Flutter + tensor flow lite = awesome sauce
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This document provides an overview of machine learning concepts and the TensorFlow environment. It discusses machine learning algorithms categorized by learning style and type. Deep learning frameworks like TensorFlow and Keras are introduced. Transfer learning and converting models to TensorFlow Lite format for mobile deployment are covered. Finally, the document discusses Flutter app architecture and how to integrate a machine learning model into a Flutter mobile app.
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TensorFlow Lite for mobile & IoT
The content was modified from Google Content Group
Eric ShangKuan(ericsk@google.com)
---
TensorFlow Lite guide( for mobile & IoT )
TensorFlow Lite is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and small binary size.
TensorFlow Lite consists of two main components:
The TensorFlow Lite interpreter:
- optimize models on many different hardware types, like mobile phones, embedded Linux devices, and microcontrollers.
The TensorFlow Lite converter:
- which converts TensorFlow models into an efficient form for use by the interpreter, and can introduce optimizations to improve binary size and performance.
---
Event: PyLadies TensorFlow All-Around
Date: Sep 25, 2019
Event link: https://www.meetup.com/PyLadies-Berlin/events/264205538/
Linkedin: http://linkedin.com/in/mia-chang/
Machine Learning on Your Hand - Introduction to Tensorflow Lite Preview
TF Dev Summit × Modulabs : Learn by Run !
Machine Learning on Your Hand - Introduction to Tensorflow Lite Preview (발표자 : 강재욱)
※ 모두의연구소 페이지 : https://www.facebook.com/lab4all/
※ 모두의연구소 커뮤니티 그룹 : https://www.facebook.com/groups/modulabs
Introduction to Keras
An introduction to Keras, a high-level neural networks library written in Python. Keras makes deep learning more accessible, is fantastic for rapid protyping, and can run on top of TensorFlow, Theano, or CNTK. These slides focus on examples, starting with logistic regression and building towards a convolutional neural network.
The presentation was given at the Austin Deep Learning meetup: https://www.meetup.com/Austin-Deep-Learning/events/237661902/
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TensorFlow and Keras are popular deep learning frameworks. TensorFlow is an open source library for numerical computation using data flow graphs. It was developed by Google and is widely used for machine learning and deep learning. Keras is a higher-level neural network API that can run on top of TensorFlow. It focuses on user-friendliness, modularization and extensibility. Both frameworks make building and training neural networks easier through modular layers and built-in optimization algorithms.
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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.
NLP using transformers
This document discusses neural network models for natural language processing tasks like machine translation. It describes how recurrent neural networks (RNNs) were used initially but had limitations in capturing long-term dependencies and parallelization. The encoder-decoder framework addressed some issues but still lost context. Attention mechanisms allowed focusing on relevant parts of the input and using all encoded states. Transformers replaced RNNs entirely with self-attention and encoder-decoder attention, allowing parallelization while generating a richer representation capturing word relationships. This revolutionized NLP tasks like machine translation.
“Accelerating Newer ML Models Using the Qualcomm AI Stack,” a Presentation fr...
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Recommended
TensorFlow Lite for mobile & IoT
The content was modified from Google Content Group
Eric ShangKuan(ericsk@google.com)
---
TensorFlow Lite guide( for mobile & IoT )
TensorFlow Lite is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and small binary size.
TensorFlow Lite consists of two main components:
The TensorFlow Lite interpreter:
- optimize models on many different hardware types, like mobile phones, embedded Linux devices, and microcontrollers.
The TensorFlow Lite converter:
- which converts TensorFlow models into an efficient form for use by the interpreter, and can introduce optimizations to improve binary size and performance.
---
Event: PyLadies TensorFlow All-Around
Date: Sep 25, 2019
Event link: https://www.meetup.com/PyLadies-Berlin/events/264205538/
Linkedin: http://linkedin.com/in/mia-chang/
Machine Learning on Your Hand - Introduction to Tensorflow Lite Preview
TF Dev Summit × Modulabs : Learn by Run !
Machine Learning on Your Hand - Introduction to Tensorflow Lite Preview (발표자 : 강재욱)
※ 모두의연구소 페이지 : https://www.facebook.com/lab4all/
※ 모두의연구소 커뮤니티 그룹 : https://www.facebook.com/groups/modulabs
Introduction to Keras
An introduction to Keras, a high-level neural networks library written in Python. Keras makes deep learning more accessible, is fantastic for rapid protyping, and can run on top of TensorFlow, Theano, or CNTK. These slides focus on examples, starting with logistic regression and building towards a convolutional neural network.
The presentation was given at the Austin Deep Learning meetup: https://www.meetup.com/Austin-Deep-Learning/events/237661902/
TensorFlow and Keras: An Overview
TensorFlow and Keras are popular deep learning frameworks. TensorFlow is an open source library for numerical computation using data flow graphs. It was developed by Google and is widely used for machine learning and deep learning. Keras is a higher-level neural network API that can run on top of TensorFlow. It focuses on user-friendliness, modularization and extensibility. Both frameworks make building and training neural networks easier through modular layers and built-in optimization algorithms.
Introduction to TensorFlow 2.0
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.
NLP using transformers
This document discusses neural network models for natural language processing tasks like machine translation. It describes how recurrent neural networks (RNNs) were used initially but had limitations in capturing long-term dependencies and parallelization. The encoder-decoder framework addressed some issues but still lost context. Attention mechanisms allowed focusing on relevant parts of the input and using all encoded states. Transformers replaced RNNs entirely with self-attention and encoder-decoder attention, allowing parallelization while generating a richer representation capturing word relationships. This revolutionized NLP tasks like machine translation.
“Accelerating Newer ML Models Using the Qualcomm AI Stack,” a Presentation fr...
“Accelerating Newer ML Models Using the Qualcomm AI Stack,” a Presentation fr...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/06/accelerating-newer-ml-models-using-the-qualcomm-ai-stack-a-presentation-from-qualcomm/
Vinesh Sukumar, Senior Director and Head of AI/ML Product Management at Qualcomm Technologies, presents the “Accelerating Newer ML Models Using the Qualcomm AI Stack” tutorial at the May 2023 Embedded Vision Summit.
The Qualcomm AI Stack revolutionizes how Qualcomm thinks about AI software and provides the ultimate tool and user interface to enable ecosystem partners to create faster and smarter AI applications for all embedded form factors. Focusing on real user experience challenges centered around model deployment, Sakumar explains how the Snapdragon developer community leverages data types, quantization and neural architecture search—among others—to optimize complex AI architectures for emerging use cases.Transformer in Vision
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[2018 ICML] Image Transformer
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Description:
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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.
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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.
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Prof. PhD. Piek Th.J.M. Vossen, is Full professor of Computational Lexicology at the Faculty of Humanities, Department of Language, Literature and Communication (LCC) at VU Amsterdam:
What is ChatGPT? What technology and thought processes underlie it? What are its consequences? What choices are being made? In the presentation, Piek will elaborate on the basic principles behind Large Language Models and how they are used as a basis for Deep Learning in which they are fine-tuned for specific tasks. He will also discuss a specific variant GPT that underlies ChatGPT. It covers what ChatGPT can and cannot do, what it is good for and what the risks are.
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Check out our Deep Learning blog series: https://bit.ly/2xVIMe1
Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
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Introduction to TensorFlow
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.
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A talk on NVIDIA's capability for sustainable accelerated computing utilising simulation for real world prototyping & development.
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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.
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with Hugging Face Transformers
The document discusses how transformers have become a general-purpose architecture for machine learning, with various transformer models like BERT and GPT-3 seeing widespread adoption. It introduces Hugging Face as a company working to make transformers more accessible through tools and libraries. Hugging Face has seen rapid growth, with its hub hosting over 73,000 models and 10,000 datasets that are downloaded over 1 million times daily. The document outlines Hugging Face's vision of facilitating the entire machine learning process from data to production through tools that support tasks like transfer learning, hardware acceleration, and collaborative model development.
Machine Learning - Convolutional Neural Network
The document provides an overview of convolutional neural networks (CNNs) for visual recognition. It discusses the basic concepts of CNNs such as convolutional layers, activation functions, pooling layers, and network architectures. Examples of classic CNN architectures like LeNet-5 and AlexNet are presented. Modern architectures such as Inception and ResNet are also discussed. Code examples for image classification using TensorFlow, Keras, and Fastai are provided.
Jupyter, A Platform for Data Science at Scale
This document discusses Jupyter, an open-source tool for interactive data science and scientific computing. Jupyter allows for interactive exploration, development, and communication through code, equations, visualizations and narrative text. It supports over 50 programming languages and has found widespread adoption in academia and industry for individual and collaborative work across the entire workflow of a scientific idea from data collection to publication. The document outlines Jupyter's history and architecture, ecosystem of related projects, and future development plans to enhance collaboration and software engineering capabilities.
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Link to Google Slides version: https://docs.google.com/presentation/d/1N7ayCRqgsFO7TqSjN4OWW-dMOQPT5DZcHXsZvw8-6FU/edit?usp=sharing
Introduction to Deep Learning (NVIDIA)
NVIDIA compute GPUs and software toolkits are key drivers behind major advancements in machine learning. Of particular interest is a technique called "deep learning", which utilizes what are known as Convolution Neural Networks (CNNs) having landslide success in computer vision and widespread adoption in a variety of fields such as autonomous vehicles, cyber security, and healthcare. In this talk is presented a high level introduction to deep learning where we discuss core concepts, success stories, and relevant use cases. Additionally, we will provide an overview of essential frameworks and workflows for deep learning. Finally, we explore emerging domains for GPU computing such as large-scale graph analytics, in-memory databases.
https://tech.rakuten.co.jp/
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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).
Pre trained language model
The document discusses recent developments in pre-trained language models including ELMO, ULMFiT, BERT, and GPT-2. It provides overviews of the core structures and implementations of each model, noting that they have achieved great performance on natural language tasks without requiring labeled data for pre-training, similar to how pre-training helps in computer vision tasks. The document also includes a comparison chart of the types of natural language tasks each model can perform.
And then there were ... Large Language Models
It is not often even in the ICT world that one witnesses a revolution. The rise of the Personal Computer, the rise of mobile telephony and, of course, the rise of the Internet are some of those revolutions. So what is ChatGPT really? Is ChatGPT also such a revolution? And like any revolution, does ChatGPT have its winners and losers? And who are they? How do we ensure that ChatGPT contributes to a positive impulse for "Smart Humanity?".
During a key note om April 3 and 13 2023 Piek Vossen explained the impact of Large Language Models like ChatGPT.
Prof. PhD. Piek Th.J.M. Vossen, is Full professor of Computational Lexicology at the Faculty of Humanities, Department of Language, Literature and Communication (LCC) at VU Amsterdam:
What is ChatGPT? What technology and thought processes underlie it? What are its consequences? What choices are being made? In the presentation, Piek will elaborate on the basic principles behind Large Language Models and how they are used as a basis for Deep Learning in which they are fine-tuned for specific tasks. He will also discuss a specific variant GPT that underlies ChatGPT. It covers what ChatGPT can and cannot do, what it is good for and what the risks are.
Introduction to TensorFlow Lite
TensorFlow Lite is TensorFlow's lightweight solution for running machine learning models on mobile and embedded devices. It provides optimized operations for low latency and small binary size on these devices. TensorFlow Lite supports hardware acceleration using the Android Neural Networks API and contains a set of core operators, a new FlatBuffers-based model format, and a mobile-optimized interpreter. It allows converting models trained in TensorFlow to the TFLite format and running them efficiently on mobile.
Attention Is All You Need
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.
Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.
ViT (Vision Transformer) Review [CDM]
Review : An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Paper Link : https://openreview.net/forum?id=YicbFdNTTy
Cnn
Convolutional neural networks (CNNs) learn multi-level features and perform classification jointly and better than traditional approaches for image classification and segmentation problems. CNNs have four main components: convolution, nonlinearity, pooling, and fully connected layers. Convolution extracts features from the input image using filters. Nonlinearity introduces nonlinearity. Pooling reduces dimensionality while retaining important information. The fully connected layer uses high-level features for classification. CNNs are trained end-to-end using backpropagation to minimize output errors by updating weights.
Introduction To TensorFlow
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.
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Landscape of AI/ML in 2023
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Large Language Models - From RNN to BERT
The document discusses the evolution of language models from RNN to BERT. It begins with an overview of language models and how they estimate probabilities of word sequences. Neural language models using RNNs were developed to address limitations of N-gram models by allowing unlimited context, but training RNNs was difficult. ELMo used a bidirectional LSTM to learn deep contextual word representations in a two-stage training process. Transformers replaced RNNs with self-attention and were highly parallelizable. A milestone was BERT, which combined the benefits of ELMo (deep bidirectional context) and Transformers (attention mechanism).
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** AI & Deep Learning Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka Tutorial on "Keras Tutorial" (Deep Learning Blog Series: https://goo.gl/4zxMfU) provides you a quick and insightful tutorial on the working of Keras along with an interesting use-case! We will be checking out the following topics:
Agenda:
What is Keras?
Who makes Keras?
Who uses Keras?
What Makes Keras special?
Working principle of Keras
Keras Models
Understanding Execution
Implementing a Neural Network
Use-Case with Keras
Coding in Colaboratory
Session in a minute
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Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz
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Deep Dive into Apache MXNet on AWS
This document provides an overview of Apache MXNet and deep learning on AWS. It begins with an introduction to deep learning applications and trends. The rest of the document discusses MXNet features like scalability, language support and frameworks comparisons. It also covers MXNet usage on AWS like integration with services and AI research. The document concludes with developer resources like notebooks, documentation and tools for building models with MXNet.
Machine Learning on the Cloud with Apache MXNet
An overview of artificial intelligence, machine learning, and how to tackle some of these problems on AWS. Some specifics given on Apache MXNet
Scalable Deep Learning on AWS Using Apache MXNet - AWS Summit Tel Aviv 2017
Artificial Intelligence (AI) and deep learning are now ready to power your business, as it is powering most of the innovation of Amazon.com with autonomous drones, and robots, Amazon Alexa, Amazon Go, and many other hard and important business problems. Come and learn why and how to get started with deep learning, and what you can expect from a future with better AI in the cloud and on the edge.
Intro to Scalable Deep Learning on AWS with Apache MXNet
Apache MXNet is an open-source deep learning framework that can scale efficiently across multiple GPUs and machines. The document introduces MXNet and compares it to other frameworks, discusses its use of imperative and symbolic programming, and provides an example using MXNet with a Jupyter notebook. It also outlines Amazon and Intel's collaborations using MXNet for deep learning workloads on AWS.
Intro to Deep Learning with Tensorflow - DevDay Los Angeles 2017
This document provides an introduction to deep learning on AWS using TensorFlow. It discusses why deep learning, why now, and why cloud computing and AWS specifically. It then discusses why TensorFlow and Keras are good options. It provides examples of neural style transfer and applications at companies like Netflix and Pinterest. It discusses Amazon's investments in artificial intelligence and machine learning. Finally, it outlines Amazon's AI services and discusses getting started with a code walkthrough of MNIST using Keras on TensorFlow.
Hands-on Learning with KubeFlow + Keras/TensorFlow 2.0 + TF Extended (TFX) + ...
Title
Hands-on Learning with KubeFlow + Keras/TensorFlow 2.0 + TF Extended (TFX) + Kubernetes + PyTorch + XGBoost + Airflow + MLflow + Spark + Jupyter + TPU
Video
https://youtu.be/vaB4IM6ySD0
Description
In this workshop, we build real-world machine learning pipelines using TensorFlow Extended (TFX), KubeFlow, and Airflow.
Described in the 2017 paper, TFX is used internally by thousands of Google data scientists and engineers across every major product line within Google.
KubeFlow is a modern, end-to-end pipeline orchestration framework that embraces the latest AI best practices including hyper-parameter tuning, distributed model training, and model tracking.
Airflow is the most-widely used pipeline orchestration framework in machine learning.
Pre-requisites
Modern browser - and that's it!
Every attendee will receive a cloud instance
Nothing will be installed on your local laptop
Everything can be downloaded at the end of the workshop
Location
Online Workshop
Agenda
1. Create a Kubernetes cluster
2. Install KubeFlow, Airflow, TFX, and Jupyter
3. Setup ML Training Pipelines with KubeFlow and Airflow
4. Transform Data with TFX Transform
5. Validate Training Data with TFX Data Validation
6. Train Models with Jupyter, Keras/TensorFlow 2.0, PyTorch, XGBoost, and KubeFlow
7. Run a Notebook Directly on Kubernetes Cluster with KubeFlow
8. Analyze Models using TFX Model Analysis and Jupyter
9. Perform Hyper-Parameter Tuning with KubeFlow
10. Select the Best Model using KubeFlow Experiment Tracking
11. Reproduce Model Training with TFX Metadata Store and Pachyderm
12. Deploy the Model to Production with TensorFlow Serving and Istio
13. Save and Download your Workspace
Key Takeaways
Attendees will gain experience training, analyzing, and serving real-world Keras/TensorFlow 2.0 models in production using model frameworks and open-source tools.
Related Links
1. PipelineAI Home: https://pipeline.ai
2. PipelineAI Community Edition: http://community.pipeline.ai
3. PipelineAI GitHub: https://github.com/PipelineAI/pipeline
4. Advanced Spark and TensorFlow Meetup (SF-based, Global Reach): https://www.meetup.com/Advanced-Spark-and-TensorFlow-Meetup
5. YouTube Videos: https://youtube.pipeline.ai
6. SlideShare Presentations: https://slideshare.pipeline.ai
7. Slack Support: https://joinslack.pipeline.ai
8. Web Support and Knowledge Base: https://support.pipeline.ai
9. Email Support: support@pipeline.ai
2018 11 14 Artificial Intelligence and Machine Learning in Azure
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Scalable Deep Learning on AWS with Apache MXNet
The document discusses deep learning and Apache MXNet. It provides an overview of deep learning applications, describes how Apache MXNet works and its advantages over other frameworks. It also demonstrates MXNet through code examples for training a neural network on MNIST data and performing object detection with a Raspberry Pi. Resources for learning more about MXNet on AWS are provided.
Introduction to Machine Learning with TensorFlow
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Introduction To TensorFlow | Deep Learning with TensorFlow | TensorFlow For B...
** AI & Deep Learning with Tensorflow Training: https://goo.gl/vDxgi5 **
This Edureka tutorial on "Introduction to TensorFlow" provides you an insight into one of the top Deep Learning frameworks that you should consider learning!
Check out our Deep Learning blog series: https://bit.ly/2xVIMe1
Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz
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Lecture 4: Deep Learning Frameworks
The document provides information on several popular deep learning frameworks: TensorFlow, Caffe, Theano, Torch, CNTK, and Keras. It describes each framework's creator, license, programming languages supported, and brief purpose or use. TensorFlow is noted as the most popular framework, created by Google for machine learning research. Caffe is described as the fastest, Theano as most efficient, Torch is used by Facebook AI, CNTK for high scalability, and Keras for easy experimentation across frameworks. The document also provides examples of building and running computational graphs in TensorFlow.
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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
Microsoft AI Platform Overview
Microsoft provides an AI platform and tools for developers to build, train, and deploy intelligent applications and services. Key elements of Microsoft's AI offerings include:
- A unified AI platform spanning infrastructure, tools, and services to make AI accessible and useful for every developer.
- Powerful tools for AI development including deep learning frameworks, coding and management tools, and AI services for tasks like computer vision, natural language processing, and more.
- Capabilities for training models at scale using GPU accelerated compute on Azure and deploying trained models as web APIs, mobile apps, or other applications.
- A focus on trusted, responsible, and inclusive AI that puts users in control and augments rather than replaces human
A Deeper Dive into Apache MXNet - March 2017 AWS Online Tech Talks
This document provides an overview and agenda for a webinar on Apache MXNet for deep learning. The webinar will include an introduction to MXNet, a demonstration of distributed deep learning with AWS CloudFormation using MXNet, and an example of training a neural network to classify handwritten digits using MXNet in Python. MXNet is an open source framework that supports deep learning workloads across multiple languages and devices, with high performance and scalability across hundreds of GPUs. The webinar will also discuss popular deep learning applications and services available on AWS.
A Deeper Dive into Apache MXNet - March 2017 AWS Online Tech Talks
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. Apache MXNet is a fully-featured, flexibly-programmable and ultra-scalable deep learning framework supporting innovative deep models including convolutional neural networks (CNNs), and long short-term memory networks (LSTMs). This Tech Talk will show you how to launch the deep learning cloud formation template and deploy the deep learning AMI to train your own deep neural network, using MNIST, to recognize handwritten digits and test it for accuracy.
Learning Objectives:
- Learn about the features and benefits of Apache MXNet
- Learn about the deep learning AMIs with the tools you need for DL
- Learn how to train a neural network using MXNet
Tensorflow Ecosystem
The document summarizes the TensorFlow ecosystem. It discusses TensorFlow's data processing, model building, training, deployment, and tooling capabilities. It highlights improvements in TensorFlow 2.x like eager execution by default, tight Keras integration, and support for distributed training. The document also discusses how TensorFlow empowers responsible AI through initiatives like privacy research, model cards, and collaborative tools to improve model performance and transparency.
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Artificial intelligence in the cloud can be used to build smart applications. AWS provides AI services like Amazon Rekognition for image recognition, Amazon Polly for text-to-speech, and Amazon Lex for conversational interfaces. These services make AI more accessible and help developers build applications across industries like autonomous vehicles, recommendations, translations and more.
Track2 02. machine intelligence at google scale google, kaz sato, staff devel...
Machine Intelligence at Google Scale
1) Google uses neural networks and deep learning across many of its services like Search, Photos, Translate, and Android apps. 2) Google provides external access to machine learning through APIs like Cloud Vision, Speech, Translation and Natural Language that allow developers to easily integrate ML into applications. 3) TensorFlow is Google's open source library for machine learning that makes it easy to design, train and deploy models at scale. 4) Google trains models using distributed processing on thousands of GPUs in its datacenters and also provides Cloud ML to allow external users to train models in the cloud.
AWS 機器學習 II ─ 深度學習 Deep Learning & MXNet
講師: Jhen-Wei Huang, Solution Architect, AWS
Artificial Intelligence (AI) and deep learning are now ready to power your business, as it is powering most of the innovation of Amazon.com with autonomous drones, and robots, Amazon Alexa, Amazon Go, and many other hard and important business problems. Come and learn why and how to get started with deep learning, and what you can expect from a future with better AI in the cloud and on the edge.
Deep Dive on Deep Learning (June 2018)
This document provides a summary of a presentation on deep learning concepts, common architectures, Apache MXNet, and infrastructure for deep learning. The agenda includes an overview of deep learning concepts like neural networks and training, common architectures like convolutional neural networks and LSTMs, a demonstration of Apache MXNet's symbolic and imperative APIs, and a discussion of infrastructure for deep learning on AWS like optimized EC2 instances and Amazon SageMaker.
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Scalable Deep Learning on AWS Using Apache MXNet - AWS Summit Tel Aviv 2017
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Intro to Scalable Deep Learning on AWS with Apache MXNet
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Flutter + tensor flow lite = awesome sauce
- 3. + =
- 4. 2000 - 2010 2010 - 2017 2017 - 2019 … 2019 … .Net Platform ASP.NET, C#, WCF, WPF, Silverlight ObjC, Swift, MacOS AWS, NodeJs, Python React, NodeJs, Swift MacOS, Kotlin, AWS, Flutter ML, AR, VR Human Learning
- 6. Agenda - Machine Learning Basics - TensorFlow Training Environment - TensorFlow + Keras Basics - Keras API Basics - TensorFlowLite Basics - Flutter App Architecture - Demo
- 7. Computer Science Artificial Intelligence Machine Learning Deep Learning CNN Computer Science is the study of computers and computational systems. AI is the simulation of human intelligence processe s by computer systems. Machine learning is a method of data analysis that automates analytical model. Deep learning is a Machine Learning technique that teaches computers to learn by example.
- 8. Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead
- 9. Supervised Unsupervised Semi-Supervised ? Input data labeled ? ? Input data not labeled ? ? Input data mixed Machine Learning algorithms organized by learning style.
- 10. Machine Learning algorithms organized by type. Source: https://machinelearningmastery.com
- 11. Cognitive computing and Machine Learning Image source: http://www.cognub.com/index.php/cognitive-platform/
- 12. Image Classification Image source: Fei-Fei Li & Andrej Karpathy & Justin Johnson
- 13. Image Classification Image source: Fei-Fei Li & Andrej Karpathy & Justin Johnson
- 14. Image Classification Image source: Fei-Fei Li & Andrej Karpathy & Justin Johnson
- 15. Spatial Localization and Detection Image source: Fei-Fei Li & Andrej Karpathy & Justin Johnson
- 16. Spatial Localization and Detection Image source: Fei-Fei Li & Andrej Karpathy & Justin Johnson
- 17. Spatial Localization and Detection Image source: Fei-Fei Li & Andrej Karpathy & Justin Johnson
- 18. Machine Learning Cloud Platforms
- 20. The core open source library to help you develop and train ML models
- 21. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.
- 22. + = tf.keras TensorFlow’s High Level API
- 23. Transfer Learning Pre-trained CNN 1000 Classifications Pre-trained CNN New Training Data + New Classifications
- 24. TensorFlow Environment Setup $ docker run -it --rm -v $(realpath ~/ notebooks):/tf/notebooks -p 8888:8888 tensorflow/tensorflow:latest-py3-jupyter
- 25. TF Keras Simple Sequential Model Sequential Model Save Model Convert Model TFLite Model
- 26. TF Keras Retraining Model Pre-trained Model New Model Mobilenet Optimize Model Save Model Convert Model TFLite Model
- 27. TensorFlow Lite is an open source deep learning framework for on-device inference.
- 28. How it works Create/Find Model Convert Model Deploy Optimize Image source: https://www.tensorflow.org/lite
- 29. TensorFlow Lite File Image source: https://www.tensorflow.org/lite FlatBuffers is an efficient cross platform serialization library for C++, C#, C, Go, Java, JavaScript, Lobster, Lua, TypeScript, PHP, Python, and Rust. It was originally created at Google for game development and other performance-critical applications. FlatBuffers
- 30. TensorFlow Lite File Image source: https://www.tensorflow.org/lite
- 31. Flutter is Google’s UI toolkit for building beautiful, natively compiled applications for mobile, web, and desktop from a single codebase.
- 32. TensorFlow Mobile Workflow Platform Channel tflite package Interpretor Flutter App Operators
- 36. App Screens
- 40. App Screens
- 41. App Screens
- 42. TensorFlow Environment Setup DEMO TIME
- 43. Contact