MLconf gathers machine learning & AI enthusiasts from a broad range of industries and academic backgrounds to share new tools, tricks, platforms, algorithms and methods with a broad audience of practitioners. Each presentation offers an educational component to be shared with the community, in which specific algorithms and techniques can be shared and new applications of such are inspired. Today, we are making a call for presentations for our MLconf San Francisco conference to be held on October 19, 2023 at the Hotel Nikko in SF. The conference will feature presentations from across the machine learning landscape. If you, your team, organization, or colleague has done something innovative related to ML algorithms, Tools and Platforms, or Building and Managing Teams to solve hard problems, let us help you share your story. In your abstract, we encourage you to mention where you feel your techniques will transfer over into other Machine Learning applications, showing where it's relevant to the MLconf audience. Prior submissions have included presentations related to: Algorithms that have graduated from an academic/theory state and have proven to be effective, robust and scalable in production within industry application; Machine Learning/AI examples of specific challenges faced within current industry and how teams have found success by applying new algorithms and techniques or by applying modifications to existing practices for optimal outcomes; New platforms, tools for machine learning; New business practices for managing and growing data science teams; and Expanding machine learning to new domains. Abstracts should be 150-500 words in length and should illustrate the level of technicality in the proposed presentation. At the time of the event, presentations will be generally limited to 25-30 minutes in length in order to allow you to provide depth while also allowing for presentations from colleagues and Q&A. Emphasis should be given to the technical challenges, benchmarks, innovations and motivation for the development of models, algorithms and statistical models to analyze and draw inferences from patterns in data. Your presentation should definitely not be a product or sales pitch. ABSTRACT DEADLINE: June 30, 2023 Topics we are looking for include but are not limited to: AI/ML Ops Natural Language Processing Deep Learning Reinforcement Learning Data Science for Social Good Kernel Methods Causality Embeddings Recommendation Systems Quantum Computing and AI/ML Chemistry & AI/ML Pandemic Data & ML Model Interpretability Fraud Detection DeepFake Detection Generative Teaching Networks Facial Recognition/Biometric Identification Genetics & ML Experimental Reproducibility Best Practices Model Uncertainty and Data Drift Generative Adversarial Networks Transfer Learning Adversarial Machine Learning IoT and edge computing applications Genetic Algorithms Tensor Algebra Probabilistic Programming and Logic Machine Learning for Music and Art Bayesian Methods Markov Logic Networks Synthetic Art, Biology Ethics in Machine Learning Data / Algorithm Ethics Sketching Randomized Algorithms AI Education Game Theory Diversity in AI Community Detection Time Series Image Analysis Structured Learning using Neural Networks Healthcare & ML (Clinical Decision Support Systems, Record Keeping, Medical Imaging, etc.) FinTech & ML (Algorithmic Trading, Predictive Analytics, Fraud Detection & Prevention, Payments, etc.) In the spirit of sharing knowledge, presentation slides are shared with attendees and photographs and/or video footage of presentations are shared as well.

Machine Learning (ML) and Artificial Intelligence (AI) are two of the most popular and rapidly developing technologies used in many industries today. They have been around for decades, but their importance has grown exponentially in the last few years due to advances in technology and the increasing need for automation and data analysis. In this article, we will explore what ML and AI are, how they are different, and how they can be used in various fields. Before we dive into the specifics of ML and AI, it is important to understand what they are. Machine Learning is a type of artificial intelligence that enables computer systems to learn from data and make decisions without being explicitly programmed.

Have you ever wondered how your mobile uses facial recognition to unlock itself? Or how different deep learning models are used to detect anomalies in an image? Or how do the Instagram filters work in order to manipulate the image it receives? In this post, we are going to understand how image processing and computer vision work, and how they are used with deep learning in order to create innovative and complex solutions for many day-to-day problems. In order to get a better understanding of this article, I recommend you to read my article on Artificial intelligence and Machine Learning.

Data science is one of the rapidly growing fields that demand a data scientist growing up daily. As of October 2020, I can't see this demand slowing down anytime soon. It is an interdisciplinary field that can help us analyze the data around us to make our life better and our future brighter. Luckily, becoming a data scientist does not require a degree. As long as you are open to learning new things and willing to put in the effort and time, you can become a data scientist.

Image by the author (made using Canva). Data science is one of the rapidly growing fields that demands that a data scientist grows up daily, and I can't see this demand slowing down anytime soon. It is an interdisciplinary field that can help us analyze the data around us to make our life better and our future brighter. Luckily, becoming a data scientist does not require a degree. As long as you are open to learning new things and willing to put in the effort and time, you can become a data scientist.

When fantasies and Innovative technologies mix and gel together, the result would always be a magic to witness. Yes, Google has always been leading in bringing a lot of inventions that not only give us a wow factor but also make our day-today activities much easier. One such invention is PROJECT SOLI. It's a futuristic interface that will forever change the way we use all the technological devices, and not just wearable. A smartphone or a device with a Soli Chip would allow you to just wave your fingers in the air to naturally interact and get things done. What is Project Soli? Soli is a creation of Google's research and development lab, ATAP (Advanced Technology and Projects). Project Soli has a millimetre-wave radar chip that can detect "very fine" gestures with your fingers and hands in front of your phone – without touching it. It can then be used for anything from games to web browsing using hand gestures on mobile devices, computers, and electronics. Project Soli is having

Having one of these in your resume can make a lot of difference. Data science is one of the most versatile fields ever around; even its name is not very explanatory of what the field actually involves. Perhaps that's one reason people find this field quite challenging and difficult to get into and even more difficult to show professionalism. It is well known within the data science community that to be a "good" data scientist is all about how strong of a portfolio you build, how diverse your projects are, and how well they show your ability to solve any problem creatively and efficiently. Although being a data scientist -- or have a specialty in any of its branches -- doesn't require a university degree, having some certificate that proves your profession in some aspects of the field can transform your portfolio and take your career on step further.

Due to recent technological developments, Machine Learning (ML), a subfield of Artificial Intelligence (AI), has been successfully used to process and extract knowledge from a variety of complex problems. However, a thorough ML approach is complex and highly dependent on the problem at hand. Additionally, implementing the logic required to execute the experiments is no small nor trivial deed, consequentially increasing the probability of faulty code which can compromise the results. Propheticus is a data-driven framework which results of the need for a tool that abstracts some of the inherent complexity of ML, whilst being easy to understand and use, as well as to adapt and expand to assist the user's specific needs. Propheticus systematizes and enforces various complex concepts of an ML experiment workflow, taking into account the nature of both the problem and the data. It contains functionalities to execute all the different tasks, from data preprocessing, to results analysis and comparison. Notwithstanding, it can be fairly easily adapted to different problems due to its flexible architecture, and customized as needed to address the user's needs.

Practically they are the same thing. DS: stats like avg, median and sum are also metrics considered to be tools in Data Science. Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data.[3] It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization. Making a visualization tool can also be a data science solution.