This slides explains how Convolution Neural Networks can be coded using Google TensorFlow.
Video available at : https://www.youtube.com/watch?v=EoysuTMmmMc
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.
TensorFlow에 대한 분석 내용
- TensorFlow?
- 배경
- DistBelief
- Tutorial - Logistic regression
- TensorFlow - 내부적으로는
- Tutorial - CNN, RNN
- Benchmarks
- 다른 오픈 소스들
- TensorFlow를 고려한다면
- 설치
- 참고 자료
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
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.
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.
TensorFlow에 대한 분석 내용
- TensorFlow?
- 배경
- DistBelief
- Tutorial - Logistic regression
- TensorFlow - 내부적으로는
- Tutorial - CNN, RNN
- Benchmarks
- 다른 오픈 소스들
- TensorFlow를 고려한다면
- 설치
- 참고 자료
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
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.
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.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...Simplilearn
This presentation on Convolutional neural network tutorial (CNN) will help you understand what is a convolutional neural network, hoe CNN recognizes images, what are layers in the convolutional neural network and at the end, you will see a use case implementation using CNN. CNN is a feed forward neural network that is generally used to analyze visual images by processing data with grid like topology. A CNN is also known as a "ConvNet". Convolutional networks can also perform optical character recognition to digitize text and make natural-language processing possible on analog and hand-written documents. CNNs can also be applied to sound when it is represented visually as a spectrogram. Now, lets deep dive into this presentation to understand what is CNN and how do they actually work.
Below topics are explained in this CNN presentation(Convolutional Neural Network presentation)
1. Introduction to CNN
2. What is a convolutional neural network?
3. How CNN recognizes images?
4. Layers in convolutional neural network
5. Use case implementation using CNN
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.
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. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
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:
Learn more at: https://www.simplilearn.com/
** AI & Deep Learning Using TensorFlow - https://www.edureka.co/ai-deep-learning-with-tensorflow **
This Edureka tutorial will provide you with a detailed and comprehensive knowledge of TensorFlow Object detection and how it works. It will also provide you with the details on how to use Tensorflow to detect objects in deep learning method. Below are the topics covered in this tutorial:
1. What is Object Detection?
2. Industrial use of Object Detection
3. Object Detection Workflow
4. What is Tensorflow?
5. Object Detection using Tensorflow - Demo
6. Live Object Detection using Tensorflow- Demo
Presentation on Neural Networks in Tensorflow. Code available at https://github.com/nfmcclure/tensorflow_cookbook . Presentation for Open Source Bridge, Portland, 2016.
Python is dominating the fast-growing data-science landscape. This talk provides a foundational overview of the practice of data science and some of the most popular Python libraries for doing data science. It also provides an overview of how Anaconda brings it all together.
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.
classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects.we'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded.
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).
Learn the fundamentals of Deep Learning, Machine Learning, and AI, how they've impacted everyday technology, and what's coming next in Artificial Intelligence technology.
Introducing TensorFlow: The game changer in building "intelligent" applicationsRokesh Jankie
This is the slidedeck used for the presentation of the Amsterdam Pipeline of Data Science, held in December 2016. TensorFlow in the open source library from Google to implement deep learning, neural networks. This is an introduction to Tensorflow.
Note: Videos are not included (which were shown during the presentation)
On-device machine learning: TensorFlow on AndroidYufeng Guo
Machine learning has traditionally been the solely performed on servers and high performance machines. But there is great value is having on-device machine learning for mobile devices. Doing ML inference on mobile devices has huge potential and is still in its early stages. However, it's already more powerful than most realize.
In this demo-oriented talk, you will see some examples of deep learning models used for local prediction on mobile devices. Learn how to use TensorFlow to implement a machine learning model that is tailored to a custom dataset, and start making delightful experiences today!
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.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...Simplilearn
This presentation on Convolutional neural network tutorial (CNN) will help you understand what is a convolutional neural network, hoe CNN recognizes images, what are layers in the convolutional neural network and at the end, you will see a use case implementation using CNN. CNN is a feed forward neural network that is generally used to analyze visual images by processing data with grid like topology. A CNN is also known as a "ConvNet". Convolutional networks can also perform optical character recognition to digitize text and make natural-language processing possible on analog and hand-written documents. CNNs can also be applied to sound when it is represented visually as a spectrogram. Now, lets deep dive into this presentation to understand what is CNN and how do they actually work.
Below topics are explained in this CNN presentation(Convolutional Neural Network presentation)
1. Introduction to CNN
2. What is a convolutional neural network?
3. How CNN recognizes images?
4. Layers in convolutional neural network
5. Use case implementation using CNN
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.
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. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
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:
Learn more at: https://www.simplilearn.com/
** AI & Deep Learning Using TensorFlow - https://www.edureka.co/ai-deep-learning-with-tensorflow **
This Edureka tutorial will provide you with a detailed and comprehensive knowledge of TensorFlow Object detection and how it works. It will also provide you with the details on how to use Tensorflow to detect objects in deep learning method. Below are the topics covered in this tutorial:
1. What is Object Detection?
2. Industrial use of Object Detection
3. Object Detection Workflow
4. What is Tensorflow?
5. Object Detection using Tensorflow - Demo
6. Live Object Detection using Tensorflow- Demo
Presentation on Neural Networks in Tensorflow. Code available at https://github.com/nfmcclure/tensorflow_cookbook . Presentation for Open Source Bridge, Portland, 2016.
Python is dominating the fast-growing data-science landscape. This talk provides a foundational overview of the practice of data science and some of the most popular Python libraries for doing data science. It also provides an overview of how Anaconda brings it all together.
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.
classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects.we'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded.
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).
Learn the fundamentals of Deep Learning, Machine Learning, and AI, how they've impacted everyday technology, and what's coming next in Artificial Intelligence technology.
Introducing TensorFlow: The game changer in building "intelligent" applicationsRokesh Jankie
This is the slidedeck used for the presentation of the Amsterdam Pipeline of Data Science, held in December 2016. TensorFlow in the open source library from Google to implement deep learning, neural networks. This is an introduction to Tensorflow.
Note: Videos are not included (which were shown during the presentation)
On-device machine learning: TensorFlow on AndroidYufeng Guo
Machine learning has traditionally been the solely performed on servers and high performance machines. But there is great value is having on-device machine learning for mobile devices. Doing ML inference on mobile devices has huge potential and is still in its early stages. However, it's already more powerful than most realize.
In this demo-oriented talk, you will see some examples of deep learning models used for local prediction on mobile devices. Learn how to use TensorFlow to implement a machine learning model that is tailored to a custom dataset, and start making delightful experiences today!
Deep Learning for Data Scientists - Data Science ATL Meetup Presentation, 201...Andrew Gardner
Note: these are the slides from a presentation at Lexis Nexis in Alpharetta, GA, on 2014-01-08 as part of the DataScienceATL Meetup. A video of this talk from Dec 2013 is available on vimeo at http://bit.ly/1aJ6xlt
Note: Slideshare mis-converted the images in slides 16-17. Expect a fix in the next couple of days.
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Deep learning is a hot area of machine learning named one of the "Breakthrough Technologies of 2013" by MIT Technology Review. The basic ideas extend neural network research from past decades and incorporate new discoveries in statistical machine learning and neuroscience. The results are new learning architectures and algorithms that promise disruptive advances in automatic feature engineering, pattern discovery, data modeling and artificial intelligence. Empirical results from real world applications and benchmarking routinely demonstrate state-of-the-art performance across diverse problems including: speech recognition, object detection, image understanding and machine translation. The technology is employed commercially today, notably in many popular Google products such as Street View, Google+ Image Search and Android Voice Recognition.
In this talk, we will present an overview of deep learning for data scientists: what it is, how it works, what it can do, and why it is important. We will review several real world applications and discuss some of the key hurdles to mainstream adoption. We will conclude by discussing our experiences implementing and running deep learning experiments on our own hardware data science appliance.
Learn about Tensorflow for Deep Learning now! Part 1Tyrone Systems
In this comprehensive workshop, learn how to use TensorFlow, how to build data pipelines and implement a simple deep learning model using Tensorflow Keras. Enhance your knowledge and skills by have better understanding of Tensorflow with all the resources we have available for you!
Overview of TensorFlow For Natural Language Processingananth
TensorFlow open sourced recently by Google is one of the key frameworks that support development of deep learning architectures. In this slideset, part 1, we get started with a few basic primitives of TensorFlow. We will also discuss when and when not to use TensorFlow.
Workshop about TensorFlow usage for AI Ukraine 2016. Brief tutorial with source code example. Described TensorFlow main ideas, terms, parameters. Example related with linear neuron model and learning using Adam optimization algorithm.
I have conducted a workshop on Tensorflow2.0 at Facebook Dev CIrcle. This mostly covers the importance of TensorFlow to implement deep neural networks.
You can check the related demo at:
https://github.com/rayyan17/Introduction-To-Tensor-Flow.git
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.
Artificial Intelligence and Deep Learning in Azure, CNTK and TensorflowJen Stirrup
Artificial Intelligence and Deep Learning in Azure, using Open Source technologies CNTK and Tensorflow. The tutorial can be found on GitHub here: https://github.com/Microsoft/CNTK/tree/master/Tutorials
and the CNTK video can be found here: https://youtu.be/qgwaP43ZIwA
An Introduction to TensorFlow architectureMani Goswami
Introduces you to the internals of TensorFlow and deep dives into distributed version of TensorFlow. Refer to https://github.com/manigoswami/tensorflow-examples for examples.
Natural language processing open seminar For Tensorflow usagehyunyoung Lee
This is presentation for Natural Language Processing open seminar in Kookmin University.
The open seminar reference : https://cafe.naver.com/nlpk
My presentation about how to use tensorflow for NLP open seminar for newbies for tensorflow.
Separating Hype from Reality in Deep Learning with Sameer FarooquiDatabricks
Deep Learning is all the rage these days, but where does the reality of what Deep Learning can do end and the media hype begin? In this talk, I will dispel common myths about Deep Learning that are not necessarily true and help you decide whether you should practically use Deep Learning in your software stack.
I’ll begin with a technical overview of common neural network architectures like CNNs, RNNs, GANs and their common use cases like computer vision, language understanding or unsupervised machine learning. Then I’ll separate the hype from reality around questions like:
• When should you prefer traditional ML systems like scikit learn or Spark.ML instead of Deep Learning?
• Do you no longer need to do careful feature extraction and standardization if using Deep Learning?
• Do you really need terabytes of data when training neural networks or can you ‘steal’ pre-trained lower layers from public models by using transfer learning?
• How do you decide which activation function (like ReLU, leaky ReLU, ELU, etc) or optimizer (like Momentum, AdaGrad, RMSProp, Adam, etc) to use in your neural network?
• Should you randomly initialize the weights in your network or use more advanced strategies like Xavier or He initialization?
• How easy is it to overfit/overtrain a neural network and what are the common techniques to ovoid overfitting (like l1/l2 regularization, dropout and early stopping)?
Automatic Attendace using convolutional neural network Face Recognitionvatsal199567
Automatic Attendance System will recognize the face of the student through the camera in the class and mark the attendance. It was built in Python with Machine Learning.
Similar to Neural Networks with Google TensorFlow (20)
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
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.
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.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
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.
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
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.
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.
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.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
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Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
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Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
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DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
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Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
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13. • Mathematical Definition:
A function derived from two given functions by integration that expresses how the shape
of one is modified by the other
What is Convolution?
17. • ConvNet architectures make the explicit assumption that the inputs
are images, which allows us to encode certain properties into the
architecture.
• This assumption makes the forward function more efficient to
implement and vastly reduces the amount of parameters in the
network.
How CNN/ConvNets is different?
18. Cont. How CNN/ConvNets is different?
Source : http://cs231n.github.io/convolutional-networks
43. • A ConvNet architecture is a list of Layers that transform the image volume
into an output volume (e.g. holding the class scores)
• There are a few distinct types of Layers (e.g. CONV/FC/RELU/POOL are by far
the most popular)
• Each Layer accepts an input 3D volume and transforms it to an output 3D
volume through a differentiable function
• Each Layer may or may not have parameters (e.g. CONV/FC do, RELU/POOL
don't)
ConvNets Summary
44.
45.
46. • Second generation Machine Learning system, followed by DistBelief
• TensorFlow grew out of a project at Google, called Google Brain, aimed at applying various
kinds of neural network machine learning to products and services across the company.
• An open source software library for numerical computation using data flow graphs
• Used in following projects at Google
1. DeepDream
2. RankBrain
3. Smart Reply
And many more..
Google TensorFlow
47. Data Flow Graph
• Data flow graphs describe mathematical computation
with a directed graph of nodes & edges
• Nodes in the graph represent mathematical operations.
• Edges represent the multidimensional data arrays
(tensors) communicated between them.
• Edges describe the input/output relationships between
nodes.
• The flow of tensors through the graph is where
TensorFlow gets its name.
48. Google TensorFlow Basic Elements
• Tensor
• Variable
• Operation
• Session
• Placeholder
• TensorBoard
49. • TensorFlow programs use a tensor data structure to represent all data
• Think of a TensorFlow tensor as an n-dimensional array or list
In the following example, c, d and e are symbolic Tensor Objects, where as result is a
numpy array
Tensor
50. 1. Constant Value Tensors
2. Sequences
3. Random Tensors
Tensor Types
54. • In-memory buffers containing tensors
• Initial value defines the type and shape of the variable.
• They must be explicitly initialized and can be saved to disk during and after
training.
Variable
55. • An Operation is a node in a TensorFlow Graph
• Takes zero or more Tensor objects as input, and produces zero or
more Tensor objects as output.
• Example:
c = tf.matmul(a, b)
Creates an Operation of type "MatMul" that takes tensors a and b as input,
and produces c as output.
Operation
56. • A class for running TensorFlow operations
• InteractiveSession is a TensorFlow Session for use in interactive contexts, such as
a shell and Ipython notebook.
Session & Interactive Session
57. • A value that we'll input when we ask TensorFlow to run a computation.
Placeholder
67. First Convolution Layer including ReLU
It will consist of convolution, followed by max pooling
Filter/Patch Dimension
Number of Input Channels
Number of Output Channel
Number of Output Channel
69. Fully Connected Layer
• Reshape the tensor from the pooling layer into a batch of vectors
• Multiply by a weight matrix, add a bias, and apply a ReLU
70. Dropout
• To reduce over fitting, we will apply dropout before the readout layer.
• Dropout is an extremely effective, simple and recently introduced regularization technique by
Srivastava et al. in “Dropout: A Simple Way to Prevent Neural Networks from Overfitting” that
complements the other methods (L1, L2, maxnorm).
Source : http://cs231n.github.io/neural-networks-2/
71. Dropout
• We create a placeholder for the probability that a neuron's output is kept during dropout.
• This allows us to turn dropout on during training, and turn it off during testing.
• While training, dropout is implemented by only keeping a neuron active with some
probability pp (a hyperparameter)
72. Readout Layer
• Finally, we add a softmax layer, just like for the one layer softmax regression.
73. Train and Evaluate the Model
Initialize
All
Variables
Training
Accuracy
Testing
Optimizer
Loss Function
74. • TensorBoard operates by reading TensorFlow events files, which contain
summary data that you can generate when running TensorFlow.
TensorBoard
75. • TensorBoard operates by reading TensorFlow events files, which contain
summary data that you can generate when running TensorFlow.
• First, create the TensorFlow graph that we'd like to collect summary data
from, and decide which nodes should be annotated with summary operation.
• For example,
• For MNIST digits CNNs, we'd like to record how the learning rate varies over time, and how
the objective function is changing
• We’d like to record distribution of gradients or weights
TensorBoard