If you'd go by the marketing newsletters of leading IT solutions vendors of the world, it would appear that artificial intelligence and machine learning are ideas that have come into being, almost magically, in the past two to three years. Artificial intelligence, in fact, is a term that was coined way back in the 1950s by computer programmers and researchers to describe machines that could respond with appropriate behaviors to abstract problems without human input. Machine learning is one of the more prominent approaches to making artificial intelligence a reality. It is centered on the idea of creating algorithms that are inherently capable of identifying patterns in data and improving their outcomes based on the large datasets. This guide is dedicated to helping you understand and identify the fundamental skills you need to master machine learning technologies and find fulfilling employment in this hot and growing field.
Automatic speech transcription, Self-driving cars, a computer program beating the world champion GO player and computers learning to play video games and achieving better results than humans. Astonishing results that makes you wonder what Artificial Intelligence (AI) can achieve now and in the future. Futurist Ray Kurzweil predicts that by 2029 computers will have human level intelligence and by 2045 computers will be smarter than humans, the so called "Singularity". Some of us are looking forward to that, others think of it as their worst nightmare. In 2015 several top scientists and entrepreneurs called for caution over AI as it could be used to create something that cannot be controlled.
I must not have got the memo, because as a young lecturer in computer science at the University of Southampton in 1985 I was unaware that "women didn't do computing". Southampton had always recruited a healthy number of women to study computing in our fledgling department, and a quarter of the staff were women, but the student lists for the new academic year showed that quite suddenly, or so it appeared, we'd achieved the unenviable record of having no female students in that year's intake. Many women made important contributions to computing in its early decades, figures such as Karen Spärck Jones in Britain or Grace Hopper in the US, among many others who worked in the vital field of cryptography during the Second World War or, later, on the enormous challenges of the space race. But it had become clear that by the mid-1980s something fundamental had changed. We found that UK university admission figures revealed that the number of girls studying computing had fallen dramatically compared to the number of boys: from 25% percent in 1978 to just 10% in 1985.
Andrej Karpathy has an article "Software 2.0" that makes the argument that Neural Networks (or Deep Learning) is a new kind of software. I do agree that there indeed a trend towards "teachable machines" as opposed to the more conventional programmable machines, however I do have an issue with some of the benefits that Karpathy mentions to back-up his thesis. Certainly Deep Learning is already eating the Machine Learning world with advances across the board. Karpathy mentions several well known ones: visual recognition, speech recognition, speech synthesis, machine translation, robotics and games. This frames his argument about the sea change in computing and perhaps its time to think about a new kind of software (I guess the kind that you teach like a dog instead of programming).
Specifically, it was engineered to exploit every bit of memory and hardware resources for tree boosting algorithms. The implementation of XGBoost offers several advanced features for model tuning, computing environments and algorithm enhancement. It is capable of performing the three main forms of gradient boosting (Gradient Boosting (GB), Stochastic GB and Regularized GB) and it is robust enough to support fine tuning and addition of regularization parameters. XGBoost specifically, implements this algorithm for decision tree boosting with an additional custom regularization term in the objective function.
"Classical machine learning methods such as deep neural networks frequently have the feature that they can both recognize statistical patterns in data and produce data that possess the same statistical patterns: they recognize the patterns that they produce," they write. If small quantum information processors can produce statistical patterns that are computationally difficult for a classical computer to produce, then perhaps they can also recognize patterns that are equally difficult to recognize classically." At present, the authors say very little is known about how many gates--or operations--a quantum machine learning algorithm will require to solve a given problem when operated on real-world devices. The authors say this is probably the most promising near-term application for quantum machine learning and has the added benefit that any insights can be fed back into the design of better hardware.
Essentially, the idea is that, given a good set of starting rules and opportunities to interact with data and situations, computers can program themselves, or improve upon basic programs provided for them. Because some companies build or use technologies that employ machine learning and AI, there has been considerable demand for skilled and knowledgeable researchers and developers. In fact, it's hard to find a reputable graduate computer science program that doesn't include machine learning amidst its targeted subject matters. MOOCs can encompass actual degree programs at reputable universities, certificate programs that provide ample training but don't confer a full-fledged degree, or mapped-out curricula in machine learning or AI that cover the ground in as much depth as one might wish to learn the subject matter.
You will explore the main features and capabilities of TensorFlow such as a computation graph, data model, programming model, and TensorBoard. He is also the author of the book Building Machine Learning Projects with TensorFlow, Packt Publishing. Rezaul Karim has more than 8 years of experience in the area of research and development with a solid knowledge of algorithms and data structures in C/C, Java, Scala, R, and Python, focusing on Big Data technologies such as Spark, Kafka, DC/OS, Docker, Mesos, Zeppelin, Hadoop, and MapReduce, and deep learning technologies such as TensorFlow, DeepLearning4j, and H2O-Sparking Water. His research interests include machine learning, deep learning, semantic web/linked data, Big Data, and bioinformatics.
This course is for the absolute beginner to Artificial Intelligence (AI), Machine Learning, Deep Learning, and Data Science. If you are feeling overwhelmed by either the tsunami of data that you are tasked with trying to make sense out of, or overwhelmed by the tsunami of media coverage around Artificial Intelligence, Deep Learning, Data Science, and Machine Learning, I am here to share a competitive advantage. From there, we will work through signing up for a free Salesforce Einstein account (that you can keep for life) that has the new Salesforce Einstein AI engine enabled. There is no coding required - you can build AI enabled apps with clicks instead of code, thanks to being able to leverage the Salesforce Einstein AI framework.
Within machine learning, there are different types of learning (e.g., supervised, unsupervised) and various techniques (e.g., regression, neural nets). One big one is that when a neural net determines certain features are important and makes decisions based on them, we do not know why. Therefore companies leveraging machine learning should focus on providing insights that are actionable, and move from helping customers manipulate data for analysis to focusing on strategy and recommendations to make decision making more efficient and accurate. Facebook's photo tagging engine has moved to recommended tagging (left), making it smarter and simpler to use than its previous version (right).