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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.


A Wild Snark comforts Alice by Wild Snark

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A Wild Snark comforts Alice is a piece of digital artwork by Wild Snark which was uploaded on January 21st, 2022. The digital art may be purchased as wall art, home decor, apparel, phone cases, greeting cards, and more. All products are produced on-demand and shipped worldwide within 2 - 3 business days.


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.


The goal of life is to finish

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Created the world's first internet cafe. Meet Minho Jung, the founder of Sahara Street LLC, known as the creator of the PC room. I understand that you applied for a patent related to Metaverse 20 years ago. As you know, Metaverse is emerging as the hottest potato these days, and I am curious about the patent background. In fact, in 20 years, it was known as AR and VR.


More special features in Python

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Python is an awesome programming language! It is one of the most popular languages for developing AI and machine learning applications. With a very easy to learn syntax, Python has some special features that distinguish it from other languages. Python Special Features Photo by M Mani, some rights reserved. The libraries used in this tutorial are imported in the code below.


Addition and Subtraction using Recurrent Neural Networks.

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How does google understand how to translate '今日はどうですか?' to'How are you doing today?' or vice versa? How do we get to predict a disease spread such as COVID-19 way into the future beforehand? How do automatic Text generation or Text Summarization mechanisms work? The answer is Recurrent Neural Networks. RNNs have been the solution to deal with most problems in Natural language Processing and not only NLP but in Bio-informatics, Financial Forecasting, Sequence modelling etc.


UNDP's Initiative To Build an Immersive and Safe World with AI

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UNDP has launched a new digital strategy to enhance its support to governments in shifting to this transforming environment including by building digital capacity within the organization. The AI strategy of UNDP seeks to increase the understanding of digital technologies and how they can be used to achieve the Sustainable Development Goals. As well as risks and trade-offs that come with them. On the other hand, it is also working to manage the unique ethical issues that can rise from deploying AI in an international development context. The AI readiness tool for stakeholders is building on UNDP's Digital Readiness Assessment piloted.


10 Takeaways from the Harvard Business Review on Artificial Intelligence

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There have been Kondratiev waves throughout history, commonly referred to as innovation waves, including the invention of electricity, the printing press, and the steam engine. All of these technologies spurred a paradigm shift which resulted in transforming the way the world operated. Today, many believe AI is the next Kondratiev wave and that it will be responsible for transforming how businesses create value, how people work, and ultimately how people live. For businesses to survive the era of AI, they must prepare to abandon legacy technology and invest in new ways of doing things, sometimes reasonably quickly in order to stay relevant. This phenomenon is called the "burning platform" effect, based on the idea that in order to stay competitive, businesses must adopt a radical change strategy as if their current way of doing things was on fire.


Supervised vs Unsupervised & Discriminative vs Generative

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Highlights: GANs and classical Deep Learning methods (classification, object detection) are similar, but they are also fundamentally different in nature. Reviewing their properties will be the topic of this post. Therefore, before we proceed further with the GANs series, it will be useful to refresh and recap what is supervised and unsupervised learning. In addition, we will explain the difference between discriminative and generative models. Finally, we will introduce latent variables, since they are an important concept in GANs.