording of the session can be viewed here: https://youtu.be/yG1UJEzpJ64
Description:
This fast-paced session starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next, we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful. If time permits, we'll look at the UAT, CLT, and the Fixed Point Theorem. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)
Oswald's Bio:
Oswald Campesato is an education junkie: a former Ph.D. Candidate in Mathematics (ABD), with multiple Master's and 2 Bachelor's degrees. In a previous career, he worked in South America, Italy, and the French Riviera, which enabled him to travel to 70 countries throughout the world.
He has worked in American and Japanese corporations and start-ups, as C/C++ and Java developer to CTO. He works in the web and mobile space, conducts training sessions in Android, Java, Angular 2, and ReactJS, and he writes graphics code for fun. He's comfortable in four languages and aspires to become proficient in Japanese, ideally sometime in the next two decades. He enjoys collaborating with people who share his passion for learning the latest cool stuff, and he's currently working on his 15th book, which is about Angular 2.
PyTorch vs TensorFlow: The Force Is Strong With Which One? | Which One You Sh...Edureka!
( ** Deep Learning Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka comparison PPT of "PyTorch vs TensorFlow" provides you with a detailed comparison between the top 2 Python Deep Learning Frameworks.
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Brief introduction on attention mechanism and its application in neural machine translation, especially in transformer, where attention was used to remove RNNs completely from NMT.
The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.
A fast-paced introduction to TensorFlow 2 about some important new features (such as generators and the @tf.function decorator) and TF 1.x functionality that's been removed from TF 2 (yes, tf.Session() has retired).
Some concise code samples are presented to illustrate how to use new features of TensorFlow 2.
Introduction To TensorFlow | Deep Learning Using TensorFlow | TensorFlow Tuto...Edureka!
In this TensorFlow tutorial, you will be learning all the basics of TensorFlow and how to create a Deep Learning Model. It includes the following topics:
1. Deep Learning vs Machine Learning
2. What is TensorFlow?
3. TensorFlow Use-Case
Intro - End to end ML with Kubeflow @ SignalConf 2018Holden Karau
There are many great tools for training machine learning tools, ranging from sci-kit to Apache Spark, and tensorflow. However many of these systems largely leave open the question how to use our models outside of the batch world (like in a reactive application). Different options exist for persisting the results and using them for live training, and we will explore the trade-offs of the different formats and their corresponding serving/prediction layers.
TonY: Native support of TensorFlow on HadoopAnthony Hsu
Anthony Hsu, Jonathan Hung, and Keqiu Hu offer an overview of TensorFlow on YARN (TonY), a framework to natively run TensorFlow on Hadoop. TonY enables running TensorFlow distributed training as a new type of Hadoop application. Its native Hadoop connector, together with other features, aims to run TensorFlow jobs as reliably and flexibly as other applications on Hadoop.
Video: https://youtu.be/sIfnsU-5jHM
Introduction to the new Tensorflow 2.x and the Coral AI Edge TPU hardware. The presentation introduces Tensorflow main features such as Sequential and Functional APIs, mobile support with Tensorflow Lite, web support with TensorflowJS and Google Cloud support with TFX.
In addition, the presentation introduces the new edge TPU architecture coming from Coral AI, including its main hardware features and description of the compiling flow.
Blending Supersonic, Subatomic Java with deep learning to perform object detection. Sounds interesting? Because it is! Then watch this session to learn how to create a microservice combining TensorFlow and Quarkus together into one executable using GraalVM native image, JNI, and Protobuf. With this, we detect objects in photos by returning labels, bounding boxes, and confidence scores. Additionally, we will touch on Open Data Hub, an AI/ML solution for OpenShift.
Get your organization’s feet wet with Semantic Web TechnologiesAndré Torkveen
Slide deck from tutorial, given during Semantic Days 2013 Conference. A Linux VM image that holds all material demonstrated (Jena/Fuseki server with ELDA GUI, Turtle model, dataset and custom code) is available separately via https://www.dropbox.com/s/jzixmn47fh421us/SemDays_image.zip
Webinar : Talend : The Non-Programmer's Swiss Knife for Big DataEdureka!
Talend Open Studio (TOS) is a wonderful open source Data Integration (DI) tool used to build end-to-end ETL solutions. This course will not only help the beginners to understand the art of data integration but also equip them with Big Data skills in the smart way. This course also aims to educate you about Big Data through Talend's powerful product "Talend for Big Data" (the first Hadoop-based data integration platform). The topics covered in the presentation are:
1. Why ETL is still essential and arrival of Big Data is not the doom of ETL era
2.How and why ETL is using Talend
3.Talend complementing Hadoop Ecosystem? Adopting to ETL-Big Data industry
4.Learn Big Data not in months but in Minutes! Sounds too good?
Spark Summit EU 2015: Lessons from 300+ production usersDatabricks
At Databricks, we have a unique view into over a hundred different companies trying out Spark for development and production use-cases, from their support tickets and forum posts. Having seen so many different workflows and applications, some discernible patterns emerge when looking at common performance and scalability issues that our users run into. This talk will discuss some of these common common issues from an engineering and operations perspective, describing solutions and clarifying misconceptions.
Watch video: https://www.youtube.com/watch?v=SgmmoRCmIa4&list=PLIuWze7quVLDSxJKDj3pRSqvmHAzQ_9vd&index=6
Here is the summary of what you'll learn:
00:02:00 Welcome
00:03:32 Meet Chafik, CEO of Brainboard.co
00:05:00 Our goal at Brainboard
00:06:00 Terraform modules definition
00:20:00 Build your own modules
00:21:00 Azure
00:48:00 AWS
00:52:00 Best practices
00:56:00 Review some of the most used community modules
00:56:43 Lambda
01:00:30 AKS
01:04:00 Where to host your modules?
01:06:04 Challenges of maintaining modules within a team
01:09:00 Build your own modules’ catalog
This conference is dedicated exclusively to application development, cloud transformation and web new framework like Blazor,
Angular, React, software architecture and Patterns like Microservices and Functions, IA and ML, Blockchain, Big data, analytics, IoT.and more.
This is the first edition of TechDay Conf, it is a virtual Conference, this conference, full-day technical sessions, 2 hours of learning. and sharing.
This virtual conference will be presented MVP (Microsoft Most Valuable Professional) and expert, by a slot of 15 minutes each one to present the best practices or a demo.
This digital virtual event enables all types of developers to connect and learn differents online sessions in two languages: French and English.
Tech talk on what Azure Databricks is, why you should learn it and how to get started. We'll use PySpark and talk about some real live examples from the trenches, including the pitfalls of leaving your clusters running accidentally and receiving a huge bill ;)
After this you will hopefully switch to Spark-as-a-service and get rid of your HDInsight/Hadoop clusters.
This is part 1 of an 8 part Data Science for Dummies series:
Databricks for dummies
Titanic survival prediction with Databricks + Python + Spark ML
Titanic with Azure Machine Learning Studio
Titanic with Databricks + Azure Machine Learning Service
Titanic with Databricks + MLS + AutoML
Titanic with Databricks + MLFlow
Titanic with DataRobot
Deployment, DevOps/MLops and Operationalization
Today, when data is mushrooming and coming in heterogeneous forms, there is a growing need for a flexible, adaptable, efficient and cost effective integration platform which will take minimum on-boarding time and interact and entertain n number of platforms. Talend fits just perfect in this space with a proven track record, so learning talend makes lot of sense for anybody associated with data world.
If you understand how to manage, transform, store your organisation data (retail, banking, airlines, research, insurance, cards etc.) and effectively represent it which is the backbone behind any successful MIS system/reporting/dash board then you are a key person that organisation most sought after.
En esta charla miraremos al futuro introduciendo Spark como alternativa al clásico motor de Hadoop MapReduce. Describiremos las diferencias más importantes frente al mismo, se detallarán los componentes principales que componen el ecosistema Spark, e introduciremos conceptos básicos que permitan empezar con el desarrollo de aplicaciones básicas sobre el mismo.
Future proofing design work with Web componentsbtopro
Web components are a W3C standard that's been adopted by all major browsers as of October 2018. The Version 1 specification is a joy to work with and brings the web into a composing context from a raw materials one. That is, we can now directly repurpose and leverage our efforts to build bigger and better experiences (like modern home development practices) instead of constantly reinventing the wheel (like molding bricks out of clay to work on our house).
As of this writing, the ELMS:LN team (4 people) at Penn State has created 433 web components for generalized use. We've built an editor, a CMS, integrated those elements into Drupal (multiple versions), delivered static sites, worked on desktop apps, and done design work entirely, end to end, using web components and a uniform process for creating and deploying them.
Talk structure:
What are web components, can I use them, answering questions of libraries, polyfills, SEO, and accessibility
Examples of who has adopted them and what they doing with them
Community resources like polymer slack, webcomponents, and open-wc.org
Detailed examples of adoption in production, Drupal and non-Drupal environments, lessons learned and unthinkable wins
Our WCFactory tooling that automates much of the workflow of producing a sustainable element portfolio
How teams can leverage web components across projects
Where Drupal 6,7,8,9 fit into the future with web components
Where the future is going with HAXeditor and HAXcms, the future of micro-site generation and management
Our team is in love with web components and we think you will too! Join us and build better, more sustainable design systems of the future (today)!
Unified Big Data Processing with Apache SparkC4Media
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1yNuLGF.
Matei Zaharia talks about the latest developments in Spark and shows examples of how it can combine processing algorithms to build rich data pipelines in just a few lines of code. Filmed at qconsf.com.
Matei Zaharia is an assistant professor of computer science at MIT, and CTO of Databricks, the company commercializing Apache Spark.
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Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
2. Introduction to TensorFlow 2
About myself (Matthias Feys)
work at Datatonic:
- Big Data (Dataflow/Spark)
- Machine Learning (TensorFlow/sklearn)
- DataViz (Tableau/Spotfire)
Google Qualified Developer
Contact me:
- @FsMatt
- matthias@datatonic.com
3. Introduction to TensorFlow 3
1. What is TensorFlow?
2. Why would you use it?
3. How does it work? + Demo
4. CloudML (alpha) discussion
Agenda
4. PLACE IMAGE HERE
4
Google TensorFlow
● Originally developed by the Google
Brain Team within Google's Machine
Intelligence research organization
● TensorFlow provides primitives for
defining functions on tensors and
automatically computing their
derivatives.
● An open source software library for
numerical computation using data
flow graphs
TensorFlow
5. 5
Tensor?
Simply put: Tensors can be viewed as a
multidimensional array of numbers.
This means that:
● A scalar is a tensor,
● A vector is a tensor,
● A matrix is a tensor
● ...
6. 6
Data Flow Graph?
● Computations are represented as graphs:
● Nodes are the operations (ops)
● Edges are the Tensors
(multidimensional arrays)
● Typical program consists of 2 phases:
● construction phase: assembling a
graph (model)
● execution phase:
pushing data through the graph
7. 7
Neural Networks? Deep Learning?
● Neural Networks are represented by the lower figure,
not the top one....
● Link:
Tinker with a Neural Network in Your Browser
8. Presentation title (Go to View > Master to edit) 8
Source: https://www.udacity.com/course/deep-learning--ud730
9. Presentation title (Go to View > Master to edit) 9
Source: https://www.udacity.com/course/deep-learning--ud730
10. Presentation title (Go to View > Master to edit) 10
Source: https://www.udacity.com/course/deep-learning--ud730
11. Presentation title (Go to View > Master to edit) 11
Source: https://www.udacity.com/course/deep-learning--ud730
12. Presentation title (Go to View > Master to edit) 12
Source: https://www.udacity.com/course/deep-learning--ud730
13. Presentation title (Go to View > Master to edit) 13
Source: https://www.udacity.com/course/deep-learning--ud730
14. Presentation title (Go to View > Master to edit) 14
Source: https://www.udacity.com/course/deep-learning--ud730
15. Presentation title (Go to View > Master to edit) 15
Source: https://www.udacity.com/course/deep-learning--ud730
16. Presentation title (Go to View > Master to edit) 16
Source: https://www.udacity.com/course/deep-learning--ud730
17. Introduction to TensorFlow 17
1. What is TensorFlow?
2. Why would you use it?
3. How does it work? + Demo
4. CloudML (alpha) discussion
Agenda
18. Introduction to TensorFlow 18
Why would you use NN / Deep Learning?
● Neural Networks (NNs) are universal
function approximators that work very
well with huge datasets
● NNs / deep networks do unsupervised
feature learning
● Track record, being SotA in:
○ image classification,
○ language processing,
○ speech recognition,
○ ...
19. 19
Why TensorFlow?
There are a lot of alternatives:
● Torch
● Caffe
● Theano (Keras, Lasagne)
● CuDNN
● Mxnet
● DSSTNE
● DL4J
● DIANNE
● Etc.
20. Introduction to TensorFlow 20
TensorFlow has the largest community
Sources: http://deliprao.com/archives/168
http://www.slideshare.net/JenAman/large-scale-deep-learning-wit
h-tensorflow
21. Introduction to TensorFlow 21
TensorFlow is very portable/scalable
Runs on CPUs, GPUs, TPUs over one or more
machines, but also on phones(android+iOS)
and raspberry pi’s...
22. Introduction to TensorFlow 22
TensorFlow is more than an R&D project
- Specific functionalities for deployment (TF Serving /
CloudML)
- Easier/more documentation (for more general public)
- Included visualization tool (Tensorboard)
- Simplified interfaces like SKFlow
23. Introduction to TensorFlow 23
1. What is TensorFlow?
2. Why would you use it?
3. How does it work? + Demo
4. CloudML (alpha) discussion
Agenda
24. Introduction to TensorFlow 24
How does it work?
Number Recognition w TF explained (in notebook) Speech classification (demo)
Great starting point:
https://github.com/tensorflow/models
Tensorboard notebook:
here
25. Introduction to TensorFlow 25
Do It Yourself! (in Datalab)
Do It Yourself:
1) Open Cloud Shell
2) Paste these commands:
3) Enter the returned EXTERNAL-IP+”:8080” in your browser
gcloud container clusters create datalab-cluster --machine-type n1-standard-4
--num-nodes 1 --zone europe-west1-d
kubectl run datalab --image=gcr.io/cloud-datalab/datalab:mlbeta2 --port=8080
kubectl expose deployment datalab --type="LoadBalancer"
kubectl get service datalab
26. Introduction to TensorFlow 26
1. What is TensorFlow?
2. Why would you use it?
3. How does it work? + Demo
4. CloudML (alpha) discussion
Agenda
28. Introduction to TensorFlow 28
- Curated list of TF resources: https://github.com/jtoy/awesome-tensorflow
- Models implemented in TF: https://github.com/tensorflow/models
- Slides “TF tricks of the trade”: https://drive.google.com/open?id=x_...
- Slides “TF and Deep Learning without a PhD”: https://docs.google.com/presentation/d/...
- Blogpost “DL with spark and TF”: https://databricks.com/blog/...
- The official documentation: https://www.tensorflow.org/versions/r0.10/...
Join: https://www.meetup.com/TensorFlow-Belgium
Further reading