We minimize over convex functions the maximum (over Wasserstein perturbations of the empirical measure) of the absolute regression errors.

The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently. In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems.

Introduction to Research - This session will help you to start the wonderful journey of research. Finding a research problem - Finding a research problem is the most important aspect of any research project . Introduction to Machine Learning:- What is Machine Learning?, - in this session we will get an overview of machine learning

This is the official application form for the Ghana Data Science Summit 2023 (IndabaX Ghana). Date of Conference: Saturday, 13th May, 2023 Venue: Methodist University College, Accra Please read the following general instructions/comments before completing the application: (1) Please respond to as many questions as you can in a truthful manner. (2) All applications will be reviewed by the organizing team and decisions made based on interest and academic and professional background. If you are new to this field, you are still welcome to apply. (3) Admission to the conference is free but you must be accepted by the organizing team to attend. (4) Please submit only ONE application. Multiple applications will be disqualified. (5) This year's conference will take place in one day and will comprise a hands-on tutorials session, a hackathon and poster presentations. You will be asked to indicate your interest in this form. Kindly note that you can either choose the hands-on tutorial session OR the hackathon and NOT BOTH. Also, regardless of the option you choose, you can submit a proposal for a poster presentation. Hands-On Tutorials (Recommended for Beginners) The hands-on tutorial will cover basics in Python programming useful for Machine Learning. We would cover topics like Python Lists, Introduction to Numpy, and Introduction to Scikit-Learn. Additionally, we would go over a practical project using Python. This project will guide you in a step-by-step process of building a Machine Learning Project with Python. We encourage all participants who would be selected to participate to bring along their laptops. No prior knowledge of Python programming or Machine Learning is required. Hackathon (Recommended for Intermediates and Experts) The Hackathon will be based on a practical Machine Learning project where you would have access to a starter notebook. You will be required to work in a team to come up with a better solution that can get the best score on the leaderboard. All selected participants are highly encouraged to come along with their laptops to participate in the competition. We would also not be providing any GPUs as you will not necessarily need this in the Hackathon. Advanced or intermediate knowledge in Python programming and machine learning is highly required. Prizes will be awarded to the best 3 teams on the leaderboard. (6) Kindly email us via info@indabaxghana.com if you have any questions. www.indabaxghana.com

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Any Metadata related to ML artifact creation is tracked here. We also track performance of the ML Model. Experiments become reproducible and comparable between each other. Model Registry could and in some cases should be treated as part of ML Metadata Store.

Springer has released hundreds of free books on a wide range of topics to the general public. The list, which includes 408 books in total, covers a wide range of scientific and technological topics. In order to save you some time, I have created one list of all the books (65 in number) that are relevant to the data and Machine Learning field. Among the books, you will find those dealing with the mathematical side of the domain (Algebra, Statistics, and more), along with more advanced books on Deep Learning and other advanced topics. You also could find some good books in various programming languages such as Python, R, MATLAB, etc.

With the rapid development of various social media platforms, people have an increasing and ubiquitous need for fancy image editing. Back at times, editing still requires specialized software and labor-intensive manual operations like Photoshop. Now we can easily employ AI-based techniques which significantly lower this barrier. Deep neural networks can now produce compelling results for various low-level image editing tasks, such as image inpainting, composition, colorization, and even aesthetic enhancement by learning from richly available paired data. Diffusion models work by destroying training data by adding noise and then learning to recover the data by reversing this noise process.

In 1988, The first successful CNN was implemented by the Computer Scientist Yann LeCun where CNNs were used for scanning the bank checks and recognizing characters from them. Have you ever wondered how object detection works and aids in designing self-driving cars, or how it performs medical image classification for disease detection? A Convolutional neural network is neuro-scientifically connected with the human brain. The design of CNN is inspired by the visual cortex of human brain. This visual cortex processes a large amount of data when we look at the image.

Machine learning (ML) allows us to teach computers to make predictions and decisions based on data and learn from experiences. In recent years, incredible optimizations have been made to machine learning algorithms, software frameworks, and embedded hardware. Thanks to this, running deep neural networks and other complex machine learning algorithms is possible on low-power devices like microcontrollers. This course will give you a broad overview of how machine learning works, how to train neural networks, and how to deploy those networks to microcontrollers, which is known as embedded machine learning or TinyML. You do not need any prior machine learning knowledge to take this course.