This document provides an introduction to deep learning. It begins with a refresher on machine learning, covering classification, regression, supervised learning, unsupervised learning, and reinforcement learning. It then discusses neural networks and their basic components like layers, nodes, and weights. An example of unsupervised learning is given about learning Chinese. Deep learning is introduced as using large neural networks to learn complex feature hierarchies from large amounts of data. Key aspects of deep learning covered include representation learning, layer-wise training, and using unsupervised pre-training before supervised fine-tuning. Applications and impact areas of deep learning are also mentioned.
Deep Learning: Towards General Artificial IntelligenceRukshan Batuwita
For the past several years Deep Learning methods have revolutionized the areas in Pattern Recognition, namely, Computer Vision, Speech Recognition, Natural Language Processing etc. These techniques have been mainly developed by academics, closely working with tech giants such as Google, Microsoft and Facebook where the research outcomes have been successfully integrated into commercial products such as Google image and voice search, Google Translate, Microsoft Cortana, Facebook M and many more interesting applications that are yet to come. More recently, Google DeepMind Technologies has been working on Artificial General Intelligence using Deep Reinforcement Learning methods, where their AlphaGo system beat the world champion of the complex Chinese game 'Go' in March 2016. This talk will present a thorough introduction to major Deep Learning techniques, recent breakthroughs and some exciting applications.
The document is a PowerPoint presentation on artificial intelligence that contains the following key points:
1. It discusses the origins and early history of AI research from the 1950s conference at Dartmouth College.
2. It covers various aspects of AI including knowledge representation, natural language processing, emotion and social skills in machines, and creativity in AI systems.
3. It provides an overview of artificial neural networks and how they are inspired by biological neural systems, focusing on artificial neurons, learning processes, and function approximation using neural networks.
Artificial Intelligence AI Topics History and Overviewbutest
The document discusses the history and concepts of artificial intelligence including machine learning. It provides definitions of key AI terms and describes some famous early AI programs. It also discusses machine learning methods and applications, different types of learning, and challenges in the field. Games AI is explored through techniques like min-max trees used in chess programs. The Turing Test is introduced as a proposal to measure intelligence along with proposed modifications.
This fast-paced session provides a brief history of AI, followed by AI-related topics, such as Machine Learning, Deep Learning and Reinforcement Learning, and the most popular frameworks for Machine Learning. You will learn about some of the successes of AI, and also some of the significant challenges in AI. No specialized knowledge is required, but an avid interest is recommended to derive the maximum benefit from this session.
This presentation deals with the basics of AI and it's connection with neural network. Additionally, it explains the pros and cons of AI along with the applications.
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
The document provides an overview of deep learning, including its history, key concepts, applications, and recent advances. It discusses the evolution of deep learning techniques like convolutional neural networks, recurrent neural networks, generative adversarial networks, and their applications in computer vision, natural language processing, and games. Examples include deep learning for image recognition, generation, segmentation, captioning, and more.
This document provides an overview of deep learning, machine learning, and artificial intelligence. It defines artificial intelligence as efforts to automate intellectual tasks normally performed by humans. Machine learning involves training systems using examples rather than explicit programming. Deep learning uses successive layers of representations in neural networks to transform input data into more useful representations. It has achieved near-human level performance on tasks like image classification and speech recognition. While popular, deep learning is not always the best approach and other machine learning methods exist.
This document provides an introduction to deep learning. It begins with a refresher on machine learning, covering classification, regression, supervised learning, unsupervised learning, and reinforcement learning. It then discusses neural networks and their basic components like layers, nodes, and weights. An example of unsupervised learning is given about learning Chinese. Deep learning is introduced as using large neural networks to learn complex feature hierarchies from large amounts of data. Key aspects of deep learning covered include representation learning, layer-wise training, and using unsupervised pre-training before supervised fine-tuning. Applications and impact areas of deep learning are also mentioned.
Deep Learning: Towards General Artificial IntelligenceRukshan Batuwita
For the past several years Deep Learning methods have revolutionized the areas in Pattern Recognition, namely, Computer Vision, Speech Recognition, Natural Language Processing etc. These techniques have been mainly developed by academics, closely working with tech giants such as Google, Microsoft and Facebook where the research outcomes have been successfully integrated into commercial products such as Google image and voice search, Google Translate, Microsoft Cortana, Facebook M and many more interesting applications that are yet to come. More recently, Google DeepMind Technologies has been working on Artificial General Intelligence using Deep Reinforcement Learning methods, where their AlphaGo system beat the world champion of the complex Chinese game 'Go' in March 2016. This talk will present a thorough introduction to major Deep Learning techniques, recent breakthroughs and some exciting applications.
The document is a PowerPoint presentation on artificial intelligence that contains the following key points:
1. It discusses the origins and early history of AI research from the 1950s conference at Dartmouth College.
2. It covers various aspects of AI including knowledge representation, natural language processing, emotion and social skills in machines, and creativity in AI systems.
3. It provides an overview of artificial neural networks and how they are inspired by biological neural systems, focusing on artificial neurons, learning processes, and function approximation using neural networks.
Artificial Intelligence AI Topics History and Overviewbutest
The document discusses the history and concepts of artificial intelligence including machine learning. It provides definitions of key AI terms and describes some famous early AI programs. It also discusses machine learning methods and applications, different types of learning, and challenges in the field. Games AI is explored through techniques like min-max trees used in chess programs. The Turing Test is introduced as a proposal to measure intelligence along with proposed modifications.
This fast-paced session provides a brief history of AI, followed by AI-related topics, such as Machine Learning, Deep Learning and Reinforcement Learning, and the most popular frameworks for Machine Learning. You will learn about some of the successes of AI, and also some of the significant challenges in AI. No specialized knowledge is required, but an avid interest is recommended to derive the maximum benefit from this session.
This presentation deals with the basics of AI and it's connection with neural network. Additionally, it explains the pros and cons of AI along with the applications.
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
The document provides an overview of deep learning, including its history, key concepts, applications, and recent advances. It discusses the evolution of deep learning techniques like convolutional neural networks, recurrent neural networks, generative adversarial networks, and their applications in computer vision, natural language processing, and games. Examples include deep learning for image recognition, generation, segmentation, captioning, and more.
This document provides an overview of deep learning, machine learning, and artificial intelligence. It defines artificial intelligence as efforts to automate intellectual tasks normally performed by humans. Machine learning involves training systems using examples rather than explicit programming. Deep learning uses successive layers of representations in neural networks to transform input data into more useful representations. It has achieved near-human level performance on tasks like image classification and speech recognition. While popular, deep learning is not always the best approach and other machine learning methods exist.
This presentation attempts to explain some of the concepts used when describing data science, machine learning, and deep learning. IT also describes data science as a process, rather than as a set of specific tools and services.
"You Can Do It" by Louis Monier (Altavista Co-Founder & CTO) & Gregory Renard (CTO & Artificial Intelligence Lead Architect at Xbrain) for Deep Learning keynote #0 at Holberton School (http://www.meetup.com/Holberton-School/events/228364522/)
If you want to assist to similar keynote for free, checkout http://www.meetup.com/Holberton-School/
This document introduces machine learning algorithms. It discusses supervised and unsupervised learning problems and strategies. It provides examples of machine learning applications including neural networks for handwritten digit recognition, evolutionary algorithms for nozzle design, and Bayesian networks for gene expression analysis.
Currently hundreds of tools are promising to make artificial intelligence accessible to the masses. Tools like DataRobot, H20 Driverless AI, Amazon SageMaker or Microsoft Azure Machine Learning Studio.
These tools promise to accelerate the time-to-value of data science projects by simplifying model building.
In the workshop we will approach the AI Topic head on!
What is AI? What can AI do today? What do I need to start my own project?
We do all this using Microsoft's Machine Learning Studio.
Trainer: Philipp von Loringhoven - Chef, Designer, Developer, Markeeter - Data Nerd!
He has acquired a lot of expertise in marketing, business intelligence and product development during his time at the Rocket Internet startups (Wimdu, Lamudi) and Projekt-A (Tirendo).
Today he supports customers of the Austrian digitisation agency TOWA as Director Data Consulting to generate an added value from their data.
This is the first lecture of the AI course offered by me at PES University, Bangalore. In this presentation we discuss the different definitions of AI, the notion of Intelligent Agents, distinguish an AI program from a complex program such as those that solve complex calculus problems (see the integration example) and look at the role of Machine Learning and Deep Learning in the context of AI. We also go over the course scope and logistics.
Deep learning is a type of machine learning that uses neural networks inspired by the human brain. It has been successfully applied to problems like image recognition, speech recognition, and natural language processing. Deep learning requires large datasets, clear goals, computing power, and neural network architectures. Popular deep learning models include convolutional neural networks and recurrent neural networks. Researchers like Geoffry Hinton and companies like Google have advanced the field through innovations that have won image recognition challenges. Deep learning will continue solving harder artificial intelligence problems by learning from massive amounts of data.
This document discusses machine learning and artificial intelligence. It provides an overview of the machine learning process, including obtaining raw data, preprocessing the data, applying algorithms to extract features and train models, and generating outputs. It then describes different types of machine learning, including supervised learning, unsupervised learning, reinforcement learning, and semi-supervised learning. Specific algorithms like artificial neural networks, support vector machines, genetic algorithms are also briefly explained. Real-world applications of machine learning like character recognition and medical diagnosis are listed.
The document provides an overview of deep learning, including its past, present, and future. It discusses the concepts of artificial general intelligence, artificial superintelligence, and predictions about their development from experts like Hawking, Musk, and Gates. Key deep learning topics are summarized, such as neural networks, machine learning approaches, important algorithms and researchers, and how deep learning works.
Introduction to machine learningunsupervised learningSardar Alam
The document provides an introduction to machine learning and discusses different types of machine learning algorithms including supervised and unsupervised learning. It provides examples of problems that could be addressed using supervised learning like regression to predict housing prices and classification to detect cancer. Unsupervised learning is used to discover hidden patterns in unlabeled data like grouping customer accounts or news articles.
4 technologies including AI, IoT, blockchain, and virtual reality will revolutionize the maritime sector. AI and blockchain will have many applications including supply chain visibility, data science, voyage optimization, and diagnosis systems. Training seafarers for an AI-based future will require changes to curriculum, including teaching mathematics, programming, cybersecurity, and manual operation skills. Ensuring safe return to port capabilities with redundancy will also be important as autonomy increases.
Introduction to Deep Learning for Non-ProgrammersOswald Campesato
This session provides a brief history of AI, followed by AI-related topics, such as robots in AI, Machine Learning and Deep Learning, use cases for AI, some of the successes of AI, and also some of the significant challenges in AI. You will also learn about AI and mobile devices and the ethics of AI. An avid interest is recommended to derive the maximum benefit from this session.
Machine learning is a scientific discipline that develops algorithms to allow systems to learn from data and improve automatically without being explicitly programmed. The document discusses several key machine learning concepts including supervised learning algorithms like decision trees and Naive Bayes classification. Decision trees use branching to represent classification or regression rules learned from data to make predictions. Naive Bayes classification is a simple probabilistic classifier that applies Bayes' theorem with strong independence assumptions between features.
This document provides legal notices and disclaimers for an Intel presentation. It states that the presentation is for informational purposes only and that Intel makes no warranties. It also notes that performance can vary depending on system configuration and that sample source code is released under an Intel license agreement. Finally, it lists various trademarks.
The document provides an overview of artificial intelligence including definitions, types of AI tasks, foundations of AI, history of AI, current capabilities and limitations of AI systems, and techniques for problem solving and planning. It discusses machine learning, natural language processing, expert systems, neural networks, search problems, constraint satisfaction problems, linear and non-linear planning approaches. The key objectives of the course are introduced as understanding common AI concepts and having an idea of current and future capabilities of AI systems.
Mengenal Machine/Deep Learning, Artificial Intelligence dan mengenal apa bedanya dengan Business Intelligence, apa hubungannya dengan Big Data dan Data Science/Analytics.
This document provides an overview of deep learning and neural networks. It begins with definitions of machine learning, artificial intelligence, and the different types of machine learning problems. It then introduces deep learning, explaining that it uses neural networks with multiple layers to learn representations of data. The document discusses why deep learning works better than traditional machine learning for complex problems. It covers key concepts like activation functions, gradient descent, backpropagation, and overfitting. It also provides examples of applications of deep learning and popular deep learning frameworks like TensorFlow. Overall, the document gives a high-level introduction to deep learning concepts and techniques.
This document provides an overview and introduction to deep learning. It discusses motivations for deep learning such as its powerful learning capabilities. It then covers deep learning basics like neural networks, neurons, training processes, and gradient descent. It also discusses different network architectures like convolutional neural networks and recurrent neural networks. Finally, it describes various deep learning applications, tools, and key researchers and companies in the field.
This presentation attempts to explain some of the concepts used when describing data science, machine learning, and deep learning. IT also describes data science as a process, rather than as a set of specific tools and services.
"You Can Do It" by Louis Monier (Altavista Co-Founder & CTO) & Gregory Renard (CTO & Artificial Intelligence Lead Architect at Xbrain) for Deep Learning keynote #0 at Holberton School (http://www.meetup.com/Holberton-School/events/228364522/)
If you want to assist to similar keynote for free, checkout http://www.meetup.com/Holberton-School/
This document introduces machine learning algorithms. It discusses supervised and unsupervised learning problems and strategies. It provides examples of machine learning applications including neural networks for handwritten digit recognition, evolutionary algorithms for nozzle design, and Bayesian networks for gene expression analysis.
Currently hundreds of tools are promising to make artificial intelligence accessible to the masses. Tools like DataRobot, H20 Driverless AI, Amazon SageMaker or Microsoft Azure Machine Learning Studio.
These tools promise to accelerate the time-to-value of data science projects by simplifying model building.
In the workshop we will approach the AI Topic head on!
What is AI? What can AI do today? What do I need to start my own project?
We do all this using Microsoft's Machine Learning Studio.
Trainer: Philipp von Loringhoven - Chef, Designer, Developer, Markeeter - Data Nerd!
He has acquired a lot of expertise in marketing, business intelligence and product development during his time at the Rocket Internet startups (Wimdu, Lamudi) and Projekt-A (Tirendo).
Today he supports customers of the Austrian digitisation agency TOWA as Director Data Consulting to generate an added value from their data.
This is the first lecture of the AI course offered by me at PES University, Bangalore. In this presentation we discuss the different definitions of AI, the notion of Intelligent Agents, distinguish an AI program from a complex program such as those that solve complex calculus problems (see the integration example) and look at the role of Machine Learning and Deep Learning in the context of AI. We also go over the course scope and logistics.
Deep learning is a type of machine learning that uses neural networks inspired by the human brain. It has been successfully applied to problems like image recognition, speech recognition, and natural language processing. Deep learning requires large datasets, clear goals, computing power, and neural network architectures. Popular deep learning models include convolutional neural networks and recurrent neural networks. Researchers like Geoffry Hinton and companies like Google have advanced the field through innovations that have won image recognition challenges. Deep learning will continue solving harder artificial intelligence problems by learning from massive amounts of data.
This document discusses machine learning and artificial intelligence. It provides an overview of the machine learning process, including obtaining raw data, preprocessing the data, applying algorithms to extract features and train models, and generating outputs. It then describes different types of machine learning, including supervised learning, unsupervised learning, reinforcement learning, and semi-supervised learning. Specific algorithms like artificial neural networks, support vector machines, genetic algorithms are also briefly explained. Real-world applications of machine learning like character recognition and medical diagnosis are listed.
The document provides an overview of deep learning, including its past, present, and future. It discusses the concepts of artificial general intelligence, artificial superintelligence, and predictions about their development from experts like Hawking, Musk, and Gates. Key deep learning topics are summarized, such as neural networks, machine learning approaches, important algorithms and researchers, and how deep learning works.
Introduction to machine learningunsupervised learningSardar Alam
The document provides an introduction to machine learning and discusses different types of machine learning algorithms including supervised and unsupervised learning. It provides examples of problems that could be addressed using supervised learning like regression to predict housing prices and classification to detect cancer. Unsupervised learning is used to discover hidden patterns in unlabeled data like grouping customer accounts or news articles.
4 technologies including AI, IoT, blockchain, and virtual reality will revolutionize the maritime sector. AI and blockchain will have many applications including supply chain visibility, data science, voyage optimization, and diagnosis systems. Training seafarers for an AI-based future will require changes to curriculum, including teaching mathematics, programming, cybersecurity, and manual operation skills. Ensuring safe return to port capabilities with redundancy will also be important as autonomy increases.
Introduction to Deep Learning for Non-ProgrammersOswald Campesato
This session provides a brief history of AI, followed by AI-related topics, such as robots in AI, Machine Learning and Deep Learning, use cases for AI, some of the successes of AI, and also some of the significant challenges in AI. You will also learn about AI and mobile devices and the ethics of AI. An avid interest is recommended to derive the maximum benefit from this session.
Machine learning is a scientific discipline that develops algorithms to allow systems to learn from data and improve automatically without being explicitly programmed. The document discusses several key machine learning concepts including supervised learning algorithms like decision trees and Naive Bayes classification. Decision trees use branching to represent classification or regression rules learned from data to make predictions. Naive Bayes classification is a simple probabilistic classifier that applies Bayes' theorem with strong independence assumptions between features.
This document provides legal notices and disclaimers for an Intel presentation. It states that the presentation is for informational purposes only and that Intel makes no warranties. It also notes that performance can vary depending on system configuration and that sample source code is released under an Intel license agreement. Finally, it lists various trademarks.
The document provides an overview of artificial intelligence including definitions, types of AI tasks, foundations of AI, history of AI, current capabilities and limitations of AI systems, and techniques for problem solving and planning. It discusses machine learning, natural language processing, expert systems, neural networks, search problems, constraint satisfaction problems, linear and non-linear planning approaches. The key objectives of the course are introduced as understanding common AI concepts and having an idea of current and future capabilities of AI systems.
Mengenal Machine/Deep Learning, Artificial Intelligence dan mengenal apa bedanya dengan Business Intelligence, apa hubungannya dengan Big Data dan Data Science/Analytics.
This document provides an overview of deep learning and neural networks. It begins with definitions of machine learning, artificial intelligence, and the different types of machine learning problems. It then introduces deep learning, explaining that it uses neural networks with multiple layers to learn representations of data. The document discusses why deep learning works better than traditional machine learning for complex problems. It covers key concepts like activation functions, gradient descent, backpropagation, and overfitting. It also provides examples of applications of deep learning and popular deep learning frameworks like TensorFlow. Overall, the document gives a high-level introduction to deep learning concepts and techniques.
This document provides an overview and introduction to deep learning. It discusses motivations for deep learning such as its powerful learning capabilities. It then covers deep learning basics like neural networks, neurons, training processes, and gradient descent. It also discusses different network architectures like convolutional neural networks and recurrent neural networks. Finally, it describes various deep learning applications, tools, and key researchers and companies in the field.
This document provides an overview of deep learning concepts including:
- Deep learning uses neural networks inspired by the human brain to learn representations of data without being explicitly programmed.
- Key deep learning concepts are explained such as convolutional neural networks, activation functions, gradient descent, and overfitting.
- TensorFlow is introduced as an open-source library for machine learning that allows for implementing deep learning models at scale.
- Applications of deep learning like computer vision, natural language processing, and recommender systems are discussed.
This document discusses artificial intelligence, machine learning, deep learning, and data science. It defines each term and explains the relationships between them. AI is the overarching field, while machine learning and deep learning are subsets of AI. Machine learning allows machines to improve performance over time without human intervention by learning from examples, and deep learning uses artificial neural networks with many layers to closely mimic the human brain. The document provides an example of a fruit detection system using deep learning that trains a neural network to detect ripe fruit for automated harvesting.
This document provides an overview of three types of machine learning: supervised learning, reinforcement learning, and unsupervised learning. It then discusses supervised learning in more detail, explaining that each training case consists of an input and target output. Regression aims to predict a real number output, while classification predicts a class label. The learning process typically involves choosing a model and adjusting its parameters to reduce the discrepancy between the model's predicted output and the true target output on each training case.
Artificial intelligence and machine learning are advancing rapidly. Neural networks allow computers to learn from large amounts of data through supervised, unsupervised, and reinforcement learning. Applications include computer vision, natural language processing, adaptive websites, speech recognition, and autonomous vehicles. Advancements have been enabled by cheap parallel computing, vast data availability, improved algorithms, and cloud infrastructure. Open questions remain around how neural networks work and how to ensure AI is beneficial to humanity.
This document provides an agenda and overview for a deep learning course. The agenda includes an introduction to program and course learning outcomes, the syllabus, class management tools, and an introduction to week 1 of deep learning. The syllabus outlines 15 weekly topics on deep learning concepts and algorithms. Example student projects are provided showing applications of deep learning to areas like computer vision, natural language processing, and games. The introduction to week 1 discusses artificial intelligence, machine learning, and deep learning definitions and provides an overview of programming assignments and deep learning in action.
Hacking Predictive Modeling - RoadSec 2018HJ van Veen
This document provides an overview of machine learning and predictive modeling techniques for hackers and data scientists. It discusses foundational concepts in machine learning like functionalism, connectionism, and black box modeling. It also covers practical techniques like feature engineering, model selection, evaluation, optimization, and popular Python libraries. The document encourages an experimental approach to hacking predictive models through techniques like brute forcing hyperparameters, fuzzing with data permutations, and social engineering within data science communities.
Artificial intelligence uses techniques like machine learning, artificial neural networks, deep learning, computer vision, and natural language processing to create intelligent machines that can learn from large amounts of data in an accurate manner similar to humans. It works by combining data with fast, iterative processing and smart algorithms to learn patterns and deliver outputs close to human level. Specifically, machine learning allows programs to learn from examples and experiences without being explicitly programmed, while artificial neural networks were inspired by the human brain to recognize complex patterns in data.
The document discusses generative AI and how it has evolved from earlier forms of AI like artificial intelligence, machine learning, and deep learning. It explains key concepts like generative adversarial networks, large language models, transformers, and techniques like reinforcement learning from human feedback and prompt engineering that are used to develop generative AI models. It also provides examples of using generative AI for image generation using diffusion models and how Stable Diffusion differs from earlier diffusion models by incorporating a text encoder and variational autoencoder.
Machine learning basics by akanksha baliAkanksha Bali
This document provides an introduction to machine learning, including definitions of machine learning, why it is needed, and the main types of machine learning algorithms. It describes supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. For each type, it provides examples and brief explanations. It also discusses applications of machine learning and the differences between machine learning and deep learning.
The term Machine Learning was coined by Arthur Samuel in 1959, an american pioneer in the field of computer gaming and artificial intelligence and stated that “ it gives computers the ability to learn without being explicitly programmed” And in 1997, Tom Mitchell gave a “ well-Posed” mathematical and relational definition that “ 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”.
Machine learning is needed for tasks that are too complex for humans to code directly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.
This document provides an introduction to deep learning. It discusses the history of machine learning and how neural networks work. Specifically, it describes different types of neural networks like deep belief networks, convolutional neural networks, and recurrent neural networks. It also covers applications of deep learning, as well as popular platforms, frameworks and libraries used for deep learning development. Finally, it demonstrates an example of using the Nvidia DIGITS tool to train a convolutional neural network for image classification of car park images.
Deep learning is introduced along with its applications and key players in the field. The document discusses the problem space of inputs and outputs for deep learning systems. It describes what deep learning is, providing definitions and explaining the rise of neural networks. Key deep learning architectures like convolutional neural networks are overviewed along with a brief history and motivations for deep learning.
Unit one ppt of deeep learning which includes Ann cnnkartikaursang53
Deep learning involves using neural networks with multiple layers to automatically learn patterns from large amounts of data. The document discusses the working of deep learning networks, which take raw input data and pass it through successive hidden layers to determine higher-level features until reaching the output layer. It also covers applications of deep learning like image recognition and Amazon Alexa, as well as advantages such as automatic feature learning and ability to handle complex datasets.
Training machine learning deep learning 2017Iwan Sofana
This document discusses deep learning and neural networks. It begins with a brief history of neural networks, from the earliest Perceptron algorithm in 1958 to modern developments enabled by increased computational power and data. Deep learning uses neural networks with multiple hidden layers to automatically learn representations of data and hierarchical feature detectors. Examples are given of applying deep learning to tasks like image recognition. The document outlines challenges of deep learning like the large amount of training required and complexity of modeling real-world behaviors.
This document provides an overview of machine learning concepts from the first lecture of an introduction to machine learning course. It discusses what machine learning is, examples of tasks that can be solved with machine learning, and key concepts like supervised vs. unsupervised learning, hypothesis spaces, searching hypothesis spaces, generalization, and model complexity.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
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.
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.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
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.
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.
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!
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
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.
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
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
2. What I am going to cover in this talk?
• General view of AI, machine learning and deep learning.
• Understand basics of deep learning .
• Some exciting opportunities for applying deep learning.
3. Artificial Intelligence
• What is intelligence? Why to create it artificially?
• Strong artificial intelligence
• Agent and Environment
• Intelligence is the capacity to learn and solve problems
• Ability to interact with the real world
• Reasoning and Planning
• Learning and Adaptation
4. Poster boy of AI – IBM Deep Blue
• ~200 million moves /
second = 3.6 * 1010
moves in 3 minutes
• 3 min corresponds to ~7
plies of uniform depth
minimax search
• 1 sec corresponds to 380
years of human thinking
time
• 32-node RS6000 SP
multicomputer, 16 chess
chips, 32 GB opening &
endgame database
5. Artificial Intelligence Impact
• Complex but repetitive movements with confined cognition of the
environment.
• Searching in large possible answers.
• Predicting based on what seen so far in the environment.
6. Evolution of AI
• Machines that search and eliminate irrelevant possibilities.
• Machines storing knowledge about the world and then use sored
knowledge for answering.
• Machines learning to generalize what it has learned by examples
seen.
7. Learning by examples
• Humans are good pattern matchers at unconscious level. We all learn
by examples.
• Learning from examples = Learning from data
• What you are learning? A model.
• How computer scientist is going to create it? Probability and
Mathematics.
• Learning = tuning the model.
• How to tune it? How to make it best possible ? Error.
8. Applications so far…
• image recognition
• voice recognition
• image search
• effective text search
• marketing targeting
• sales prediction
• optimization of advertisements
• store shelf or space planning
• movements of the stock market
Yes, machine learning is powerful !!!
9. Its all about features.
• More Data.
• Advanced algorithms.
• Feature engineering – Ultimately its as smart as features. Finding the
correct features is critical in the success.
Data Features Model
10. Machine learning engineer’s
fears
• A machine learning algorithm can only work
well on data with the assumption that
training data represents all the real data
available. If unseen data has different
distribution, the learned model does not
generalize well.
• What you see is not always what you will get
next.
• There is no reason*.
• I need data in the format I like.
11. Pause and think.
• Machine can't recognize what knowledge it should use when it is
assigned a task.
• Machine can't understand a concept that puts knowledge pieces
together, it is at the mercy of chunks of examples fed in.
• Machine can't find out which features should be considered while
learning from examples.
12. Intuitive Example
• Imagine that you don’t speak a word of Chinese, but your company is moving you to China next
month. Company will sponsor Chinese speaking lesson for you once you are there, but you
want to prepare yourself before you go.
• You decide to listen to Chinese radio station
• For a month, you bombard yourself with Chinese radio.
• You don’t know the meaning of Chinese words.
• Lets think that somehow your brain develops capacity to understand few commonly occurring
patterns without meaning. In other words, you have developed a different level of
representation for some part of Chinese by becoming more tuned to its common sounds and
structures.
• Hopefully, when you arrive in China, you’ll be in a better position to start the lessons.
Example loosely taken from Lecture series by Prof. Abu Mustafa
13. Welcome to deep learning
• Learn features without being explicit - automatic feature extraction.
• Multiple linear and non-linear transformations.
• Build hierarchy of notable features into more informative features,
keep doing it.
• Work with very large number of examples. Modern data sets are
enormous.
• Beat the benchmarks.
14. Biology Neuron
• The brain is composed of lot of
interconnected neurons. Each
neuron is connected to many other
neurons.
• Neurons transmit signals to each
other.
• Whether a signal is transmitted is
an all-or-nothing event (threshold).
• Strength of the signal is sent,
depends on the strength of the
bond (synapse) between two
neurons.
Neurons (10^11 )
synapses (10^14) connect the
neurons
Brains learns by 1) Altering strength between neurons
2) Creating/deleting connections
17. Back propagation idea
• Treat the problem as one of minimizing errors between the example
label and the network output, given the example and network
weights as input
• Error(example) = (true value – calculated value from inputs)2
• Sum this error term over all examples
• E(w) = Error = i (yi – f(xi,w))2
• Minimize errors using an optimization algorithm
• Stochastic gradient descent is typically used.
Forward pass: signal = activity = y
Backward pass: signal = dE/dx
18. Back propagation algorithm
• Initialize all weights to small random numbers.
• Until stopping condition (# epochs or no errors), do
• For each training input, do
1. Input the training example to the network and propagate computations
to output
2. Error = Compare actual value to calculated value
3. Adjust weights according to the delta rule, propagating the errors back;
The weights will be nudged closer so that the network learns to give the
desired output. The weights will begin to converge to a point where
error across multiple training inputs is minimum.
19. Back propagation thoughts
• Is powerful - can learn any function, given enough hidden units.
• Has the standard problem of generalization vs. Memorization. With too many
units, the network will tend to memorize the input and not generalize well. Some
schemes exist to “prune” the neural network.
• Networks require extensive training, many parameters to fiddle with. Can be
extremely slow to train. May not find the best possible combination of weights.
• Inherently parallel algorithm, ideal for multiprocessor hardware.
• Despite these, is a very powerful algorithm that has seen widespread successful
deployments.
20. Do more…
• Create columns of artificial neurons
• Connect the columns. Create depth.
• Go deep. How deep you can go?
• Keep feeding massive amounts of data. And labels too…
• Give more days to learn.
• Use machines good at multiplying large matrices.
• At the end… tune it! tune it!
22. Multiple levels of abstraction
• Layer 1: presence/absence of edge at particular location &
orientation.
• Layer 2: motifs formed by particular arrangements of edges;
allows small variations in edge locations
• Layer 3: assemble motifs into larger combinations of familiar
objects
• Layer 4 and beyond: higher order combinations
Key Idea: the layers are not designed by an engineer, but learned
from data using a general-purpose learner.
27. Deep Learning Impact
Computer Vision
Image recognition (e.g. Tagging faces in photos)
Audio Processing
Voice recognition (e.g. Voice based search, Siri)
Natural Language Processing
automatic translation
Pattern detection (e.g. Handwriting recognition)
28. C for Cat… Learning DL way
• Google scientists created one of the largest deep neural networks by
connecting 16,000 computer processors. They presented this network
called Google Brain with 10 million digital images found in YouTube
videos, what did Google’s Brain learn after viewing these images for
three days?
29. Latest buzz
Alpha Go
• DeepMind’s AlphaGo
beats Lee Sedol in Go
• AlphaGo used 40 search
threads, 48 CPUs, and 8
GPUs
• AlphaGo learned using a
general-purpose
algorithm that allowed it
to interpret the game’s
patterns.
• AlphaGo program applied
deep learning.
30. Anatomy of deep nets
• Batches and Epochs
• Layers and stacking
• Preprocessing
• Objective function and Optimizer
• Activations
• Initialization
• train - model - test
31. What it can solve?
• Classification
• Classify visual objects, Identify objects - faces in images and video
• Classify audio and text
• Prediction
• Predict the probability that a customer will choose a product.
• Forecast demand for a product.
• Predict what happens next in videos?
• Generation
• Generate pictures and paintings, cool artsy stuff.
• Generate writing – write headlines, articles and novels.
• Give captions
32. ML in automotive industry
• Identify and navigate roads and obstructions in real-time for
autonomous driving.
• Predict failure and recommend proactive maintenance on vehicle
components.
• In vehicle recommendation engine.
• Discover anomalies across fleet of vehicle sensor data to identify
potential failure risks.
33. ML in manufacturing
• Predict failure and recommend proactive maintenance for production
and moving equipment.
• Predict supply chain failures and demand cycles.
• Detect product defects visually.
34. ML in stores and e-commerce
• Optimize in-store product assortment to maximize sales.
• Personalize product recommendations and advertising to target
individual consumers.
• Classify visual features from in-store video.
• Product search.
35. ML in finance
• Personalize product offerings to target individual consumers.
• Fraud detection.
• Optimize branch/ATM network based on diverse signals of demand.
• Predict asset price movements based on greater data.
• Predict risk of churn for individual customers/clients and recommend
renegotiation strategy.
• Loan. How much? How long? Customize.
36. ML in agriculture
• Customize growing techniques specific to individual plot
characteristics.
• Optimize pricing in real time based on future market, weather, and
other forecasts.
• Predict yield for farming or production leveraging IoT sensor data.
• Predict new high-value crop strains based on past crops, weather/soil
trends, and other data.
• Construct detailed map of farm characteristics based on aerial video.
• Intrusion detection from video.
37. ML in energy
• Predict failure and recommend proactive maintenance for mining,
drilling, power generation, and moving equipment.
• Replicate human-made decisions to control room environments to
reduce cost.
• Optimize energy scheduling/dispatch of power plants based on
energy pricing, weather, and other real-time data.
• Predict energy demand.
38. ML in healthcare
• Diagnose known diseases from scans, biopsies, audio, and other data.
• Predict personalized health outcomes to optimize recommended
treatment.
• Identify fraud, waste, and abuse patterns in clinical and operations data.
• Detect major trauma events from wearables sensor data and signal
emergency response.
• Optimize design of clinical trials.
• Predict outcomes from fewer or diverse (e.g., animal testing) experiments
• Identify target patient subgroups that are underserved (e.g., not
diagnosed).
39. ML in public service and social sector
• Optimize public resource allocation for urban development to improve
quality of life. (e.g., reduce traffic, minimize pollution)
• Replicate back-office decision processes for applications, permits and tax
auditing.
• Predict individualized educational and career paths to maximize
engagement and success.
• Predict risk of failure for physical assets (e.g., military, infrastructure) and
recommend proactive maintenance.
• Predict risk of illicit activity or terrorism using historical crime data,
intelligence data, and other available sources (e.g., predictive policing).
40. ML in media
• Discover new trends in consumption patterns. Serve content and
advertisements.
• Optimize pricing for services/offerings based on customer-specific
data.
41. ML in telecom
• Predict regional demand trends for voice/data/other traffic.
• Discover new trends in consumer behaviour using mobile data and
other relevant data.
42. ML in logistics
• Read addresses/bar codes in mail/parcel sorting
• Identify performance and risk for drivers/pilots through driving
patterns.
• Personalize loyalty programs and promotional offerings to individual
customers.
• Predict failure and recommend proactive maintenance for planes,
trucks, and other moving equipment.
• Optimize pricing and scheduling based on real-time demand updates.
43. Acknowledgements
• Images and slides taken from various deep learning courses.
• Use cases in various industries taken from Mckinsey Analytics survey.
• This presentation and is created for deep learning audience for no
monetary benefits. Please inform the uploader if you want some part
to be taken out.
44. Obtaining an understanding of the human mind is
one of the final frontiers of modern science.
Thanks
Adwait Bhave