TinyML And Its ‘Great’ Application in IoT Technology – Analytics India Magazine

Tiny machine learning (TinyML) is an embedded software technology that can be used to build low power consuming devices to run machine learning models. It is also more famously referred to as the missing link between device intelligence and edge hardware. It makes computing at edge cheaper, less expensive, and more stable. Further, TinyML also facilitates improved response time, privacy, and low energy cost.

TinyML is massively growing in popularity with every passing year. As per ABI Research, a global tech market advisory firm, by 2030, about 230 billion devices will be shipped with TinyML chipset.

TinyML has the ability to provide a range of applications, from imagery micro-satellite, wildfire detection, and for identifying crop ailments and animal illness. Another area of application that is drawing great attention is its application in IoT devices.

TinyML brings ultra-low-power systems and machine learning communities together; this paves the way for more exciting on-device machine learning. TinyML is placed at the intersection of embedded machine learning applications, algorithms, hardware, and software. As compared with a desktop CPU, which consumes 100 watts of power, TinyML just required a few milliwatts of battery power. With such a major advantage, TinyML can provide great longevity to always-on ML applications at the edge/endpoint.

Currently, there are 250 billion microcontrollers in the world today. This number is growing by 30 billion annually. The reason for its pervasiveness is that, firstly, it gives small devices the ability to make smart decisions without needing to send the data to the cloud. Further, TinyML models are small enough to fit into almost any environment. Taking the example of an imagery micro-satellite which are required to capture high-resolution images but are restricted by the size and number of photos they can transmit back to Earth. With TinyML, however, the microsatellite only captures an image if there was an object of interest such as a ship or weather pattern.

TinyML has the potential to transform the way one deals with IoT data, where billions of tiny devices are already used to provide greater efficiency in fields of medicine, automation, and manufacturing.

It is very important to make a clear distinction between serving machine learning to IoT and developing machine learning inside the IoT devices. In the former, the machine learning tasks are outsourced to the cloud, while the IoT device waits for the execution of intelligent services, however, in latter, TinyML-as-a-service is employed, and the IoT device is part of the execution of the services. The TinyML represents a connecting point between the IoT devices and the ML.

The hardware requirements for machine learning in larger systems are analogous to TinyML in smaller IoT. As the size of IoT devices hitting the market increase, we could see even higher investment in terms of research in TinyML, exploring concepts such as deep neural networks, model compression, and deep reinforcement learning.

There are a few challenges of integrating TinyML in the IoT devices; some of them are:

Speaking in detail about the applications of TinyML, it can be used in sensors for real-time traffic management and ease of urban mobility; in manufacturing, TinyML can be used to enable real-time decision making to identify equipment failure. The workers can be alerted to perform preventive maintenance based on the equipment conditions; TinyML can also be used in the retail business for monitoring the availability of the resource.

TinyML is gaining its ground but is still in a very nascent stage. It is expected to take over space with inter-sector applications very soon.

I am a journalist with a postgraduate degree in computer network engineering. When not reading or writing, one can find me doodling away to my hearts content.

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TinyML And Its 'Great' Application in IoT Technology - Analytics India Magazine

Machine learning to transform delivery of major rail projects in UK – Global Railway Review

By utilising machine learning, Network Rail can increase prediction accuracy, reduce delays, unlock early risk detection and enable significant cost savings.

Credit: Network Rail

Network Rail has announced that it is working with technology startup nPlan to use machine learning technology across its portfolio of projects, which has the potential to transform the way major rail projects are delivered across Britain.

Through using data from past projects to produce accurate cost and time forecasts, the partnership will deliver efficiencies in the way projects are planned and carried out, and improve service reliability for passengers by reducing the risk of overruns.

In a world-first for such work on this scale, Network Rail tested nPlans risk analysis and assurance solution on two of its largest rail projects on the Great Western Main Line and the Salisbury to Exeter Signalling project representing over 3 billion of capital expenditure.

This exercise showed that, by leveraging past data, cost savings of up to 30 million could have been achieved on the Great Western Main Line project alone. This was primarily achieved by flagging unknown risks to the project team those that are invisible to the human eye due to the size and complexity of the project data and allowing them to mitigate those risks before they occur at a significantly lower cost than if they are missed or ignored.

The machine learning technology works by learning from patterns in historical project performance. Put simply, the algorithm learns by comparing what was planned against what actually happened on a project at an individual activity level. This facilitates transparency and a shared, improved view of risk between project partners.

Following the success of this trial, nPlan and Network Rail will now embark on the next phase of deployment, rolling out the software on 40 projects before scaling up on all Network Rail projects by mid-2021. Using data from over 100,000 programmes, Network Rail will increase prediction accuracy, reduce delays, allow for better budgeting and unlock early risk detection, leading to greater certainty in the outcome of these projects.

Network Rails Programme Director for Affordability, Alastair Forbes, said: By championing innovation and using forward-thinking technologies, we can deliver efficiencies in the way we plan and carry out rail upgrade and maintenance projects. It also has the benefit of reducing the risk of project overruns, which means, in turn, we can improve reliability for passengers.

Dev Amratia, CEO and co-founder of nPlan, said: Network Rail is amongst the largest infrastructure operators in Europe, and adopting technology to forecast and assure projects can lead to better outcomes for all of Britains rail industry, from contractors to passengers. I look forward to significantly delayed construction projects, and the disruption that they cause for passengers, becoming a thing of the past, with our railways becoming safer and more resilient.

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Fintech Harvest to Offer AI and Machine Learning enhanced Financial Wellness Reports – Crowdfund Insider

US-based Fintech firm Harvest is reportedly planning to transform the existing credit scoring systems.

Like many other Fintechs, Harvest will be using artificial intelligence (AI) and machine learning (ML) algorithms to offer clients a dynamic view of their financial profile. This should help people with creating their financial wellness plans. It will also assist them with making informed decisions throughout the lifetime of any loans they decide to take out.

Harvests PRO Index (PariFi Rating & Opportunity Index) takes into consideration many different factors along with a clients credit score so that individuals and businesses are able to make better assessments and decisions related to managing their finances.

The Harvest index uses advanced AI and machine learning algorithms to create a holistic or thorough financial plan for each client based on their credit score, income level, and spending habits. The AI is able to automatically negotiate or settle banking fees and interest charges. The software can also manage and identify recurring payments.

The platform has been designed to provide a central picture of all of a clients financial profile. It provides an easy-to-read summary of their different assets, debts, and expenses in one convenient place. It includes action items so that they can take the necessary steps to improve the financial wellness plan. Customers who begin using the index are able to access special tools that help with automatically obtaining refunds from bank fees and various interest charges. Users are also able to better manage their debt and analyze their day-to-day or monthly expenses.

Harvest also offers the Ability-to-Pay as a Service (A2PaaS),. This option allows users to gain access to real-time transactional data. It also offers an overview of a clients financial status. This feature allows lenders to identify pre-arrears clients a lot faster than using traditional methods and also improves recovery.

Nami Baral, CEO of Harvest, stated:

Consumers are facing an unparalleled time of insecurity around their finances and the future. Were launching the PRO Index now to help them truly understand their baseline. Our hope is that this index and our platform will empower the majority to take control of and secure their hard-earned money.

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GreenFlux, Eneco eMobility and Royal HaskoningDHV implement smart charging based on machine learning – Green Car Congress

Royal HaskoningDHVs office in the city of Amersfoort, the Netherlands, is the first location in the world where electric vehicles are smart charged using machine learning. The charging stations are managed by the charging point operator Eneco eMobility, with smart charging technology provided by the GreenFlux platform.

With the number of electric vehicles ever increasing, so is the pressure to increase the number of charging stations on office premises. This comes at a cost; electric vehicles require a significant amount of power, which can lead to high investments in the electrical installation. With smart charging these costs can be significantly reduced, by ensuring that not all vehicles charge at the same time.

With the innovation, developed by GreenFlux, deployed by Eneco eMobility and applied at Royal HaskoningDHVs Living Lab Charging Plaza in Amersfoort, the Netherlands, smart charging is now taken to the next level, allowing up to three times more charging stations on a site than with regular smart charging.

The novelty in this solution is that machine learning is used to determine or estimate how charge station sites are wired physicallydata that commonly is incomplete and unreliable. At Royal HaskoningDHV, the algorithm determines over time the topology of how all the three-phase electricity cables are connected to each individual charge station.

Using this topology, the algorithm can optimize between single and three phase charging electric vehicles. Though this may seem like a technicality, it allows up to three times as many charging stations to be installed on the same electrical infrastructure.

Now that this part has been tested and proven, there is so much more we can add. We can use the same technology to, for instance, predict a drivers departure time or how much energy they will need. With these kinds of inputs, we can optimize the charging experience even further.

Lennart Verheijen, head of innovation at GreenFlux

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GreenFlux, Eneco eMobility and Royal HaskoningDHV implement smart charging based on machine learning - Green Car Congress

Collaboration Will Offer Data to Train Machine Learning Tools – HealthITAnalytics.com

September 28, 2020 -Researchers at the University of Iowa (UI) have received a $1 million grant from the National Science Foundation (NSF) to develop a machine learning platform to train algorithms with data from around the world.

The phase one grant will enable the UI team to lead a multi-university and industry collaboration and address concerns around patient privacy and data security in clinical AI development.

The researchers noted that although the use of AI is widespread in healthcare, training effective machine learning algorithms require thousands of samples annotated by doctors. This can lead to privacy and security issues, the team stated.

Traditional methods of machine learning require a centralized database where patient data can be directly accessed for training a machine learning model, said Stephen Baek, assistant professor of industrial and systems engineering at UI.

Such methods are impacted by practical issues such as patient privacy, information security, data ownership, and the burden on hospitals which must create and maintain these centralized databases.

The team will develop a decentralized, asynchronous solution called ImagiQ, which relies on an ecosystem of machine learning models so that institutions can select models that work best for their populations. Organizations will be able to upload and share the models, not patient data, with each other.

As each institution improves the model using their local patient data sets, models will be uploaded back to a centralized server. This ensemble learning approach will allow the most reliable and efficient models to come to the forefront, resulting in a better AI system for analyzing images like lung x-rays or CT scans that detect tumors.

The UI-led team includes researchers from Stanford University, the University of Chicago, Harvard University, Yale University, and Seoul National University.

Over the next nine months, the group will aim to develop a prototype of the system as well as participate in the Accelerators innovation curriculum to ensure the solution has societal impact. By the end of phase one, the team will participate in a pitch competition and proposal evaluation and if selected will proceed to phase two, with potential funding up to $5 million for 24 months.

ImagiQ will further federated learning by decentralizing the model updates and eliminating the synchronous update cycle, said Baek. We are going to create a whole ecosystem of machine learning models that will evolve and improve over time. High performing models will be selected by many institutions, while others are phased out, producing more reliable and trustworthy outputs.

The research team is part of the AI-driven data and model sharing track topic under the 2020 cohort NSF Convergence Accelerator program, designed to leverage a convergence approach to transition basic research and discovery into practice. NSF is investing more than $27 million to support the teams in phase one to develop the solution groundwork for AI-Driven Data and Model Sharing.

The Convergent Accelerators AI-Driven Innovation via Data and Model Sharing topic involves 18 teams concentrating on solution development. These research teams will also address a variety of data and model-related challenges and data types to include platform development to enable easy and efficient data matching and sharing.

The quantum technology and AI-driven data and model sharing topics were chosen based on community input and identified federal research and development priorities, said Douglas Maughan, head of the NSF Convergence Accelerator program. This is the program's second cohort and we are excited for these teams to use convergence research and innovation-centric fundamentals to accelerate solutions that have a positive societal impact.

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Odias in Machine Learning global virtual conference to be held today – Times of India

BHUBANESWAR: A group of Odias, who are working and showing interest in the fields of artificial intelligence (AI) and machine learning (ML), will organise a global virtual conference on Sunday to promote the use of AI and ML for the development of Odisha and advancement of Odia language in the digital era.The conference, called Odias in ML Conference, also aims to showcase career and entrepreneurship opportunities in AI and ML for Odias across the world, said Anjan Kumar Panda, convenor of the conference. It will be attended by technologists, researchers, academicians, business executives, entrepreneurs, policymakers, linguists, language activists, media persons and community leaders, all with a commitment to AI and machine learning. The conference will have four sessions, themed AI for Odisha, AI for Odia language, research and career opportunities in AI and entrepreneurship and business opportunities in AI. Odisha government's IT secretary Manoj Mishra, president of Odisha Society of America Kuku Das, Mo School chairperson Susmita Bagchi and other noted persons will attend the conference. The virtual conference which will commence at 5pm on October 4 will be available across different social media platforms like YouTube, Facebook and Twitter.

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Odias in Machine Learning global virtual conference to be held today - Times of India

Machine Learning as a Service (MLaaS) Market Challenges Report 2020: Comprehensive Insights, Future Forecasts To 2028 | Covid 19 Implications And…

Machine Learning as a Service (MLaaS) Market study offers a thorough assessment of the market by highlighting facts on various features including restraints, drivers, threats, and opportunities. The global market report includes a competitive landscape analysis, market trends, and strategic regional growth status.

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This study offers an inclusive numerical analysis of the Machine Learning as a Service (MLaaS) industry, along with the statistics for planning and making strategies to augment market growth. The study also evaluates the gross margin, market size, revenue, price, and market share, growth rate, and cost structure in the market for efficient decision making.

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Companies are facing growing business concerns associated with the coronavirus outbreak, including a growing risk of recession, supply chain disruptions, and a possible drop in consumer spending. However, these circumstances will play out differently across various states and industries. The report will help companies in taking accurate and timely decisions in these hard times.

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Machine Learning as a Service (MLaaS) Market

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The Machine Learning as a Service (MLaaS) Market research study offers a complete assessment of the market and contains projections with a suitable set of assumptions, thoughtful insights, historical data, facts, statistically-supported information, industry-validated market data, and methodology. It offers an analysis and data by categories such as regions, market segments, distribution channels, and product type.

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This research study classifies the global market by brands, players, types, regions, and applications. The market study offers insights on company variables, complex conditions prevailing in the industry including situational factors, and industry features. The market is segmented as By Type (Special Services and Management Services), By Organization Size (SMEs and Large Enterprises), By Application (Marketing & Advertising, Fraud Detection & Risk Analytics, Predictive Maintenance, Augmented Reality, Network Analytics, and Automated Traffic Management), By End User (BFSI, IT & Telecom, Automobile, Healthcare, Defense, Retail, Media & Entertainment, and Communication) The market study provides a detailed analysis by studying individual conditions and circumstances that are facilitating the market growth.

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The Machine Learning as a Service (MLaaS) Market is analyzed and market scope and information is provided by regions (countries). The key regions covered in the Machine Learning as a Service (MLaaS) Market study are North America, Europe, Asia Pacific, Middle East and Africa, South America. It also covers key regions (countries), viz, Canada, U.S., Germany, U.K., France, Italy, China, Russia, Japan, India, South Korea, Australia, Indonesia, Taiwan, Thailand, Philippines, Malaysia, Vietnam, Brazil, Mexico, Turkey, U.A.E, Saudi Arabia, etc.

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The study offers a complete analysis and precise statistics and revenue figures, by the players, for the period of 2016-2028. It also offers a thorough analysis sustained by reliable statistics on revenue (global and regional level) and by participants for the timeframe of 2016-2028. Details included in the report are a major business, company description, company total revenue, and sales, recent developments, and revenue generated in the Machine Learning as a Service (MLaaS) business. The leading companies covered in this report are Microsoft, IBM Corporation, International Business Machine, Amazon Web Services, Google, Bigml, Fico, Hewlett-Packard Enterprise Development, At&T, Fuzzy.ai, Yottamine Analytics, Ersatz Labs, Inc., and Sift Science Inc.

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The study is an all-inclusive research study of the global Machine Learning as a Service (MLaaS) Market taking into account the recent trends, growth factors, developments, competitive landscape, and opportunities. The market researchers and analysts have done a broad analysis of the global Machine Learning as a Service (MLaaS) Market with the help of research methodologies such as Porters Five Forces analysis and PESTLE.

The study will help the market leaders as well as the new entrants in this market with information on the closest approximations of the revenue numbers for the overall market and the sub-segments. This study will help stakeholders understand the competitive landscape and gain more insights to better position their businesses and plan suitable go-to-market strategies.

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We take pride in serving our present and new customers with information and analysis that match and suit their objective. Our research study can be customized to include clinical trial results data, price trend analysis of target brands for understanding the market for additional countries (ask for the list of countries), literature review, product base analysis, and refurbished market.Contact:

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A Machine Learning Tool Supports the Search for New Craters on Mars – Science Times

Planetary scientists and artificial intelligence (AI) researchers have collaborated on a machine learning tool that helped discover new craters on Mars - including small impacts left by a meteor about eight years ago.

Between March 2010 and May 2012, a meteor flew over Mars, burned, and eventually disintegrated into smaller pieces that crashed into the planet's surface. This left unusually small - and relatively easy to miss, at only 4 meters (13 feet) wide - craters. With the help of its AI-driven tool, NASA scientists at the Jet Propulsion Laboratory (JPL) in Pasadena, California are looking forward to reduced lead time and increased findings on the Red Planet's surface.

(Photo: Photo by David McNew/Getty Images)PASADENA, CA - MAY 27: Principal Investigator, HiRise Camera on Mars Reconnaissance Orbiter, Brian Portock talks to reporters in front of an image of a crater taken during the descent of the Phoenix Mars Lander during an update briefing, two days after landing in a northern polar region of Mars, at NASA's Jet Propulsion Laboratory (JPL) on May 27, 2008, in Pasadena, California. The Phoenix Mars Lander is the newest hope in the search for signs of life on Mars. Fewer than half of the Mars missions have made successful landings. At a cost of $420 million, the Phoenix Mars Lander has flown 422 million miles since leaving Earth in August 2007.

Usually, NASA scientists have to manually analyze the images taken by the Mars Reconnaissance Orbiter (MRO) in search of uncommon phenomena in the Red Planet's surface - avalanche, shifting sand dunes, dust devils, and more. Throughout the MRO's 14-year service, it has provided data that allowed the space agency to find more than 1,000 craters. Most of these discoveries begin with the Context Camera installed in the orbiter, taking extremely large yet low-resolution images of the planet's surface, covering hundreds of miles per shot.

RELATED: Elon Musk on Mars Colonization: "Good Chance You'll Die"

Craters are detected through their blast marks, making them visible from the low-res images. However, the craters themselves remain virtually invisible, which leads to the next process. Using the High-Resolution Imaging Experiment (HiRISE). It provides clearer, more detailed pictures of the target. In fact, its vision system can detect even the tracks left behind by the Curiosity rover. Additionally, the research team allows the public to put in their specific request through the HiRISE HiWish website.

This next process, according to a NASA press release, takes around 40 minutes for a researcher to go through a single Context Camera image. To cut the time required, the JPL team created a machine learning tool called the Automated Fresh Impact Crater Classifier. The AI tool is a part of a wider effort among Jet Propulsion Laboratory scientists called COSMIC - for Capturing Onboard Summarization to Monitor Image Change - that aims to continuously improve Mars orbiters.

JPL researchers trained the crater classifier by providing it with a total of 6,830 Context Camera images, including locations that contained impacts already identified and confirmed by HiRISE. The images provided to the machine learning tool also included images with no impacts, to also train the tool to identify what not to look for.

After the training phase, the crater classifier was deployed on Context Camera's repository of more than 100,000 pictures. A process that used to take 40 minutes is now accomplished on an average of 5 seconds, thanks to a set of high-performance computers operating in parallel within JPL's supercomputer cluster.

"It wouldn't be possible to process over 112,000 images in a reasonable amount of time without distributing the work across many computers," explained Gary Doran, a computer scientist at JPL. The team was challenged at first with running 750 copies of the classifier across the entire cluster.

RELATED: Deep Learning Model Outperforms NPC, Player Records in Gran Turismo

However, a human operator still checks the data returned by the AI tool. Kiri Wagstaff, also a JPL computer scientist, explained that AI tools still can't do the "skilled analysis" that a scientist can do.

Check out more news and information on Mars in Science Times.

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A Machine Learning Tool Supports the Search for New Craters on Mars - Science Times

A beginners guide to the math that powers machine learning – The Next Web

How much math knowledge do you need for machine learning and deep learning? Some people say not much. Others say a lot. Both are correct, depending on what you want to achieve.

There are plenty of programming libraries, code snippets, and pretrained models that can get help you integrate machine learning into your applications without having a deep knowledge of the underlying math functions.

But theres no escaping the mathematical foundations ofmachine learning. At some point in your exploration and mastering of artificial intelligence, youll need to come to terms with the lengthy and complicated equations that adorn AI whitepapers and machine learning textbooks.

In this post, I will introduce some of my favorite machine learning math resources. And while I dont expect you to have fun with machine learning math, I will also try my best to give you some guidelines on how to make the journey a bit more pleasant.

Khan Academys online courses are an excellent resource to acquire math skills for machine learning

Many machine learning books tell you that having a working knowledge of linear algebra. I would argue that you need a lot more than that. Extensive experience with linear algebra is a must-havemachine learning algorithms squeeze every last bit out of vector spaces and matrix mathematics.

You also need to know a good bit of statistics and probability, as well as differential and integral calculus, especially if you want to become more involved indeep learning.

There are plenty of good textbooks, online courses, and blogs that explore these topics. But my personal favorite isKhan Academys math courses. Sal Khan has done a great job of putting together a comprehensive collection of videos that explain different math topics. And its free, which makes it even better.

Although each of the videos (which are also available on YouTube) explain a separate topic, going through the courses end-to-end provides a much richer experience.

I recommend thelinear algebracourse in particular. Here, youll find everything you need about vector spaces, linear transformations, matrix transformations, and coordinate systems. The course has not been tailored for machine learning, and many of the examples are about 2D and 3D graphic systems, which are much easier to visualize than the multidimensional spaces of machine learning problems. But they discuss the same concepts youll encounter in machine learning books and whitepapers. In the course are some hidden gems like least square calculations and eigenvectors, which are important topics in machine learning.

The calculus course are a bit more fragmented, but it might be a good feature for readers who already have a strong foundation and just want to brush up their skills. Khan includes precalculus, differential calculus, and integral calculus courses that cover the foundations. Themultivariable calculus coursediscusses some of the topics that are central to deep learning, such as gradient descent and partial derivatives.

There are also several statistics courses in Khan Academys platform, and there are some overlaps between them. They all discuss some of the key concepts you need in data science and machine learning, such as random variables, distributions, confidence intervals, and the difference between continuous and categorical data. I recommend thecollege statistics course, which includes some extra material that is relevant to machine learning, such as the Bayes theorem.

To be clear, Khan Academys courses are not a replacement for the math textbook and classroom. They are not very rich in exercises. But they are very rich in examples, and for someone who just needs to blow the dust off their algebra knowledge, theyre great. Sal talks very slowly, probably to make the videos usable for a wider audience who are not native English speakers. I run the videos on 1.5x speed and have no problem understanding them, so dont let the video lengths taunt you.

Vanilla algebra and calculus are not enough to get comfortable with the mathematics of machine learning. Machine learning concepts such as loss functions, learning rate, activation functions, and dimensionality reduction are not covered in classic math books. There are more specialized resources for that.

My favorite isMathematics for Machine Learning. Written by three AI researchers, the provides you with a strong foundation to explore the workings of different components of machine learning algorithms.

The book is split into two parts. The first part is mathematical foundations, which is basically a revision of key linear algebra and calculus concepts. The authors cover a lot of material in little more than 200 pages, so most of it is skimmed over with one or two examples. If you have a strong foundation, this part will be a pleasant read. If you find it hard to grasp, you can combine the chapters with select videos from Khans YouTube channel. Itll become much easier.

The second part of the book focuses on machine learning mathematics. Youll get into topics such as regression, dimensionality reduction, support vector machines, and more. Theres no discussion ofartificial neural networksand deep learning concepts, but being focused on the basics makes this book a very good introduction to the mathematics of machine learning.

As the authors write on their website: The book is not intended to cover advanced machine learning techniques because there are already plenty of books doing this. Instead, we aim to provide the necessary mathematical skills to read those other books.

For a more advanced take on deep learning, I recommendHands-on Mathematics for Deep Learning. This book also contains an intro on linear algebra, calculus, and probability and statistics. Again, this section is for people who just want to jar their memory. Its not a basic introductory book.

The real value of this book comes in the second section, where you go into the mathematics of multilayer perceptrons,convolutional neural networks(CNN), andrecurrent neural networks(RNN). The book also goes into the logic of other crucial concepts such as regularization (L1 and L2 norm), dropout layers, and more.

These are concepts that youll encounter in most books on machine learning and deep learning. But knowing the mathematical foundations will help you better understand the role hyperparameters play in improving the performance of your machine learning models.

A bonus section dives into advanced deep learning concepts, such as the attention mechanism that has made Transformers so efficient and popular, generative models such as autoencoders andgenerative adversarial networks, and the mathematics oftransfer learning.

Agreeably, mathematics is not the most fun way to start machine learning education, especially if youre self-learning. Fortunately, as I said at the beginning of this article, you dont need to begin your machine learning education by poring over double integrals, partial derivatives, and mathematical equations that span a pages width.

You can start with some of the more practical resources on data science and machine learning. A good introductory book isPrinciples of Data Science, which gives you a good overview of data science and machine learning fundamentals along with hands-on coding examples in Python and light mathematics.Hands-on Machine Learning andPython Machine Learningare two other books that are a little more advanced and also give deeper coverage of the mathematical concepts. UdemysMachine Learning A-Zis an online course that combines coding with visualization in a very intuitive way.

I would recommend starting with one or two of the above-mentioned books and courses. They will give you a working knowledge of the basics of machine learning and deep learning and prepare your mind for the mathematical foundations. Once you know have a solid grasp of different machine learning algorithms, learning the mathematical foundations becomes much more pleasant.

As you master the mathematics of machine learning, you will find it easier to find new ways to optimize your models and tweak them for better performance. Youll also be able to read the latest cutting edge papers that explain the latest findings and techniques in deep learning, and youll be able to integrate them into your applications. In my experience, the mathematics of machine learning is an ongoing educational experience. Always look for new ways to hone your skills.

This article was originally published by Ben Dickson on TechTalks, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech and what we need to look out for. You can read the original article here.

Published October 2, 2020 10:00 UTC

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A beginners guide to the math that powers machine learning - The Next Web

BullGuard launches new anti-malware range with machine learning and multi-layer protection – BetaNews

BullGuard has announced its new 2021 security suite, featuring Dynamic Machine Learning, which continuously monitors all processes on a user's device, enabling real-time detection and blocking of potentially malicious behavior before it can do damage.

The new suite also offers Multi-Layered Protection which uses six layers -- Safe Browsing, Dynamic Machine Learning, Sentry Protection for Zero-Day Malware, an On-Access AV Engine, a Firewall and a Vulnerability Scanner -- to defend the users devices from malware, without the need for user interaction.

These layers work together to create a buffer between the internet and each device BullGuard 2021 is installed on, designed to catch inbound and local malware, any erroneous outbound communication to the internet, phishing scams and more.

BullGuard 2021 also offers improved application performance while reducing system resource usage, including significantly reduced virus definition file sizes. Other enhancements include identity protection, with additional support for international phone numbers and bank accounts, that ensures accurate monitoring of dark web platforms where stolen user data is sold or traded. There's also an improved Game Booster that now includes support for anti-cheat engines and uninterrupted video performance while broadcasting during gameplay.

"Unlike the majority of other cybersecurity solutions, BullGuard's Dynamic Machine Learning protection continually monitors all processes on your device, enabling real-time detection and blocking of potentially malicious behavior, even if malware attempts to cut your internet connection," says Paul Lipman, CEO of BullGuard. "BullGuard 2021 is ideal for consumers who want 'set-it-and-forget-it' cybersecurity that works behind-the-scenes to provide the best endpoint protection against today's known and zero-day threats."

The product line is being offered in three versions:

You can find out more on the BullGuard site.

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BullGuard launches new anti-malware range with machine learning and multi-layer protection - BetaNews