Adding more visuals without affecting performanceSt1X
Smallest viable set of performance optimizations recommendations for game artists. This presentation targets artist that have little knowledge about computer hardware capabilities and limitations.
The document describes an algorithm using convolutional neural networks for image classification. It includes details on the network architecture with multiple convolutional and max pooling layers, as well as a fully connected layer and softmax output. It then compares convolutional neural networks implemented on FPGAs to CPUs, GPUs, and ASICs, noting FPGAs provide adaptability and lower cost compared to ASICs while offering better performance and efficiency than CPUs and GPUs. The proposed solution is to use an FPGA-based convolutional neural network for image classification.
Build, train, and deploy machine learning models at scaleAmazon Web Services
Machine learning often feels a lot more difficult than it should be because the process of building and training models, and deploying them into production is complicated and slow. Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Apache MXNet and TensorFlow are pre-installed, and Amazon SageMaker offers a range of built-in, high-performance machine learning algorithms. If you want to train with an alternative framework or algorithm, you can bring your own in a Docker container.
Build, train, and deploy Machine Learning models at scale (May 2018)Julien SIMON
The document discusses Amazon SageMaker, a fully managed service that allows users to build, train and deploy machine learning models at scale. It provides pre-built algorithms and frameworks, managed hosting, one-click deployment and hyperparameter tuning capabilities. It also supports bringing your own custom algorithms by allowing users to run their own Docker containers. The document highlights how SageMaker simplifies and automates ML workflows and provides examples of customers using it at scale for image and data analysis.
Build, train, and deploy Machine Learning models at scale (May 2018)Julien SIMON
This document discusses Amazon SageMaker, a fully managed service that allows users to build, train and deploy machine learning models at scale. It provides pre-built algorithms and frameworks to simplify the ML workflow. Models can be trained on Amazon EC2 instances optimized for ML like P3 and C5 instances which provide GPUs and new CPUs respectively. SageMaker also allows users to use their own custom Docker containers. It was used by DigitalGlobe to extract information from satellite imagery using ML models. Demos of SageMaker's capabilities were shown.
This was my presentation on JavaScript Animation for Silicon Valley Code Camp 09. You get the material from http://my/personal/rbiggs/Blog/default.aspx
Adding more visuals without affecting performanceSt1X
Smallest viable set of performance optimizations recommendations for game artists. This presentation targets artist that have little knowledge about computer hardware capabilities and limitations.
The document describes an algorithm using convolutional neural networks for image classification. It includes details on the network architecture with multiple convolutional and max pooling layers, as well as a fully connected layer and softmax output. It then compares convolutional neural networks implemented on FPGAs to CPUs, GPUs, and ASICs, noting FPGAs provide adaptability and lower cost compared to ASICs while offering better performance and efficiency than CPUs and GPUs. The proposed solution is to use an FPGA-based convolutional neural network for image classification.
Build, train, and deploy machine learning models at scaleAmazon Web Services
Machine learning often feels a lot more difficult than it should be because the process of building and training models, and deploying them into production is complicated and slow. Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Apache MXNet and TensorFlow are pre-installed, and Amazon SageMaker offers a range of built-in, high-performance machine learning algorithms. If you want to train with an alternative framework or algorithm, you can bring your own in a Docker container.
Build, train, and deploy Machine Learning models at scale (May 2018)Julien SIMON
The document discusses Amazon SageMaker, a fully managed service that allows users to build, train and deploy machine learning models at scale. It provides pre-built algorithms and frameworks, managed hosting, one-click deployment and hyperparameter tuning capabilities. It also supports bringing your own custom algorithms by allowing users to run their own Docker containers. The document highlights how SageMaker simplifies and automates ML workflows and provides examples of customers using it at scale for image and data analysis.
Build, train, and deploy Machine Learning models at scale (May 2018)Julien SIMON
This document discusses Amazon SageMaker, a fully managed service that allows users to build, train and deploy machine learning models at scale. It provides pre-built algorithms and frameworks to simplify the ML workflow. Models can be trained on Amazon EC2 instances optimized for ML like P3 and C5 instances which provide GPUs and new CPUs respectively. SageMaker also allows users to use their own custom Docker containers. It was used by DigitalGlobe to extract information from satellite imagery using ML models. Demos of SageMaker's capabilities were shown.
This was my presentation on JavaScript Animation for Silicon Valley Code Camp 09. You get the material from http://my/personal/rbiggs/Blog/default.aspx
This document provides an overview of deep learning including definitions of machine learning, types of machine learning, and techniques used in deep learning such as neural networks. It discusses applications of deep learning in companies like Netflix and Uber. Finally, it provides resources for learning more about deep learning.
A PPT which gives a brief introduction on Machine Learning and on the products developed by using Machine Learning Algorithms in them. Gives the introduction by using content and also by using a few images in the slides as part of the explanation. It includes some examples of cool products like Google Cloud Platform, Cozmo (a tiny robot built by using Artificial Intelligence), IBM Watson and many more.
This copyright notice specifies that DeepLearning.AI slides are distributed under a Creative Commons license, can be used non-commercially for education
This document provides an overview of machine learning capabilities on AWS. It begins with introductions to machine learning concepts and the benefits of performing machine learning in the cloud. It then describes various AWS machine learning services like Amazon SageMaker for building, training, and deploying models. The rest of the document explores Amazon SageMaker in more detail, demonstrating how to train models using built-in algorithms or custom containers and deploy them for inference.
This document outlines the curriculum for a deep learning course that will enable students to build neural networks using popular packages like TensorFlow and Keras. The course covers topics such as recurrent neural networks, convolutional neural networks, initialization techniques, optimization methods, evaluation metrics, loss functions, regularization, using CNNs for image processing, and using RNNs for text processing. Students will apply what they learn to a capstone project at the end of the course.
This document provides an overview of recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. It discusses how RNNs can be used for sequence modeling tasks like sentiment analysis, machine translation, and speech recognition by incorporating context or memory from previous steps. LSTMs are presented as an improvement over basic RNNs that can learn long-term dependencies in sequences using forget gates, input gates, and output gates to control the flow of information through the network.
Transfer Learning
What Is Transfer Learning?
How Does Transfer Learning Work?
Why Is Transfer Learning Used?
When Should Transfer Learning Be Used?
Approaches to Transfer Learning
This presentation focusses on performance and cost optimization. First, we'll show you how to automatically tune hyper-parameters, and quickly converge to optimal models. Second, you'll learn how to use SageMaker Neo, a new service that optimizes models for the underlying hardware architecture. Third, we'll show you how Elastic Inference lets you attach GPU acceleration to EC2 and SageMaker instances at the fraction of the cost of a full-fledged GPU instance. Finally, we'll share additional cost optimization tips for SageMaker.
Cutting Edge Computer Vision for EveryoneIvo Andreev
Microsoft offers a wide range of tools and advanced solutions to support you in managing computer vision related tasks.
From purely coding approaches with ML.NET, through zero-code ComputerVision.ai to advanced and flexible AI service in Azure ML, there is a solution for every need and each type of person.
From running on premises, through managed infrastructure to completely cloud services the speed of getting to the desired results and the return of investment are guaranteed.
Join this session to get insights about the options, deployment, pricing, pros and cons compared and select the most appropriate tech for your business case.
Deep-Dive into Deep Learning Pipelines with Sue Ann Hong and Tim HunterDatabricks
Deep learning has shown tremendous successes, yet it often requires a lot of effort to leverage its power. Existing deep learning frameworks require writing a lot of code to run a model, let alone in a distributed manner. Deep Learning Pipelines is a Spark Package library that makes practical deep learning simple based on the Spark MLlib Pipelines API. Leveraging Spark, Deep Learning Pipelines scales out many compute-intensive deep learning tasks. In this talk we dive into – the various use cases of Deep Learning Pipelines such as prediction at massive scale, transfer learning, and hyperparameter tuning, many of which can be done in just a few lines of code. – how to work with complex data such as images in Spark and Deep Learning Pipelines. – how to deploy deep learning models through familiar Spark APIs such as MLlib and Spark SQL to empower everyone from machine learning practitioners to business analysts. Finally, we discuss integration with popular deep learning frameworks.
How Machine Learning Helps Organizations to Work More Efficiently?Tuan Yang
Data is increasing day by day and so is the cost of data storage and handling. However, by understanding the concepts of machine learning one can easily handle the excessive data and can process it in an affordable manner.
The process includes making models by using several kinds of algorithms. If the model is created precisely for certain task, then the organizations have a very wide chance of making use of profitable opportunities and avoiding the risks lurking behind the scenes.
Learn more about:
» Understanding Machine Learning Objectives.
» Data dimensions in Machine Learning.
» Fundamentals of Algorithms and Mapping from Input/Output.
» Parametric and Non-parametric Machine Learning Algorithms.
» Supervised, Unsupervised and Semi-Supervised Learning.
» Estimating Over-fitting and Under-fitting.
» Use Cases.
■ You’ve probably heard that Deep Learning is making news across the world as one of the most promising techniques in machine learning, especially for analyzing image data. With every industry dedicating resources to unlock the deep learning potential, to be competitive, you will want to use these models in tasks such as image tagging, object recognition, speech recognition, and text analysis. In this training session you will build deep learning models using neural networks, explore what they are, what they do, and how. To remove the barrier introduced by designing, training, and tuning networks, and to be able to achieve high performance with less labeled data, you will also build deep learning classifiers tailored to your specific task using pre-trained models, which we call deep features. Also, you’ll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets.
The document describes a project aimed at developing a machine learning model to automatically grade short written responses. The goal is to create a model that provides fairness, less human resource cost, and timely feedback compared to human grading. The model will be trained on a dataset of 17,000 student essays graded by humans. Features like part-of-speech tags, word lengths, spelling errors, grammar errors, term usage, and essay content will be engineered from the essays. Feature selection and various machine learning algorithms like KNN, Naive Bayes, decision trees, and ensemble models will be used to build and evaluate the final grading model.
Scalable Automatic Machine Learning in H2OSri Ambati
Abstract:
In recent years, the demand for machine learning experts has outpaced the supply, despite the surge of people entering the field. To address this gap, there have been big strides in the development of user-friendly machine learning software that can be used by non-experts. Although H2O and other tools have made it easier for practitioners to train and deploy machine learning models at scale, there is still a fair bit of knowledge and background in data science that is required to produce high-performing machine learning models. Deep Neural Networks in particular, are notoriously difficult for a non-expert to tune properly.
In this presentation, we provide an overview of the the field of "Automatic Machine Learning" and introduce the new AutoML functionality in H2O. H2O's AutoML provides an easy-to-use interface which automates the process of training a large, comprehensive selection of candidate models and a stacked ensemble model which, in most cases, will be the top performing model in the AutoML Leaderboard.
H2O AutoML is available in all the H2O interfaces including the h2o R package, Python module and the Flow web GUI. We will also provide simple code examples to get you started using AutoML.
Erin’s Bio:
Erin is a Statistician and Machine Learning Scientist at H2O.ai. She is the main author of H2O Ensemble. Before joining H2O, she was the Principal Data Scientist at Wise.io and Marvin Mobile Security (acquired by Veracode in 2012) and the founder of DataScientific, Inc. Erin received her Ph.D. in Biostatistics with a Designated Emphasis in Computational Science and Engineering from University of California, Berkeley. Her research focuses on ensemble machine learning, learning from imbalanced binary-outcome data, influence curve based variance estimation and statistical computing. She also holds a B.S. and M.A. in Mathematics.
This document provides an overview of machine learning and Azure ML Studio. It discusses how machine learning grew out of artificial intelligence work and is used for applications like database mining, recommendations, and computer vision. The document then outlines the process for creating models on Azure ML Studio, including getting data, pre-processing, defining features, training a model with an algorithm like linear regression, scoring and testing the model. It provides an example of using the automobile price dataset to predict new automobile prices through this process and a demonstration.
Setting up custom machine learning environments on AWS - AIM204 - Chicago AWS...Amazon Web Services
Sometimes, you might need to set up your own deep learning environments for domain-specific performance optimization and integration with custom applications. AWS offers prepackaged, optimized Amazon Machine Images (AMIs) and Docker container images that make it easy to quickly deploy these custom environments by letting you skip the complicated process of building and optimizing your environments from scratch. In this session, you learn about how to use AWS Deep Learning AMIs and AWS Deep Learning Containers to create custom machine learning environments with TensorFlow and Apache MXNet frameworks.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
More Related Content
Similar to Introduction to Deep Learning on Azure - Global Azure Bootcamp 2018
This document provides an overview of deep learning including definitions of machine learning, types of machine learning, and techniques used in deep learning such as neural networks. It discusses applications of deep learning in companies like Netflix and Uber. Finally, it provides resources for learning more about deep learning.
A PPT which gives a brief introduction on Machine Learning and on the products developed by using Machine Learning Algorithms in them. Gives the introduction by using content and also by using a few images in the slides as part of the explanation. It includes some examples of cool products like Google Cloud Platform, Cozmo (a tiny robot built by using Artificial Intelligence), IBM Watson and many more.
This copyright notice specifies that DeepLearning.AI slides are distributed under a Creative Commons license, can be used non-commercially for education
This document provides an overview of machine learning capabilities on AWS. It begins with introductions to machine learning concepts and the benefits of performing machine learning in the cloud. It then describes various AWS machine learning services like Amazon SageMaker for building, training, and deploying models. The rest of the document explores Amazon SageMaker in more detail, demonstrating how to train models using built-in algorithms or custom containers and deploy them for inference.
This document outlines the curriculum for a deep learning course that will enable students to build neural networks using popular packages like TensorFlow and Keras. The course covers topics such as recurrent neural networks, convolutional neural networks, initialization techniques, optimization methods, evaluation metrics, loss functions, regularization, using CNNs for image processing, and using RNNs for text processing. Students will apply what they learn to a capstone project at the end of the course.
This document provides an overview of recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. It discusses how RNNs can be used for sequence modeling tasks like sentiment analysis, machine translation, and speech recognition by incorporating context or memory from previous steps. LSTMs are presented as an improvement over basic RNNs that can learn long-term dependencies in sequences using forget gates, input gates, and output gates to control the flow of information through the network.
Transfer Learning
What Is Transfer Learning?
How Does Transfer Learning Work?
Why Is Transfer Learning Used?
When Should Transfer Learning Be Used?
Approaches to Transfer Learning
This presentation focusses on performance and cost optimization. First, we'll show you how to automatically tune hyper-parameters, and quickly converge to optimal models. Second, you'll learn how to use SageMaker Neo, a new service that optimizes models for the underlying hardware architecture. Third, we'll show you how Elastic Inference lets you attach GPU acceleration to EC2 and SageMaker instances at the fraction of the cost of a full-fledged GPU instance. Finally, we'll share additional cost optimization tips for SageMaker.
Cutting Edge Computer Vision for EveryoneIvo Andreev
Microsoft offers a wide range of tools and advanced solutions to support you in managing computer vision related tasks.
From purely coding approaches with ML.NET, through zero-code ComputerVision.ai to advanced and flexible AI service in Azure ML, there is a solution for every need and each type of person.
From running on premises, through managed infrastructure to completely cloud services the speed of getting to the desired results and the return of investment are guaranteed.
Join this session to get insights about the options, deployment, pricing, pros and cons compared and select the most appropriate tech for your business case.
Deep-Dive into Deep Learning Pipelines with Sue Ann Hong and Tim HunterDatabricks
Deep learning has shown tremendous successes, yet it often requires a lot of effort to leverage its power. Existing deep learning frameworks require writing a lot of code to run a model, let alone in a distributed manner. Deep Learning Pipelines is a Spark Package library that makes practical deep learning simple based on the Spark MLlib Pipelines API. Leveraging Spark, Deep Learning Pipelines scales out many compute-intensive deep learning tasks. In this talk we dive into – the various use cases of Deep Learning Pipelines such as prediction at massive scale, transfer learning, and hyperparameter tuning, many of which can be done in just a few lines of code. – how to work with complex data such as images in Spark and Deep Learning Pipelines. – how to deploy deep learning models through familiar Spark APIs such as MLlib and Spark SQL to empower everyone from machine learning practitioners to business analysts. Finally, we discuss integration with popular deep learning frameworks.
How Machine Learning Helps Organizations to Work More Efficiently?Tuan Yang
Data is increasing day by day and so is the cost of data storage and handling. However, by understanding the concepts of machine learning one can easily handle the excessive data and can process it in an affordable manner.
The process includes making models by using several kinds of algorithms. If the model is created precisely for certain task, then the organizations have a very wide chance of making use of profitable opportunities and avoiding the risks lurking behind the scenes.
Learn more about:
» Understanding Machine Learning Objectives.
» Data dimensions in Machine Learning.
» Fundamentals of Algorithms and Mapping from Input/Output.
» Parametric and Non-parametric Machine Learning Algorithms.
» Supervised, Unsupervised and Semi-Supervised Learning.
» Estimating Over-fitting and Under-fitting.
» Use Cases.
■ You’ve probably heard that Deep Learning is making news across the world as one of the most promising techniques in machine learning, especially for analyzing image data. With every industry dedicating resources to unlock the deep learning potential, to be competitive, you will want to use these models in tasks such as image tagging, object recognition, speech recognition, and text analysis. In this training session you will build deep learning models using neural networks, explore what they are, what they do, and how. To remove the barrier introduced by designing, training, and tuning networks, and to be able to achieve high performance with less labeled data, you will also build deep learning classifiers tailored to your specific task using pre-trained models, which we call deep features. Also, you’ll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets.
The document describes a project aimed at developing a machine learning model to automatically grade short written responses. The goal is to create a model that provides fairness, less human resource cost, and timely feedback compared to human grading. The model will be trained on a dataset of 17,000 student essays graded by humans. Features like part-of-speech tags, word lengths, spelling errors, grammar errors, term usage, and essay content will be engineered from the essays. Feature selection and various machine learning algorithms like KNN, Naive Bayes, decision trees, and ensemble models will be used to build and evaluate the final grading model.
Scalable Automatic Machine Learning in H2OSri Ambati
Abstract:
In recent years, the demand for machine learning experts has outpaced the supply, despite the surge of people entering the field. To address this gap, there have been big strides in the development of user-friendly machine learning software that can be used by non-experts. Although H2O and other tools have made it easier for practitioners to train and deploy machine learning models at scale, there is still a fair bit of knowledge and background in data science that is required to produce high-performing machine learning models. Deep Neural Networks in particular, are notoriously difficult for a non-expert to tune properly.
In this presentation, we provide an overview of the the field of "Automatic Machine Learning" and introduce the new AutoML functionality in H2O. H2O's AutoML provides an easy-to-use interface which automates the process of training a large, comprehensive selection of candidate models and a stacked ensemble model which, in most cases, will be the top performing model in the AutoML Leaderboard.
H2O AutoML is available in all the H2O interfaces including the h2o R package, Python module and the Flow web GUI. We will also provide simple code examples to get you started using AutoML.
Erin’s Bio:
Erin is a Statistician and Machine Learning Scientist at H2O.ai. She is the main author of H2O Ensemble. Before joining H2O, she was the Principal Data Scientist at Wise.io and Marvin Mobile Security (acquired by Veracode in 2012) and the founder of DataScientific, Inc. Erin received her Ph.D. in Biostatistics with a Designated Emphasis in Computational Science and Engineering from University of California, Berkeley. Her research focuses on ensemble machine learning, learning from imbalanced binary-outcome data, influence curve based variance estimation and statistical computing. She also holds a B.S. and M.A. in Mathematics.
This document provides an overview of machine learning and Azure ML Studio. It discusses how machine learning grew out of artificial intelligence work and is used for applications like database mining, recommendations, and computer vision. The document then outlines the process for creating models on Azure ML Studio, including getting data, pre-processing, defining features, training a model with an algorithm like linear regression, scoring and testing the model. It provides an example of using the automobile price dataset to predict new automobile prices through this process and a demonstration.
Setting up custom machine learning environments on AWS - AIM204 - Chicago AWS...Amazon Web Services
Sometimes, you might need to set up your own deep learning environments for domain-specific performance optimization and integration with custom applications. AWS offers prepackaged, optimized Amazon Machine Images (AMIs) and Docker container images that make it easy to quickly deploy these custom environments by letting you skip the complicated process of building and optimizing your environments from scratch. In this session, you learn about how to use AWS Deep Learning AMIs and AWS Deep Learning Containers to create custom machine learning environments with TensorFlow and Apache MXNet frameworks.
Similar to Introduction to Deep Learning on Azure - Global Azure Bootcamp 2018 (20)
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
5. MACHINE LEARNING DEFINITION
• Machine Learning: Field of study that gives computers the
ability to learn without being explicitly programmed.
~Arthur Samuel, 1959
• Well posed Learning Problem: A computer program is said to
learn from experience E with respect to some task T and some
performance measure P, if its performance on T, as measured
by P, improves with experience E.
~Tom Mitchell, 1998
6. MACHINE LEARNING TYPES
• Supervised Learning – Naïve Bayes, SVM, Artificial Neural Nets,
Random Forest
• Unsupervised Learning – K Means Clustering
• Reinforcement Learning – Model Free Learning, MDP, Q
Learning
• Semi Supervised Learning – GAN (New)
33. AZURE
• Data Science VM
• Deep Learning VM
• GPU support (NC- Series)
• Use Azure Trial (need to convert to Paid account)
• Trial account has 4 cores
• Deep Learning VM requires minimum 6 cores
• Specific Regions
• East US, East US 2, North Central US, South Central US and West US 2
35. SETUP ON AZURE
• Create Deep Learning VM Ubuntu
• Setup Auto shutdown
• Connect using SSH with the hostname (IP Address is reset on
restart)
• Build Model
• Train
• Infer
36. TYPICAL USES
• Ready to use for development
• Consistent setup for a team
• Use it for a temporary training tasks
37. DEMO
• Training
• MNIST dataset
• Keras with Microsoft CNTK backend
• Inference
• Use trained model
• Predict the digit in Jupyter
• Write and Predict
38. DEMO CODE LINKS
• Jupyter Notebooks for Training and Prediction
https://github.com/shashijeevan/keras_mnist_notebooks
• Python Flask App for generating digits and predict
https://github.com/shashijeevan/mnist-draw
39.
40. NEXT STEPS
• ONNX – Model Exchange Standard
• Windows ML – Inference Built into Windows
• Visual Studio Tools for AI