Showing posts with label datascience. Show all posts
Showing posts with label datascience. Show all posts

Sunday, November 17, 2019

What is Machine Learning: Understanding Machine Learning Basics

ai , Artificial Intelligence
What is Machine Learning: Understanding Machine Learning Basics

What was considered to be fiction not so long ago, now is a reality. The technology that only could be seen in movies and read in books is currently a reality we live in. While some of the greatest minds only could’ve dreamed in the past about what is machine learning and what it could bring to humanity, the phenomenon is very much alive.
Machine learning, or shortened as ML, is a computer science term standing for machine intelligence. It is a technology that can learn and mimic cognitive functions such as neurons. It can solve problems on its own and not just answer questions like a virtual assistant.
With the rise of machines’ capability to improve people’s lives, we can already notice machine learning software in some parts like face recognition, self-driving cars, social networking, and auto-pilots in planes. As the Tesler’s Theorem says “ML is whatever hasn’t been done yet”. Machine intelligence capabilities that are classified as ML can successfully understand human speech, military simulations, competing at the highest level of computer games, and more. Now that we have tasted a bit of what is machine learning, let’s dive deeper, shall we?

    A deeper insight into Machine Learning TechnologyWhat is machine learning - A robot

    From the virtual assistants like Siri and Alexa, machine learning software is rapidly integrating into our daily lives. Although some of these examples could not be considered as the “true” machine intelligence that can make decisions on its own, the spin-off projects’ impact continues to advance in capability and prevalence.
    To have a better understanding of what is ML, it is needed to go back a little over its development.

    A short history of ML

    The very first ideas of artificial beings were mentioned in antiques and have been in the fiction scene for a very long time. Stories like Frankenstein were the results of it. The field of artificial intelligence studies was born in 1956, at Dartmouth College in the United States. A group of scientists from universities like MIT and CMU became the founders of ML technology research. The programs they created have been considered as the first machine learning basics. They were the ones to create a computer system that could learn checkers’ strategies, solve problems in algebra, and prove logical theorems. They believed that in 20 years or so the machines will be capable to do anything that a man can do.
    Although they were very optimistic about the progress of their creation, they failed to realize what is machine learning development going to challenge them next. Because of the hard financial times, both the United States and British governments decided to stop funding the projects on the exploratory research of ML. The period, in which it was very hard to find enough funds to continue the research was called “ML winter”.
    Nonetheless, the “ML winter” did not last too long. By 1985, the research was alive again and by that time, the market of machine learning reached over a billion dollars. Through stumbles and falls, by the end of 20th, and by the beginning of the 21st century, machine intelligence development has been used in medical diagnosis, logistics, data mining, etc. Machine learning software started gaining success because of increasing computational power. As Moore’s law states, the speed, and capability of computers can be expected to double every two years. That means that the evolution of computer science progresses unquestionably fast and it will continue to increase the quality of people’s work accordingly.

    The basic concept of ML

    Machine learning as a process and as a product is very hard to understand if it’s not in your expertise. To make it as simple as possible, ML technology is a software, that takes input information and turns it into other information, that is output.
    The biggest difference between machine intelligence and other kinds of software programs is that to machine intelligence, the creator, that is a programmer, did not have to give instructions on every feature that it is doing. Through examples and practices, it learns the needed information by itself.

    What is machine learning - BB-8 droidWhy is machine learning important?

    Understanding what is machine learning and its’ importance has to begin with the very simple statement – it was created to reduce human effort and help in the areas where it is dangerous for a person to step in. Although there are many different ways of using machines’ intelligence, it works as a speed-up to some sort of a process and gives the user an accurate result. The idea of ML software is to create an error-free world. Let’s break down some of it’s main and most important features:
    • Machine-learning learns through repetitive learning and discovery through data. Instead of handling the information by yourself, the ML makes robotic automation that can perform high-volume, computerized tasks without experiencing any form of tiredness and tardiness. It is worth to mention that this process still needs a human inquiry since the ML system needs to have the right questions.
    • It will get the most out of the data. As mentioned above, with the right set up from an expert, ML technology can work without fatigue for a very long time. What is machine learning amazing for creating a competitive advantage against business competitors. Data collecting has grown significantly over the last years, and the importance of it has become huge. It’s no surprise that there have been many scandals and data protection regulations over this time. Everyone knows, that the data can play a big role in many work areas, and ML can make it easier to sort through it.
    • Machine learning software plays a huge role in safety. By giving the ML access to data storage, it can work as a fraud detection system a lot faster with the help of deep learning.
    • Using machine learning basics to improve current products. If you are familiar with digital marketing then you know that the internet of things is coming whether we like it or not. Web 3.0, the alternative name to the internet of things (IoT). The definition of IoT means that it extends the purpose of casual and everyday devices that we use. In the consumer market, the internet of things is the synonym of the things that make a “smart home”. It covers devices, appliances, security cameras, thermostats, etc.
    • Deep neural networks help us achieve extreme precision. What is machine learning also astonishing about is that through deep learning, image classification, and object recognition machine intelligence can spot cancer on MRIs just as precise as an expert radiologist.
    As we can see machine learning impact is undeniable in the current stage of computer science and technologies. Don’t get it wrong, it’s not all advantages of ML technology, there are much more than that. But now that we mentioned deep learning and neural networks, what exactly are they?

    Neural Networks

    Theoretically, the neural network is a circuit or a network of neurons. In this case, it is an artificial neural network that helps machine learning to solve a problem. A neural network is a set of certain algorithms that have been modelled to be similar to the human brain. These algorithms are designed to recognize patterns of information. The information is recognized through a machine perception, labelling or clustering raw input. Just like it would be real-life images, sounds or texts, artificial neural networks understand it through n-dimensional tensors(arrays) that hold the values and numbers. It is one of the most important things about what is machine learning all about.
    Neural networks help to cluster and classify data. The whole process helps to group unlabeled data according to similarities among the example inputs, and neural networks classify data when have a labelled dataset to train on. This type of learning is called supervised. On the other hand, there is unsupervised learning, that helps to find previously unknown patterns in data set without pre-existing labels.

    Deep learning

    Another essential part of machine intelligence is deep learning. This process is a machine learning technique that helps them to learn from examples, just like humans do. If you have seen self-driving cars then you probably had your first contact with machine learning.
    In deep learning, machine intelligence can learn to perform tasks from images, texts, sounds, like a human from books, videos or lectures. Human beings always have a chance to make a mistake, while computers with deep learning models can achieve picture-perfect accuracy and exceed human performance. Deep learning models are part of neural networks since they use the labelled data and datasets that have been collected. It is a huge part of what is machine learning.

    Real-life example: Sofia the RobotWhat is machine learning - Real life example Sofia

    Although the name itself suggests that it is a robot, do not get tricked. The robot is what is on the outside – the skeleton of the whole project. What is most impressive about Sofia – it is her mind.
    Sofia is a social humanoid robot that has been developed by a company Hanson Robotics. She was activated on February 14th in 2016.
    Combined with many algorithms, Sofia the Robot can see, follow movements, sustain eye contact with its companion, and recognize people. It can even understand facial, expressions of people, and understand companions’ emotions. This whole process is done through the cameras that are in her eyes. In 2018 she was upgraded and since then, Sofia the Robot can walk.
    The creator of Sofia, David Hanson, said that the goal was to create a machine learning-driven robot that could serve in healthcare, customer service, therapy or education. Sofia’s machine intelligence is constantly being trained in the lab, so she is developing new skills and making fewer errors as we speak.
    Moreover, what is machine learning of Sofia so groundbreaking is that it combines cutting-edge neural networks, expert systems, machine perception, conversational natural language processing, adaptive motor control, and cognitive architecture.
    Sofia the robot can function in separate ways – the first is a completely ML autonomous operation, the second is ML operation mixed with human-generated words. It is a fully functioning hybrid human-ML intelligence.

    Overview

    It is hard to deny that machine learning is currently the biggest cutting-edge technology out there. It is important to acknowledge that if we want to grow and continue making human lives better it is one of the best ways to do so. If you want to understand better on what is machine learning and learn more about it, head over to our BitDegree course and give it a try. If you’re interested in the absolute machine learning basics, then head over to this course.
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    What is Deep Learning: Neural Network Software Basics

    datascience , software
    What is Deep Learning: Neural Network Software Basics


    The evolution of technology has taken humanity to heights like never before. Work areas of medicine, safety, learning, and providing other kinds of help has reached a peak. But it does not stop there. Artificial intelligence is the next big thing in the world of technology and computer science but to understand it, it’s important to know what it consists of. It is essential to know what is deep learning and what artificial neural network means.
    The AI technology field is extremely advanced and interesting. These two tools that are being used in artificial intelligence are very powerful in terms of solving complex problems and to develop even higher standards in science.
    It is safe to say that this kind of mechanism is a transition to the next level of technology. The companies of today have already recognized its importance and started using it in most of their cases. Let’s take Google for example. Google uses search engine AI to learn from its’ users. If you are looking for something in its search bar, for example, a “laptop computer”, and after getting the results you press on it, you just taught Googles’ AI that a “laptop computer” is what you pressed on. Wonder how does it work? Let’s dive deeper and find out.

    standing Deep Learning AIwhat is deep learning - example of a robot

    What is Deep Learning technology so special about, that it is a technique for computers (AI) to learn just like humans do – by trial and error. If you are wondering if you have ever seen it before, you probably have. It is the technology behind such applications as voice control over devices like phones, tablets or television. Not so long ago we have been introduced to the driverless cars, which is also a product of deep learning. With the help of DL, artificial intelligence recognizes stop signs, pedestrians, and other obstacles in the road that might cause a disaster.
    To perform such actions, a computer that is using deep learning techniques requests a large amount of training data (this is the work of neural networks, we will get to that a bit later). Such technological achievements like driverless cars need thousands of video footage and images to recognize every single situation for it to be safe. The recent improvements in Deep Learning have been taken to the level where it outperforms humans in a certain amount of tasks.

    How Does It Work?

    As already mentioned slightly above, what is deep learning using to perform such tasks are neural networks. Most of the times deep learning AI is referred to as a deep neural network. The word deep in this term stands for the layers that are hidden in the neural network.
    Deep learning models are trained by getting a sufficient amount of data and neural network data architectures that learn features directly from the data without manual labour. Neural networks are systems that are connected just like our biological neural networks. These kinds of systems are created in a way to adapt to situational needs. Once the neural nets identify the results for a certain object, the next time the NN systems can identify whether it is the same object or not. The neural networks do not recognize objects the same way we do, it recognizes objects through their own unique set of features.

    Artificial Neural Networks

    One of the most common and popular types of what is deep learning using is known as conventional neural networks or CNN for short. It combines the learned features with input data, and uses 2D convolutional layers, making this architecture well suited to process 2D data. For example, it can be images or coordinate plane sheets.
    Conventional neural networks work in a way that there is no longer a need for manual feature extraction. It extracts features directly from images. Artificial neural networks have an automated feature extraction that makes deep learning models picture-perfect accurate for computer vision tasks such as object classification.
    CNN’s learn to detect different features using numbers of hidden layers. Every number of the hidden layer increases the complexity of the learned image features. CNN’s learn different features from every layer.

    The Common Exampleswhat is deep learning - a robot learning to play piano

    According to sources, there are three most used ways to use deep learning to perform object classification:
    • Transfer learning. The learning approach is mostly used in deep learning applications. It is done by having an existing network and adding new data to previously unknown classes. This way it is a lot better to save some time because instead of you reduce the amount of image processing. It allows categorizing only certain objects rather than going through all different objects until it finds the correct one.
    • Training from nothing. This is mostly used for new applications that are going to have a large count of output categories. It begins by gathering a large number of labelled data sets and designing a network architecture that will learn the features. While transfer learning can take up to hours or minutes, this method takes a bit longer – from days to weeks to train.
    • Feature extraction. Not as popular as the mentioned methods before, but still used commonly. This is a method that is used for a more specialized approach to deep learning. It uses the network as a feature extractor. Since the layers in conventional neural networks are tasked with learning certain features from images, it is also possible to withdraw these features and make it as an input to a machine learning model.

    What Are Other Types of Neural Networks?

    While the conventional neural network could be considered as the standard neural net that has been expanded across space using shared weights, there are also some different types.
    A recurrent neural network, rather than the conventional one, is extended across time by having edges that feed into the next time step instead of the next layer in the same time step. This artificial neural network is used to recognize sequences, for example, a speech signal or a text.
    Also, there is a recursive neural network. This NN system has no time aspect to the input sequence, but the input has to be processed hierarchically.

    Neural Networks in Action

    It might get tricky when trying to understand what are the real benefits of the neural networks in real-life situations. Artificial neural networks are very popular among stock market experts. With the help of NN systems, it is possible to apply “algorithmic trading”, that can be applied to the likes of financial markets, stocks, interest rates, and various currencies. Neural network algorithms can find undervalued stocks, improve existing stock models, and use deep learning to find ways how to optimize the algorithm as the market changes.
    Since neural networks are very flexible, they can be applied in various complex pattern recognitions and predict problems. As an alternative to the example above, the NN system can be used to forecast business, detect cancer from images, and recognize faces on social media images.

    Deep Learning in Actionwhat is deep learning - data connections

    Not only neural networks have real-life examples. Deep Learning can also be described as some of the following creations:
    • Virtual assistants.
    • Chatbots or service bots.
    • Personalized shopping and entertainment.
    • Imagine colourization (uses algorithms to recreate true colours on images that are black-and-white)

    What Are The Key Differences Between DL and NN?

    With all this information it is clear that Deep Learning and Neural Networks are strongly connected and probably wouldn’t work well when separated. To be able to understand what is deep learning and what is neural networks it is essential to know the main takeaway.
    Neural networks transmit data in the form of input values and output values. It is used to transfer data by using connections. Whereas Deep Learning is related to the transformation and extraction of feature which attempts to establish a relationship between stimulus and associated neural responses present in the brain. In other words, Neural Networks are used for natural resource management, process control, vehicle control, decision making, while Deep Learning is used for automatic speech recognition, image recognition, etc.

    Overview

    To sum up, Deep Learning and Neural Network complete each other and will develop into even bigger technological wonder than it is today. Head over to our courses page and take a course on Machine Learning applications. Artificial intelligence is the next step in our age, and the more experience it gets, the more benefits it will provide to society.
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    What is IOT: Understanding What is the Internet of Things

    datascience , IoT
    What is IOT: Understanding What is the Internet of Things

    Ever since the beginning of the internet visionary scientists imagined adding some sort of intelligence to basic objects to make every person’s life a little bit easier. The idea began in the early 1980s but it was slow to realize due to lack of technological advances.
    To gain an understanding of what is IoT we must go back to the past century. In 1999 at MIT, the internet of things term was first mentioned as an idea to bring attention to Procter & Gamble’s (P&G) senior management. In addition to that, a book called “When Things Start to Think” appeared in the same years and drawn the way there the process of IoT should be heading.
    IoT applications have evolved from things like wireless technologies, microservices, microelectromechanical systems and, of course, the internet itself. The convergence of these technologies has helped to break down the operational and information technologies, that enabled devices to receive data and make insights accordingly.

      What is IoT?Amazong Alexa as an example of what is IoT

      From aeroplanes to self-driving cars and many other smaller devices, the internet of things is a reference to numbers of devices around the world that are connected to the internet. Collecting and sharing data, it enables the devices to work on its’ own and adds a “digital intelligence” to them. The electronic devices can communicate in real-time without any person involved and send generated messages.
      To paint a better view of what is IoT, it helps to imagine that it is an ecosystem of devices, machines, appliances or any other things that can be connected to the internet through the wired or wireless network. The established network between devices allows data integration and exchange between the computer system and physical devices. This new chapter of technological advances is meant to be the step towards even better efficiency and productivity of daily people’s life.
      Using the latest cutting-edge technologies like machine learning, artificial intelligence, and machine-to-machine communication, the internet of things extends the capabilities of typical physical devices like smartphones, tablets, desktop computers, and laptops. By expanding the connectivity of the devices that mostly are non-connected to the network, the IoT system bridges everyday devices like washing machines, heating systems, door or garage lock so you can monitor, control and provide other actions on a mobile phone or a tablet.

      How Does IoT Work?

      Just like any other system or mechanism works, the internet of things also has the main steps that are required for it to work. Trying to understand what is IoT and how it works may get confusing, so it is important to take it piece by piece. An IoT system is made of four main components that work together and create the desired output of the scheme. The components are:

      Sensors

      One of the main parts of the IoT engine, since it collects and specifies the data from the surroundings. The data that it collects can be considered as a very simple one – it may be a particular timing, geographical location, supply stock, or even such complex thing as the health state of the patient in a hospital. To notice even the smallest changes in surrounding data, the device can have a bundle of sensors that can be capable of more than data collection. The best example of that could be a mobile phone, that provides a lot of other functions while perfectly managing data.

      Smart thermostat as an example of what is IoTConnectivity

      After collecting the surrounding data IoT devices need to process it somewhere and this is when connectivity plays the main role. Collected data is sent to the IoT platform with the help of a medium. Wi-Fi, Ethernet, Bluetooth, Cellular Network and other network connections are crucial in transferring data to the cloud. Connectivity is what mostly defines what is IoT. Choosing the best connectivity method will decide the tradeoff of medium and power consumption, range of connectivity and bandwidth.

      Data Processing

      As mentioned slightly above, connectivity enables the transfer of data into a cloud where it is stored, analyzed, and processed. The data is processed using a “Big Data Analytics Engine” that helps the system to make better decisions according to the data. Data processing based decisions allow IoT applications to make a wide range of actions. For starters, it can be as simple as turning on the lights when the homeowner comes back at particular hours, or, with the help of surveillance systems, identifies danger or intruders.

      User Interface (UI)

      The final step of the internet of things process is to notify the main user. It can be done through numerous actions, as an alert, reminder, text message, notification or email. These actions can depend on the functionality of the system itself. An advanced IoT system can arguably control the whole home environment and even more. The user interface allows the user to perform a wide variety of actions, for example, adjusting lighting, temperature, air conditioning in the environment, etc.
      One of the best ways to describe what is IoT or how it works is through daily tasks that will be changed after implementing the system.
      • The refrigerator will notify the user when it is out-of-stock of particular groceries.
      • Kitchen appliances will start working in hours when you wake up or get ready for making dinner.
      • Lights turn off or on when it’s time to sleep or wake up.
      • The vehicle sends a notification on gas shortage or delays in the road.
      As said before, the goal is to make the day easier, more efficient, productive, and overall better for the consumer. The devices will overtake most of the chores without human intervention.

      IoT in Labor MarketApple computer saying "Do More"

      It is safe to say that the internet of things can bring many benefits not only to the regular life of casual citizens but also for many business organizations and authority institutions. Enterprises have a lot more access, devices, and appliances, therefore it needs even better and accurate overwatch of devices.
      Manufacturers realized what is IoT and what are the benefits of its capabilities so they started adding sensors to collect data on their devices. By adding sensors, companies can transmit data back and forth, and see how their products are performing. IoT applications and devices can be implemented in various industries, can offer industry-specific sensors or real-time location devices. By the year 2020, it is predicted that across the industries there will be the amount of 4.4 billion IoT units and it will hit $3 trillion revenue. Let’s take a look at the industries that already started applying cutting-edge technology.
      • Medicine and healthcare. The newest sensors enabled doctors to get real-time access to the patient’s health status, collect and store data about it in the cloud. The IoT technology in medicine has significantly decreased the time of typical procedures, simplified the processes while reducing and mitigating disease risks, and improved the use and availability of hardware.
      • Sharing Economy. With the combination of the internet of things and blockchain, the sharing economy has been reshaped into a “Blockchain of Things”. The combo of both technologies has created possibilities for many marketplaces to collect and share data due to the concept of Smart Contracts.
      • Education. What is IoT in education doing, is that with the share of access to data, many people across the world can approach information and formal or informal education.
      • Retail. Automating as much as possible processes of delivery systems is the key to quick and safe transmissions. Automation makes the process effortless.
      • Travel. The migration of the people through the world is one of the most frustrating procedures that require a lot of security attention and information processing. With the implementation of IoT in travelling, agencies are now able to deliver real-time information and automate most of the processes that make travelling a smooth and pleasing experience.

      Career Possibilities in IoT Field

      There is no question that the growing field of the internet of things will need experts that are willing to improve it. Named as the next “Industrial Revolution”, the IoT field can be a great place to start a new career or shift towards it. What are the demanded skills in such an industry?
      • Information Technology Infrastructure Library (ITIL). Considered to be one of the vital elements in what is IoT, ITIL is a great addition of stability, networking, and security in the technology.
      • The Open Group Architecture Framework (TOGAF). Compared to what HTML did to HTTP for web development, TOGAF is the element that will enable connecting everything instantly. Having a TOGAF certification can be one of the best ways to get into the internet of things field.
      • Big Data. As mostly everything on the digital space revolves around data, the IoT will generate even more data to store and process, therefore there will be, or is right now, a huge need of Big Data specialists to handle the amounts of data created by IoT.
      • Blockchain. Although the blockchain is not considered as a part of IoT, the secure nature of it can become crucial in the upcoming years.


      Amazon Go example of what is IoT
      Source: Amazon.com

      An Example of Amazon Go

      If you are still not sure what is IoT or whether it is a relevant thing in your surroundings, there are many daily examples of it where it is used. We can begin by one of the most known examples that people use in their households – Amazon Go. Being one of its kind, Amazon Go is an app that allows users to shop with no checkout required. By having a certain application on your phone, you walk into the store, collect the groceries you need and leave. No lines, no checkouts. The IoT system behind it collects data using deep learning algorithms, computer vision, and sensor fusion. Just like self-driving cars, Amazon Go is using the same technology.

      Overview – What are the Predictions?

      The internet of things technology did not come out of anywhere, it is the evolution of the internet throughout the years that lead to it. Otherwise known as WEB 3.0, the process has still a long way to go. It is predicted that in the upcoming years the definition of what is IoT will become even smarter. Many companies and authority institutions will presumably start using this technology to save money and time, therefore increasing the efficiency of people’s work.
      Of course, with the great benefits that the internet of things will bring to our society, the growing cybersecurity danger will also increase. While managing information remotely and automatically, hackers will look for ways to get into the system and disrupt the security and privacy of users with cyber attacks. Many regulations are still yet to come and there is still a lot of work to improve the current technology, but their future of it looks brighter every day. If you want to learn the main skills that are needed in this field, head to BitDegree courses and become an expert in the IoT field.
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