Google announces the open source of MobileNe : Primarily focus on optimizing for latency but also yield small networks. https://arxiv.org/abs/1704.04861
This material is to serve as guide reading of the paper.
The document discusses computer vision with deep learning. It provides an overview of convolutional neural networks and their use in computer vision applications like image classification and object detection. Specifically, it discusses how CNNs use convolutional layers to learn visual features from images and provide examples of CNNs being used for pipeline defect classification and filler cap quality control.
Animation is created by displaying a series of pictures or frames to simulate movement. Rendering is the process used to create the finished images and involves compiling the frames. Popular software for 3D animation includes 3D Studio Max, Blender, and Maya. Render farms with multiple computers are often used to render complex animations more quickly through distributed processing. Motion capture can also be used to record movement that is then applied to digital models. Animation has applications in areas like films, games, television, design, and education.
This document discusses data modeling and the eXERD data modeling tool. It provides definitions of data modeling, database modeling, and database modeling tools. Data modeling is used to simplify, visualize and document real-world information for computerization by determining the scope and relationships of entities. eXERD is introduced as a database modeling tool for everyone that is less expensive than other professional tools and more user-friendly. It automates repetitive tasks and allows for intuitive editing and integration with other tools.
Neural networks are modeled after the human brain and are useful for problems involving pattern recognition and classification. They involve input data passing through multiple layers of simulated neurons that are activated based on the input. Neural networks have achieved remarkable results in applications like speech recognition, image recognition, driving cars, predicting house prices, and targeting online advertisements. Larger neural networks are formed by connecting many single neuron units together. For example, a simple neural network could take inputs like a house's size, number of bedrooms, zip code, and school quality to predict the house's price.
The document provides an overview of an introduction to computer graphics course. It discusses topics that will be covered like the history and applications of computer graphics, hardware concepts, 2D and 3D algorithms, modeling curves and 3D objects, animation, and textbooks. It also defines computer graphics and compares image processing versus computer graphics.
Deep learning is an artificial intelligence technique that imitates the human brain in processing data to recognize patterns and make decisions. It has transformed industries like web search and advertising and is enabling new products and businesses. Deep learning uses neural networks and applications like medicine, self-driving cars, agriculture, and more. It is considered a highly sought after skill and part of the rapid rise of artificial intelligence. This document provides an overview of deep learning and what topics will be covered, including neural networks, deep neural networks, improving models, structuring machine learning projects, convolutional neural networks, and natural language processing.
This document discusses computer vision and how it allows computers to understand digital images. It explains that computer vision uses deep learning techniques like convolutional neural networks (CNNs) to analyze images in a way that is similar to the human brain. CNNs break images down into pixel matrices and apply filters to detect patterns at different levels, from edges to more complex objects. The document outlines some major computer vision techniques, including image classification, object detection, object tracking, and semantic segmentation. It provides medical image analysis as a prominent application of computer vision.
Google announces the open source of MobileNe : Primarily focus on optimizing for latency but also yield small networks. https://arxiv.org/abs/1704.04861
This material is to serve as guide reading of the paper.
The document discusses computer vision with deep learning. It provides an overview of convolutional neural networks and their use in computer vision applications like image classification and object detection. Specifically, it discusses how CNNs use convolutional layers to learn visual features from images and provide examples of CNNs being used for pipeline defect classification and filler cap quality control.
Animation is created by displaying a series of pictures or frames to simulate movement. Rendering is the process used to create the finished images and involves compiling the frames. Popular software for 3D animation includes 3D Studio Max, Blender, and Maya. Render farms with multiple computers are often used to render complex animations more quickly through distributed processing. Motion capture can also be used to record movement that is then applied to digital models. Animation has applications in areas like films, games, television, design, and education.
This document discusses data modeling and the eXERD data modeling tool. It provides definitions of data modeling, database modeling, and database modeling tools. Data modeling is used to simplify, visualize and document real-world information for computerization by determining the scope and relationships of entities. eXERD is introduced as a database modeling tool for everyone that is less expensive than other professional tools and more user-friendly. It automates repetitive tasks and allows for intuitive editing and integration with other tools.
Neural networks are modeled after the human brain and are useful for problems involving pattern recognition and classification. They involve input data passing through multiple layers of simulated neurons that are activated based on the input. Neural networks have achieved remarkable results in applications like speech recognition, image recognition, driving cars, predicting house prices, and targeting online advertisements. Larger neural networks are formed by connecting many single neuron units together. For example, a simple neural network could take inputs like a house's size, number of bedrooms, zip code, and school quality to predict the house's price.
The document provides an overview of an introduction to computer graphics course. It discusses topics that will be covered like the history and applications of computer graphics, hardware concepts, 2D and 3D algorithms, modeling curves and 3D objects, animation, and textbooks. It also defines computer graphics and compares image processing versus computer graphics.
Deep learning is an artificial intelligence technique that imitates the human brain in processing data to recognize patterns and make decisions. It has transformed industries like web search and advertising and is enabling new products and businesses. Deep learning uses neural networks and applications like medicine, self-driving cars, agriculture, and more. It is considered a highly sought after skill and part of the rapid rise of artificial intelligence. This document provides an overview of deep learning and what topics will be covered, including neural networks, deep neural networks, improving models, structuring machine learning projects, convolutional neural networks, and natural language processing.
This document discusses computer vision and how it allows computers to understand digital images. It explains that computer vision uses deep learning techniques like convolutional neural networks (CNNs) to analyze images in a way that is similar to the human brain. CNNs break images down into pixel matrices and apply filters to detect patterns at different levels, from edges to more complex objects. The document outlines some major computer vision techniques, including image classification, object detection, object tracking, and semantic segmentation. It provides medical image analysis as a prominent application of computer vision.
This document provides an introduction to a computer graphics course. It defines computer graphics as using computers to communicate information visually in a managed and documented way. The course will cover topics like 2D and 3D viewing, modeling, rendering, animation and projects using OpenGL. It will include lectures, labs and assignments. The goal is to provide an overview of the key concepts and activities for the course.
This document provides lecture notes for a computer graphics course. It includes:
- An overview of the course description, prerequisites, objectives and outcomes.
- A taxonomy of different types of computer graphics such as static vs dynamic, color vs black and white, etc.
- Details of lecture topics such as drawing techniques, output picture types, and algorithms for drawing basic shapes.
- Programming assignments for students such as drawing lines and trees, and developing a game engine.
Augmented reality (AR) combines computer-generated information with the user's natural senses to enhance their view of the real world. AR works by picking a real-world scene, adding virtual objects to it, and visualizing the digital objects as if they were real. AR has applications in marketing, travel navigation, medical, education, entertainment, and space. While AR allows users to interact with virtual and real worlds simultaneously in real-time and can save time and effort, it is still under development with concerns about privacy, social acceptance, and potential misuse.
This is the presentation of first lecture in lecture series on digital image processing.Hope this is of your use
Regards
The Electronics Club (TEC)
VIT university
Computer graphics refers to creating and manipulating pictures and drawings using a computer. There are two main types: passive graphics which have no interaction and active graphics which allow two-way communication and interaction between the user and hardware. Computer graphics has many applications including user interfaces, scientific visualization, animation, computer aided design, presentation graphics, image processing, and education/training.
AI: The New Electricity to Harness Our Digital FutureDevdatt Dubhashi
- AI is being described as the "new electricity" that will transform industry in the same way electricity did 100 years ago.
- There have been significant advances in deep learning and neural networks that have enabled major improvements in machine translation, reducing errors by 55-85%.
- Researchers are developing techniques using word vectors and neural context embeddings to perform tasks like word sense induction, document summarization, and developing grounded languages through machine interactions.
- AI has the potential to be widely and broadly applied across industries in the same way electricity was, and data is being described as the "new oil" that will fuel further AI advances.
This document provides an introduction to computer graphics. It discusses graphics and computer graphics, including raster graphics and vector graphics. It also discusses animation and computer animation. Dimensions including one, two, and three dimensions are defined. The objectives of the introduction to computer graphics class are explained, which will include 3DS Max modeling, activities in Photoshop and InDesign, and an introduction to computer animation. The document provides definitions and examples of key graphics and animation terms.
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 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.
The document provides an overview of artificial intelligence (AI) concepts and applications through a 4-module online course. Module 1 defines AI and common applications like healthcare, education, and customer service. Module 2 covers machine learning, deep learning, neural networks, and their various applications. Module 3 discusses issues around AI including privacy, job disruption, bias, and ethics. Module 4 explores the future of AI and how to start a career in the field.
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.
In this presentation, we walk through what is Deep Learning in General, we see the anatomy of a typical Deep Learning Neural Network, how is it trained, how do we get the inference, optimisation of parameters, and regularising it. Then we dive deep into the Face Recognition technology, different paradigms and aspects of it. How do we train it, how are the features extracted, etc. We talk about the security as well.
The document provides an overview of artificial intelligence and machine learning techniques for image classification using small datasets. It describes how to build a basic convolutional neural network from scratch or fine-tune a pre-trained model like VGG16 to classify images of cats and dogs with only 2000 training examples. Fine-tuning the top layers of VGG16 improved accuracy from 79% using just bottleneck features to 98%, showing how transfer learning can boost performance for small datasets.
This document provides an introduction to a computer graphics course. It defines computer graphics as using computers to communicate information visually in a managed and documented way. The course will cover topics like 2D and 3D viewing, modeling, rendering, animation and projects using OpenGL. It will include lectures, labs and assignments. The goal is to provide an overview of the key concepts and activities for the course.
This document provides lecture notes for a computer graphics course. It includes:
- An overview of the course description, prerequisites, objectives and outcomes.
- A taxonomy of different types of computer graphics such as static vs dynamic, color vs black and white, etc.
- Details of lecture topics such as drawing techniques, output picture types, and algorithms for drawing basic shapes.
- Programming assignments for students such as drawing lines and trees, and developing a game engine.
Augmented reality (AR) combines computer-generated information with the user's natural senses to enhance their view of the real world. AR works by picking a real-world scene, adding virtual objects to it, and visualizing the digital objects as if they were real. AR has applications in marketing, travel navigation, medical, education, entertainment, and space. While AR allows users to interact with virtual and real worlds simultaneously in real-time and can save time and effort, it is still under development with concerns about privacy, social acceptance, and potential misuse.
This is the presentation of first lecture in lecture series on digital image processing.Hope this is of your use
Regards
The Electronics Club (TEC)
VIT university
Computer graphics refers to creating and manipulating pictures and drawings using a computer. There are two main types: passive graphics which have no interaction and active graphics which allow two-way communication and interaction between the user and hardware. Computer graphics has many applications including user interfaces, scientific visualization, animation, computer aided design, presentation graphics, image processing, and education/training.
AI: The New Electricity to Harness Our Digital FutureDevdatt Dubhashi
- AI is being described as the "new electricity" that will transform industry in the same way electricity did 100 years ago.
- There have been significant advances in deep learning and neural networks that have enabled major improvements in machine translation, reducing errors by 55-85%.
- Researchers are developing techniques using word vectors and neural context embeddings to perform tasks like word sense induction, document summarization, and developing grounded languages through machine interactions.
- AI has the potential to be widely and broadly applied across industries in the same way electricity was, and data is being described as the "new oil" that will fuel further AI advances.
This document provides an introduction to computer graphics. It discusses graphics and computer graphics, including raster graphics and vector graphics. It also discusses animation and computer animation. Dimensions including one, two, and three dimensions are defined. The objectives of the introduction to computer graphics class are explained, which will include 3DS Max modeling, activities in Photoshop and InDesign, and an introduction to computer animation. The document provides definitions and examples of key graphics and animation terms.
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 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.
The document provides an overview of artificial intelligence (AI) concepts and applications through a 4-module online course. Module 1 defines AI and common applications like healthcare, education, and customer service. Module 2 covers machine learning, deep learning, neural networks, and their various applications. Module 3 discusses issues around AI including privacy, job disruption, bias, and ethics. Module 4 explores the future of AI and how to start a career in the field.
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.
In this presentation, we walk through what is Deep Learning in General, we see the anatomy of a typical Deep Learning Neural Network, how is it trained, how do we get the inference, optimisation of parameters, and regularising it. Then we dive deep into the Face Recognition technology, different paradigms and aspects of it. How do we train it, how are the features extracted, etc. We talk about the security as well.
The document provides an overview of artificial intelligence and machine learning techniques for image classification using small datasets. It describes how to build a basic convolutional neural network from scratch or fine-tune a pre-trained model like VGG16 to classify images of cats and dogs with only 2000 training examples. Fine-tuning the top layers of VGG16 improved accuracy from 79% using just bottleneck features to 98%, showing how transfer learning can boost performance for small datasets.
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
Barcodes and image recognition technology are examples of machine-readable representations of data. Barcodes use a pattern of bars and spaces that can be read by optical scanners to identify numbers and letters. Image recognition allows computers to identify objects in images through techniques like deep learning, which automatically extracts features from image data. Face recognition is a type of image recognition that extracts features from facial images and compares them to identify individuals, using algorithms like ResNet that represent faces as vectors and compare their Euclidean distances.
This document provides information about Olivier Duchenne and his experience and qualifications. It summarizes his educational background which includes a Ph.D in Computer Science from ENS Paris/INRIA and a postdoctoral fellowship at Carnegie Mellon University. It also lists his professional experience which includes positions at NEC Labs, Intel, and as a co-founder of Solidware. The document then provides guidelines for machine learning and discusses challenges such as having enough and changing data. It explores the history and reasons for increased use of machine learning in computer vision.
Traditional Machine Learning had used handwritten features and modality-specific machine learning to classify images, text or recognize voices. Deep learning / Neural network identifies features and finds different patterns automatically. Time to build these complex tasks has been drastically reduced and accuracy has exponentially increased because of advancements in Deep learning. Neural networks have been partly inspired from how 86 billion neurons work in a human and become more of a mathematical and a computer problem. We will see by the end of the blog how neural networks can be intuitively understood and implemented as a set of matrix multiplications, cost function, and optimization algorithms.
This covers a end-to-end coverage of neural networks,CNN internals , Tensorflow and Keras basic , intution on object detection and face recognition and AI on Android x86.
Computer vision uses algorithms to understand and process images in a similar way that the human brain does. Convolutional neural networks (CNNs) are commonly used, where an image is broken down into smaller pixel groups called filters. Each layer of a CNN detects different patterns in the image, like edges or curves. Recurrent neural networks (RNNs) are also used to analyze dynamic images like videos by feeding large datasets across different angles. Computer vision has many applications including traffic monitoring, object detection, emotion analysis, and more. It requires Python, OpenCV, NumPy and other libraries to build computer vision systems and models.
This presentation by Juraj Čorba, Chair of OECD Working Party on Artificial Intelligence Governance (AIGO), was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
Carrer goals.pptx and their importance in real lifeartemacademy2
Career goals serve as a roadmap for individuals, guiding them toward achieving long-term professional aspirations and personal fulfillment. Establishing clear career goals enables professionals to focus their efforts on developing specific skills, gaining relevant experience, and making strategic decisions that align with their desired career trajectory. By setting both short-term and long-term objectives, individuals can systematically track their progress, make necessary adjustments, and stay motivated. Short-term goals often include acquiring new qualifications, mastering particular competencies, or securing a specific role, while long-term goals might encompass reaching executive positions, becoming industry experts, or launching entrepreneurial ventures.
Moreover, having well-defined career goals fosters a sense of purpose and direction, enhancing job satisfaction and overall productivity. It encourages continuous learning and adaptation, as professionals remain attuned to industry trends and evolving job market demands. Career goals also facilitate better time management and resource allocation, as individuals prioritize tasks and opportunities that advance their professional growth. In addition, articulating career goals can aid in networking and mentorship, as it allows individuals to communicate their aspirations clearly to potential mentors, colleagues, and employers, thereby opening doors to valuable guidance and support. Ultimately, career goals are integral to personal and professional development, driving individuals toward sustained success and fulfillment in their chosen fields.
This presentation by Nathaniel Lane, Associate Professor in Economics at Oxford University, was made during the discussion “Pro-competitive Industrial Policy” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/pcip.
This presentation was uploaded with the author’s consent.
XP 2024 presentation: A New Look to Leadershipsamililja
Presentation slides from XP2024 conference, Bolzano IT. The slides describe a new view to leadership and combines it with anthro-complexity (aka cynefin).
Collapsing Narratives: Exploring Non-Linearity • a micro report by Rosie WellsRosie Wells
Insight: In a landscape where traditional narrative structures are giving way to fragmented and non-linear forms of storytelling, there lies immense potential for creativity and exploration.
'Collapsing Narratives: Exploring Non-Linearity' is a micro report from Rosie Wells.
Rosie Wells is an Arts & Cultural Strategist uniquely positioned at the intersection of grassroots and mainstream storytelling.
Their work is focused on developing meaningful and lasting connections that can drive social change.
Please download this presentation to enjoy the hyperlinks!
This presentation by OECD, OECD Secretariat, was made during the discussion “Pro-competitive Industrial Policy” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/pcip.
This presentation was uploaded with the author’s consent.
This presentation by OECD, OECD Secretariat, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
Suzanne Lagerweij - Influence Without Power - Why Empathy is Your Best Friend...Suzanne Lagerweij
This is a workshop about communication and collaboration. We will experience how we can analyze the reasons for resistance to change (exercise 1) and practice how to improve our conversation style and be more in control and effective in the way we communicate (exercise 2).
This session will use Dave Gray’s Empathy Mapping, Argyris’ Ladder of Inference and The Four Rs from Agile Conversations (Squirrel and Fredrick).
Abstract:
Let’s talk about powerful conversations! We all know how to lead a constructive conversation, right? Then why is it so difficult to have those conversations with people at work, especially those in powerful positions that show resistance to change?
Learning to control and direct conversations takes understanding and practice.
We can combine our innate empathy with our analytical skills to gain a deeper understanding of complex situations at work. Join this session to learn how to prepare for difficult conversations and how to improve our agile conversations in order to be more influential without power. We will use Dave Gray’s Empathy Mapping, Argyris’ Ladder of Inference and The Four Rs from Agile Conversations (Squirrel and Fredrick).
In the session you will experience how preparing and reflecting on your conversation can help you be more influential at work. You will learn how to communicate more effectively with the people needed to achieve positive change. You will leave with a self-revised version of a difficult conversation and a practical model to use when you get back to work.
Come learn more on how to become a real influencer!
Mastering the Concepts Tested in the Databricks Certified Data Engineer Assoc...SkillCertProExams
• For a full set of 760+ questions. Go to
https://skillcertpro.com/product/databricks-certified-data-engineer-associate-exam-questions/
• SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
• It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
• SkillCertPro updates exam questions every 2 weeks.
• You will get life time access and life time free updates
• SkillCertPro assures 100% pass guarantee in first attempt.
This presentation by Yong Lim, Professor of Economic Law at Seoul National University School of Law, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
This presentation by Thibault Schrepel, Associate Professor of Law at Vrije Universiteit Amsterdam University, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
This presentation by OECD, OECD Secretariat, was made during the discussion “Competition and Regulation in Professions and Occupations” held at the 77th meeting of the OECD Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found at oe.cd/crps.
This presentation was uploaded with the author’s consent.
Why Psychological Safety Matters for Software Teams - ACE 2024 - Ben Linders.pdfBen Linders
Psychological safety in teams is important; team members must feel safe and able to communicate and collaborate effectively to deliver value. It’s also necessary to build long-lasting teams since things will happen and relationships will be strained.
But, how safe is a team? How can we determine if there are any factors that make the team unsafe or have an impact on the team’s culture?
In this mini-workshop, we’ll play games for psychological safety and team culture utilizing a deck of coaching cards, The Psychological Safety Cards. We will learn how to use gamification to gain a better understanding of what’s going on in teams. Individuals share what they have learned from working in teams, what has impacted the team’s safety and culture, and what has led to positive change.
Different game formats will be played in groups in parallel. Examples are an ice-breaker to get people talking about psychological safety, a constellation where people take positions about aspects of psychological safety in their team or organization, and collaborative card games where people work together to create an environment that fosters psychological safety.
This presentation by Professor Alex Robson, Deputy Chair of Australia’s Productivity Commission, was made during the discussion “Competition and Regulation in Professions and Occupations” held at the 77th meeting of the OECD Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found at oe.cd/crps.
This presentation was uploaded with the author’s consent.
2. • AI = New Electricity By Andrew NG
• What was meaning of CEO?
• Chief Engineering Officer
• Chief Electrical Officer
• New Product = Any Product + Electricity
• Now New Product = Any Product + AI
3. Some more Quotes about AI
• AI + Product means People should be able to interact
with computing in a natural and seamless way. – Sundar
Pichai
• The world's first trillionaire will be an AI entrepreneur –
Mark Cuban
• Predicting the future isn’t magic, it’s artificial intelligence.
- Dave Waters
4. AI Examples
Auto Tagging
Self Driving Car
Translation
E-commerce Recommendation
Gmail Smart Reply q
Windows Cortana , Apple Siri : Virtual Assistant
Prisma photo editor : Image to painting
https://www.youtube.com/watch?v=Y2VF8tmLFHw
6. AI ? ML ? DL ?
Artificial Intelligence,
deep learning, machine learning
— whatever you’re doing
if you don’t understand it
— learn it.
Because otherwise you’re going
to be a dinosaur
within 3 years. - Mark Cuban
15. Convolutional Neural Network
1. CNN is a feed-forward network that can extract topological properties
from an image.
2. Like almost every other neural networks they are trained with a version of
the back-propagation algorithm.
3. Convolutional Neural Networks are designed to recognize visual patterns
directly from pixel images with minimal preprocessing.
4. They can recognize patterns with extreme variability (such as handwritten
characters).
16. Deep Learning
1. Deep Learning can contain a number of layers of Neural Networks.
2. The input to each layer (two-dimensional arrays) looks a lot like the
output (two-dimensional arrays) from previous layer.
3. Each layer have a number of steps like Pooling, Convolution,
Rectification, Normalization, etc.
18. Convolution
So we will get maximum outputs at the pixels, where the
pattern matches with filter pixels.
19. Pooling
Pooling involves stepping a small
window across an image and taking
the maximum / average value from
the window at each step called
Strides.
So 2 types of pooling:
- Average Pooling
- Max Pooling
20. Rectified Linear Units (ReLU)
1. ReLU is most used Activation
function in Neural Networks.
2. It’s math is very simple—
wherever a negative number
occurs, swap it out for a 0.
3. This helps the CNN stay
mathematically healthy.
21. Fully connected layers
1. Fully connected layers take the
high-level filtered images and
translate them into categories.
22. CNN with MNIST Dataset
https://github.com/Hvass-Labs/TensorFlow-
Tutorials/blob/master/02_Convolutional_Neural_Network.ipynb