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Machine Learning


The Growing Role of Machine Learning in Fraud Detection

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Machine learning (ML) can quickly detect fraud, saving organizations and consumers time and money when implemented correctly. As organizations grapple with how to keep up with consumers during the Covid-19 pandemic, they are also dealing with an evolving digital landscape, with online payment fraud losses alone set to exceed $206 billion between 2021 and 2025. While machine learning can save organizations exponential amounts of time and money when implemented correctly, it can also come with some initial challenges. The key to any accurate machine learning model is the input data. Not only does enough historical data need to exist for the model to derive an accurate representation but the data also needs to be accessible.


Data2vec: The first high-performance self-supervised algorithm that works for speech, vision, and text

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But while people appear to learn in a similar way regardless of how they get information -- whether they use sight or sound, for example -- there are currently big differences in the way self-supervised learning algorithms learn from images, speech, text, and other modalities. This discrepancy has been a significant barrier to applying advances in self-supervised learning more broadly. Because a powerful algorithm designed for, say, understanding images can't be directly applied to another modality, such as text, it is difficult to push several modalities ahead at the same rate. This is why Meta AI developed and is excited to announce data2vec, the first high-performance self-supervised algorithm that works for multiple modalities. We apply data2vec separately to speech, images and text and it outperformed the previous best single-purpose algorithms for computer vision and speech and it is competitive on NLP tasks.


PhD Position in Clinical data science, Machine learning, Computer security - SDU, Denmark

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We are seeking outstanding candidates with strong analytical and problem solving skills, who are strong in written and oral communication (in English), and have documented experience in the development of complex compute systems. The applicant should have provable skills in the state-of-the-art web-development frameworks, virtualization techniques as well as database technologies. Expertise in clinical data science and machine learning, as well as computer security and data privacy are welcome. A large roadblock of medical research is the difficult access to sensitive data which therefore hinders the training of complex and powerful machine learning concepts. This issue is amplified when considering rare diseases with low incidence numbers per hospital.


Meet Sri Lankan Researcher -- Jayakody Kankanamalage Chamani Shiranthika

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What are you currently working on or worked on before? I worked on international research projects related to Artificial Intelligence research areas. My main research area is reinforcement learning. Apart from that, I engaged in machine learning-related research projects related to personalized recommendations, cancer chemotherapy treatments, frailty analysis, cancer patients' survival rates analysis, etc. Other core research areas I have worked in areas like the travel industry, Internet, Internet of Things, air pollution, behavioral sciences computing, convolutional neural nets, environmental factors, health care,human-computer interaction, recommender systems, recurrent neural nets, sentiment analysis, social networking (online), time series, unsupervised learning, etc. I am seeking research collaboration opportunities, academic positions, industrial AI events, worldwide, and would love to work on collaborative projects.


How Humans and Machines Will Cooperate in the Future of Work

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Industry 4.0 technologies have left the world awash in data. The fourth wave of industrialization also gave us the tools to mine that data for intelligence and insights. We are now busy applying those insights to change the way we live, play, learn and earn. Today, we have cheap and easy access to the fundamental ingredients of Industry 4.0: smart sensors, data, analytics, cloud, 3-D printing, the internet of things (IoT), artificial intelligence, augmented reality, machine learning, blockchain, digital twins, and horizontal and vertical system integration. Using these, businesses are rewriting the rules of the game.


60 days of Data Science and Machine Learning Series -- Day 1

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In this post we will cover end to end Python Basics ( Part 1) that you should know. You can read the Day 2 post here. You can read the Day 3 post here. Python is a high-level, most widely used multi-purpose, easy to read programming language.


Learn Python for Data Science & Machine Learning from A-Z

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In this practical, hands-on course you'll learn how to program using Python for Data Science and Machine Learning. In this practical, hands-on course you'll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner. Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job. We'll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib NumPy -- A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.


BERT for Individual: Tutorial+Baseline

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So if you're like me just beginning out at NLP after finishing a few months building Computer Vision models as a beginner then surely this story has something in supply for you. BERT is a deep learning model that has given state-of-the-art results on a wide variety of natural language processing tasks. It stands for Bidirectional Encoder Representations for Transformers. It has been pre-trained on Wikipedia and BooksCorpus and requires (only) task-specific fine-tuning. It has caused a stir in the Machine Learning community by presenting state-of-the-art results in a wide variety of NLP tasks, including Question Answering (SQuAD v1.1), Natural Language Inference (MNLI), and others.


Joan Fontanals – Principal Engineer – Jina.AI

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I had a pleasure to sit down with Joan Fontanals – Principal Engineer with Jina.AI -- framework with lots of capabilities to support your neural search journey. Listen to or watch the podcast and get a chance to win awesome swag from Jina.AI. As a special line of thank-yous, I'd like to mention Saurabh Rai, who kindly designed the Thumbnail of this episode!


A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence - Nature Neuroscience

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Extensive sampling of neural activity during rich cognitive phenomena is critical for robust understanding of brain function. Here we present the Natural Scenes Dataset (NSD), in which high-resolution functional magnetic resonance imaging responses to tens of thousands of richly annotated natural scenes were measured while participants performed a continuous recognition task. To optimize data quality, we developed and applied novel estimation and denoising techniques. Simple visual inspections of the NSD data reveal clear representational transformations along the ventral visual pathway. Further exemplifying the inferential power of the dataset, we used NSD to build and train deep neural network models that predict brain activity more accurately than state-of-the-art models from computer vision. NSD also includes substantial resting-state and diffusion data, enabling network neuroscience perspectives to constrain and enhance models of perception and memory. Given its unprecedented scale, quality and breadth, NSD opens new avenues of inquiry in cognitive neuroscience and artificial intelligence. The authors measured high-resolution fMRI activity from eight individuals who saw and memorized thousands of annotated natural images over 1 year. This massive dataset enables new paths of inquiry in cognitive neuroscience and artificial intelligence.