Machine learning ppt 2019

For a quick overview of a subject or a breakdown of concepts, SlideShare serves as a go-to platform for many. The recapitulations found in many of the presentations are both concise and informative.

Deep Learning In 5 Minutes - What Is Deep Learning? - Deep Learning Explained Simply - Simplilearn

The most popular presentations are the ones that have received the most number of likes and have been viewed more than the other presentations in a particular category. Deep Learning and everything else in between. People who are not aware of what artificial intelligence is will find the topic presented in a very simple manner here.

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Along with the explanation of what AI is, the two major approaches towards AI are discussed— logic and rules-based approach, and machine learning approach. Special emphasis on machine learning approach can be seen in the slides devoted to its detailed examination. The examination goes beyond the rudimentary explanation of what machine learning is and presents examples of proxies that seem like machine learning but are not.

The presentation lists examples of AI in the field of law and identifies some of the limitations of AI technology. For the uninitiated, this presentation offers an ideal rundown of AI. The question of AI being a threat is raised at the very beginning. However, as the presentation progresses, it discusses the basics necessary for understanding AI. The most basic question of what is artificial intelligence is answered.

machine learning ppt 2019

A brief history of AI and the discussion on recent advances in the field of AI is also found. The various areas where AI currently sees practical application have been listed. Fascinating uses that AI can be put to in the future are also found in the presentation.

The two approaches of achieving AI, machine learning and deep learning, is touched upon. All in all, this presentation serves as a simple introduction to AI.

An exciting application of AI can be found in chatbots. Here, the limitless scope of chatbots is explored. The evolution of chatbots and its absorption of more AI in the future is also looked into. E-Commerce is touted as the biggest beneficiary of the advancement in chatbots and that bot technology will owe its rise to services and commerce. Two tech giants, Facebook and Google, have been pitted against each other based on their ongoing developments in this area and the question of who will emerge as the best is raised.

In order to derive a better understanding of this presentation, it is advisable to first watch the original talk.

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During the course of the presentation, many examples of how machines can learn and perform any human task that is repetitive in nature are cited. Other possibilities suggested include the creation of new unheard jobs for human beings as a result of aggressive use of AI and other allied technologies. Qualities that are characteristic only of human beings, may be the basis on which these jobs will be created is also suggested.

In this presentation, Carol Smith establishes that AI cannot replace humans. Smith conveys that AI can serve the purpose of enabling human beings in making better decisions. The slides talk about how the actions of AI are the result of the human inputs going into its programming.

Other issues such as the need for regulations and other considerations within it that require deliberation are also touched upon. Though no descriptive breakdown of topics related to AI is found, the presentation offers interesting numerical insights into many questions.The problem was decidedly oxymoronic.

Every vendor was hyping some variation of machine learning as part of its purported Artificial Intelligence offerings; libraries of models and Machine Learning as a Service have become some of the most in demand cloud options.

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If [they] can just get their data then [they] can put it into this format and put it through the model. The days in which enterprise use of machine learning is circumscribed by a lack of knowledge and surfeit of latency of effective data preparation are set to end in The immense amount of training data necessary to build accurate machine learning models has long been one of the most consistent impediments to leveraging this technology.

There are numerous approaches for organizations to scale to utilize as much training data as necessary for machine learning models, including graph technology. It cannot be repetitive, because then there is no new learning. There are various attributes that you need to have. These models will also engender biased outputs if the selection process of the input data is flawed. This way, data scientists can see potential biases in datasets and rectify them before inputting training data into machine learning models.

Intelligent algorithms can also redress issues of data quality which may contribute to poor model quality. We compute of these in real time as calls come. As Martin alluded to, part of the challenge of feature engineering is simply standardizing data in a presentable form to readily extract features from them.

Equipping machine learning models with dependable, unbiased training datasets for reliable outcomes is unambiguously the most difficult aspect of deploying this transformative technology.

Graph mechanisms and visual approaches to managing data can empower organizations to access data at the scale required for credible machine learning inputs, facilitate feature selection, and assist with overall data quality measures important for this statistical branch of AI. The greater accessibility and overall ease of implementing machine learning attributed to the methods described above will certainly broaden the array of machine learning inputs and outputs in the coming year.

Other fascinating machine learning deployments yielding tangible business value involve image and video recognition use cases in which certain information—including that which is personally identifiable—is redacted. So you have to train [machine learning models] to find it, no matter where it is.

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Jelani Harper is an editorial consultant servicing the information technology market, specializing in data-driven applications focused on semantic technologies, data governance and analytics.

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machine learning ppt 2019

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When does the lockdown end? In Argentina, AI could decide.

10 Popular Cybersecurity Presentations On Slideshare You Should Refer To

Upcoming webinar: Seizing the AI business opportunity. Adjacent future: Trust in data science, machine learning and artificial intelligence. Informing machine learning with time series analysis. This simulation shows the incredible effectiveness of low-tech measures against Covid All eBooks Omdia Whitepapers. Data Science View.After you enable Flash, refresh this page and the presentation should play.

Get the plugin now. Toggle navigation. Help Preferences Sign up Log in. To view this presentation, you'll need to allow Flash.

machine learning ppt 2019

Click to allow Flash After you enable Flash, refresh this page and the presentation should play. View by Category Toggle navigation. Products Sold on our sister site CrystalGraphics. Title: Machine Learning. Tags: applications fuzzy learning machine. Latest Highest Rated. Title: Machine Learning 1 Machine Learning Artificial Intelligence Department of Industrial Engineering and Management Cheng Shiu University 2 Outline Artificial intelligence in 21st century Learning Machine learning Supervised learning How brain works Neural network and Artificial neural networks Simple neuron - Perceptron 3 Artificial Intelligence The capacity of a computer to perform operations analogous to learning and decision making in humans, as by an expert system, a program for CAD or CAM, or a program for the perception and recognition of shapes in computer vision systems 4 Business Intelligence The process of gathering information about a business or industry matter a broad range of applications and technologies for gathering, storing, analyzing, and providing access to data to help make business decisions BI 5 Computational Intelligence An offshoot of artificial intelligence.

Several expressions compete to name the same interdisciplinary area. It is difficult, if not impossible, to accommodate in a formal definition disparate areas with their own established individualities such as fuzzy sets, neural networks, evolutionary computation, machine learning, Bayesian reasoning, etc.

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Bringing together diverse expertise and experience can enrich each of the participating discipline and foster new research perspectives in the broad field of Computational Intelligence. Zadeh " The ease with which they can learn led to attempts to emulate a biological neural network in a computer. Learning capabilities can improve the performance of an intelligent system over time. The most popular approaches to machine learning are artificial neural networks and genetic algorithms.

The brain consists of a densely interconnected set of nerve cells, or basic information-processing units, called neurons. The human brain incorporates nearly 10 billion neurons and 60 trillion connections, synapses, between them. By using multiple neurons simultaneously, the brain can perform its functions much faster than the fastest computers in existence today. Each neuron has a very simple structure, but an army of such elements constitutes a tremendous processing power.

A neuron consists of a cell body, soma, a number of fibers called dendrites, and a single long fiber called the axon. Information is stored and processed in a neural network simultaneously throughout the whole network, rather than at specific locations.

Machine Learning - PowerPoint PPT Presentation

In other words, in neural networks, both data and its processing are global rather than local. The neurons are connected by weighted links passing signals from one neuron to another. The output signal is transmitted through the neurons outgoing connection.Securing networks and protecting data from breaching has become one of the crucial motives in an organisation. Cybercrime has eventually risen with the widespread use of emerging technologies and we have been witnessing data breaches and other hacks since a few years now.

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In this article, we list down 10 popular presentations on cybersecurity one must read on Slideshare. This presentation was published in March by Olivier Busolini, a cybersecurity professional who also works with AI in cybersecurity.

This presentation includes a basic introduction to AI, an overview of AI technologies, an overview of machine learning underlying technologies, basics of deep learning, introduction to red and blue AI, emerging usages of blue AI, difficulties faced during developing AI solutions and tips for cybersecurity strategy. This powerpoint presentation was published by Lipsita Behera, a software developer and it has gathered more thanviews till now.

In this PPT, you can understand the basics of cybersecurity such as how cybersecurity emerged, know about cyber threat evolution, types of cybercrime, how to take preventive measures in order to control the threats, learn various security reasons as well as methods.

machine learning ppt 2019

This presentation has got more than one lakh viewers and more than clipping. Here, the author introduces the basics of cybersecurity and what is the actual need of cybersecurity, what are the major security problems, different viruses and worms and its solutions, brief introduction of hackers and measures to prevent hacking, what are malware and steps to stop malware, what are trojan horses and safety measures to avoid trojans, password cracking and securing password, cybersecurity strategy in India and much more.

This presentation discusses the threats with AI and machine learning. This powerpoint presentation was published by Aeman Khan, an automation test engineer and it has crossed overviews till now. Here, the writer discusses the basics of cybersecurity such as its introduction, history, the various categories of cybercrime, its types, how cybersecurity threatens national security, advantages of cybercrime and other safety tips to cybercrimes including cyber law in India.

This ppt is published by Bijay Bhandari, an engineer and project manager by profession. It has been viewed for overtimes where the author discusses how to take action against cybercrime. You will know about the basics of cybersecurity and cybercrime, what constitutes cyber crimes, protection measures for cybercrime, advantages of cybersecurity and various safety measures.

This presentation has been viewed for overtimes and it includes a basic introduction to cybercrime. You will get to know about the variants of cybercrime such as phishing, vishing, cyberstalking, cost of cybercrime in India, cyber laws, various ACTs including the preventive measures for such activities. This presentation, an overview of artificial intelligence in cybersecurity was published in June by Olivier Busolini, a cybersecurity professional.

Here, he talks about artificial imitation augmented intelligence, basic types of AI and machine learning, difficulties to develop AI solutions, challenges in machine learning, AI in cybersecurity, key flaws of cybersecurity, AI risk framework, basic introduction to defensive AI, adversarial AI as well as offensive AI and much more.

The security automation and machine learning ppt is published by Siemplify, a security platform in January this year and has got more than viewers. It includes a basic introduction to cybersecurity and machine learning, types of machine learning techniques, security analysis for machine learning, threats on machine learning, machine learning for prevention, detection, incident response and SOC management. The role of big data, AI and ML in cyber intelligence ppt was published by Aladdin Dandis, an information security manager who gives a brief introduction to cyber intelligence, raw threat data and threat intelligence, understanding AI and machine learning drivers, various kinds of cybersecurity options such as phishing, anti-malware, fraud detection, cyber intelligence framework and its challenges.

A lover of music, writing and learning something out of the box. Contact: ambika. The list is in alphabetical order.Machine Learning is a key to develop intelligent systems and analyze data in science and engineering. Machine Learning engines enable intelligent technologies such as Siri, Kinect or Google self driving car, to name a few. This course provides an introduction to the fundamental methods at the core of modern Machine Learning.

It covers theoretical foundations as well as essential algorithms. Classes on theoretical and algorithmic aspects are complemented by practical lab sessions. Related courses: RegML website.

MIT 9. This is a term long course of roughly 25 lectures offered to graduate students at MIT. Undergraduate term-long introductory Machine Learning course offered at the University of Genova.

One day introduction to the essential concepts and algorithms at the core of modern Machine Learning. MLCC master page. Morning classes will be held in room Afternoon labs will take place in rooms SW1, SW2, and Genova is the capital of Liguriain the heart of Italian Riviera. Here is a list of places where you can go for lunch. For more info write to: vigogna [at] dibris [dot] unige [dot] it cristian [dot] rusu [at] iit [dot] it raffaello [dot] camoriano [at] iit [dot] it.

Drawing meaningful conclusions on the way complex real life phenomena work and being able to predict the behavior of systems of interest require developing accurate and highly interpretable mathematical models whose parameters need to be estimated from observations. This tutorial will introduce probabilistic models based on Gaussian processes as attractive tools to tackle these challenges in a principled way and to allow for a sound quantification of uncertainty.

The tutorial will formally define Gaussian processes starting from the formulation of Bayesian linear models with infinite basis functions, and draw connections with non-probabilistic kernel machines and deep neural networks. Carrying out inference for Gaussian processes poses huge computational challenges that arguably hinder their wide adoption. In recent years, however, have been a considerable amount of novel contributions that are allowing Gaussian processes to be applied to problems at an unprecedented scale and to new areas where uncertainty quantification is of fundamental importance.

This tutorial will expose attendees to such recent advances, trends and challenges in Gaussian process modeling and inference, and stimulate the debate about the role of Gaussian process models in solving complex modern machine-learning tasks where deep neural networks are currently the preferred choice.

Generative models of images have made an extraordinary amount of progress over the past five years, moving from vaguely plausible images of handwritten digit to nearly-photorealistic pictures of imaginary people. This tutorial will cover the key line of work, generative adversarial networks and their variants.

We will discuss the original algorithm, theoretical issues with its foundations, and various approaches to resolving them, including the Wasserstein GAN and recent kernel-based improvements.

14 Most Popular Presentations On Artificial Intelligence And Machine Learning On SlideShare

For any inquiries, please write to mlccapplications gmail. Toggle navigation MLCC Basic Info. Genova Genova is the capital of Liguriain the heart of Italian Riviera.

Lunch Here is a list of places where you can go for lunch. Extra For more info write to: vigogna [at] dibris [dot] unige [dot] it cristian [dot] rusu [at] iit [dot] it raffaello [dot] camoriano [at] iit [dot] it. Maurizio Filippone EURECOM Introduction to Gaussian Processes Drawing meaningful conclusions on the way complex real life phenomena work and being able to predict the behavior of systems of interest require developing accurate and highly interpretable mathematical models whose parameters need to be estimated from observations.

Dougal Sutherland Gatsby Computational Neuroscience Unit Adversarial generative models of images Generative models of images have made an extraordinary amount of progress over the past five years, moving from vaguely plausible images of handwritten digit to nearly-photorealistic pictures of imaginary people. Gold Sponsor. Tutto nello stesso posto. Silver Sponsors. AKQA is a communication agency that deals with customer experience.Researchers at deepsense.

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Otherwise you will be prompted again when opening a new browser window or new a tab. Other external services. Privacy Policy. You can read about our cookies and privacy settings in detail on our Privacy Policy Page.With a new year upon us, I thought it would be a good time to revisit the concept and put together a new learning path for mastering machine learning with Python.

With these 7 steps you can master basic machine learning with Python! There is an awful lot of freely-available material out there for folks who are interested in a crash course in machine learning with Python. Some time ago I wrote 7 Steps to Mastering Machine Learning With Python and 7 More Steps to Mastering Machine Learning With Pythona pair of posts which attempted to aggregate and organize some of this available quality material into just such a crash course.

However, these posts are getting stale, having been around for a few years at this point. This time around, we will split the path up into 3 posts, one each for basic, intermediate and advanced topics. Let's make sure we view these terms in a relative sense, however, and not expect to be research-caliber machine learning engineers after getting through the eventual advanced post.

This first post will start from zero, and get readers to the point of having set up an environment, gained an understanding of Python, and tried out a variety of algorithms for different scenarios. We will leverage the existing tutorials, videos, and works of a variety of folks, so the thanks for anything included herein should be directed solely at them. Instead of having a high number of resources for each topic step say, clusteringI have tried to select a quality tutorial or two, along with an accessible video preliminarily describing the underlying theory, math, or intuition of the given topic.

Fear not if the steps seem mostly aimed at machine learning algorithms, as along the way you will also come across additional important concepts, such as data preprocessing, loss metrics, data visualization, and much more. So grab a cup of your favorite beverage and settle in for the first of three in the series, and start mastering basic machine learning with Python in these 7 steps. I looked for some updated materials for this section, beyond those I pointed out in previous iterations, both for the sake of change and for the sake of keeping up with recent versions of Python.

You will need Python 3. As you will be needing a number of Python's more popular scientific libraries as we progress, I recommend using the Anaconda distribution, which you can download here choose whichever Python 3.

X version is the latest, not Python 2. Xinstead of installing components separately. Just launch the installer, and when it's done you will have Python, Jupyter notebook, and everything else you will need moving forward. Before going too much further, it's a good idea to understand what the scientific computing stack is, what its most prominent and important components are, and how they will be used in a machine learning environment.

This article from Dataquest, aptly titled Jupyter Notebook for Beginners: A Tutorialdives into why we are using Jupyter notebooks at all, and introduces some of the most important Python libraries you will encounter along this path, namely Pandas, Numpy, and Matplotlib.


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