Fundamentals Of Machine Learning And Deep Learn...
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Machine learning and artificial intelligence hold the potential to transform healthcare and open up a world of incredible promise. But we will never realize the potential of these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles.
Businesses worldwide are using artificial intelligence to solve their greatest challenges. Healthcare professionals use AI to enable more accurate, faster diagnoses in patients. Retail businesses use it to offer personalized customer shopping experiences. Automakers use it to make personal vehicles, shared mobility, and delivery services safer and more efficient. Deep learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation. Using deep learning, computers can learn and recognize patterns from data that are considered too complex or subtle for expert-written software.
Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.
Similarly, instead of codifying knowledge into computers, machine learning (ML) seeks to automatically learn meaningful relationships and patterns from examples and observations (Bishop 2006). Advances in ML have enabled the recent rise of intelligent systems with human-like cognitive capacity that penetrate our business and personal life and shape the networked interactions on electronic markets in every conceivable way, with companies augmenting decision-making for productivity, engagement, and employee retention (Shrestha et al. 2021), trainable assistant systems adapting to individual user preferences (Fischer et al. 2020), and trading agents shaking traditional finance trading markets (Jayanth Balaji et al. 2018).
During the last decades, the field of ML has brought forth a variety of remarkable advancements in sophisticated learning algorithms and efficient pre-processing techniques. One of these advancements was the evolution of artificial neural networks (ANNs) towards increasingly deep neural network architectures with improved learning capabilities summarized as deep learning (DL) (Goodfellow et al. 2016; LeCun et al. 2015). For specific applications in closed environments, DL already shows superhuman performance by excelling human capabilities (Madani et al. 2018; Silver et al. 2018). However, such benefits also come at a price as there are several challenges to overcome for successfully implementing analytical models in real business settings. These include the suitable choice from manifold implementation options, bias and drift in data, the mitigation of black-box properties, and the reuse of preconfigured models (as a service).
To provide a fundamental understanding of the field, it is necessary to distinguish several relevant terms and concepts from each other. For this purpose, we first present basic foundations of AI, before we distinguish i) machine learning algorithms, ii) artificial neural networks, and iii) deep neural networks. The hierarchical relationship between those terms is summarized in Venn diagram of Fig. 1.
During automated model building, the input is used by a learning algorithm to identify patterns and relationships that are relevant for the respective learning task. As described above, shallow ML requires well-designed features for this task. On this basis, each family of learning algorithms applies different mechanisms for analytical model building. For example, when building a classification model, decision tree algorithms exploit the features space by incrementally splitting data records into increasingly homogenous partitions following a hierarchical, tree-like structure. A support vector machine (SVM) seeks to construct a discriminatory hyperplane between data points of different classes where the input data is often projected into a higher-dimensional feature space for better separability. These examples demonstrate that there are different ways of analytical model building, each of them with individual advantages and disadvantages depending on the input data and the derived features (Kotsiantis et al. 2006).
In the context of transfer learning, new markets and ecosystems of AI as a service (AIaaS) are already emerging. Such marketplaces, for example by Microsoft or Amazon Web Services, offer cloud AI applications, AI platforms, and AI infrastructure. In addition to cloud-based benefits for deployments, they also enable transfer learning from already established models to other applications. That is, they allow customers with limited AI development resources to purchase pre-trained models and integrate them into their own business environments (e.g., NLP models for chatbot applications). New types of vendors can participate in such markets, for example, by offering transfer learning results for highly domain-specific tasks, such as predictive maintenance for complex machines. As outlined above, consumers of servitized DL models in particular need to be aware of the risks their black-box nature poses and establish similarly strict protocols as with human operators for similar decisions. As the market of AIaaS is only emerging, guidelines for responsible transfer learning have yet to be established (e.g., Amorós et al. 2020).
Learn the basics of ML with this collection of books and online courses. You will be introduced to ML and guided through deep learning using TensorFlow 2.0. Then you will have the opportunity to practice what you learn with beginner tutorials.
Once you understand the basics of machine learning, take your abilities to the next level by diving into theoretical understanding of neural networks, deep learning, and improving your knowledge of the underlying math concepts.
Learn the basics of developing machine learning models in JavaScript, and how to deploy directly in the browser. You will get a high-level introduction on deep learning and on how to get started with TensorFlow.js through hands-on exercises.
Reading is one of the best ways to understand the foundations of ML and deep learning. Books can give you the theoretical understanding necessary to help you learn new concepts more quickly in the future.
A hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience. Once you finish this book, you'll know how to build and deploy production-ready deep learning systems with TensorFlow.js.
Taking a multi-part online course is a good way to learn the basic concepts of ML. Many courses provide great visual explainers, and the tools needed to start applying machine learning directly at work, or with your personal projects.
The Machine Learning Crash Course with TensorFlow APIs is a self-study guide for aspiring machine learning practitioners. It features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.
In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects and build a career in AI. You will master not only the theory, but also see how it is applied in industry.
This online specialization from Coursera aims to bridge the gap of mathematics and machine learning, getting you up to speed in the underlying mathematics to build an intuitive understanding, and relating it to Machine Learning and Data Science.
This book provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex world of datasets needed to train models in machine learning.
A 3-part series that explores both training and executing machine learned models with TensorFlow.js, and shows you how to create a machine learning model in JavaScript that executes directly in the browser.
Part of a larger series on machine learning and building neural networks, this video playlist focuses on TensorFlow.js, the core API, and how to use the JavaScript library to train and deploy ML models.
Learn how to deploy deep learning models on mobile and embedded devices with TensorFlow Lite in this course, developed by the TensorFlow team and Udacity as a practical approach to model deployment for software developers.
This book walks you through the steps of automating an ML pipeline using the TensorFlow ecosystem. The machine learning examples in this book are based on TensorFlow and Keras, but the core concepts can be applied to any framework.
Professionals and students interested in learning Artificial Intelligence basics should have an understanding of the fundamentals of Python programming. Also, they need to have a basic knowledge of statistics. 59ce067264
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