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Thanks for the resources guys and now i’m even more confused lol so much resources out there. I don’t want to spend all my time on calculus without even diving into the practical areas of ML and DL ...
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Photo by Sigmund on Unsplash Extract information from a pretrained model using Pytorch and Hugging Face Goal Let’s begin by defining what our purpose is for this hands-on article. We want to build a model that takes as input one or more documents, and manages to classify them by their content. Some categories are for example : politics, travel , sp ...
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Before I launch learning Deep Learning with frameworks, I want to get a good understanding about how they work. Preferably, I want a resource which can teach me about deep learning and common deep learning structures in NumPy. Is this a good idea? Or does it seem unnecessary? If it's not a bad idea, do you guys know about any resources which I coul ...
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Considering the “Invisible Gap” between Measurements and their Meaning In an era of empiricism, where insights that are “data-driven” are automatically deemed superior, quantification is vital. Indeed, the measurement of constructs and phenomena is at the core of empirical science, research, and reasoning. However, this quantification is often chal ...
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Prominent statistician Frank Harrell has come out with a radically new R tutorial, rflow. The name is short for “R workflow,” but I call it “R in a box” –everything one needs for beginning serious usage of R, starting from little or no background. By serious usage I mean real ... Continue reading: A Major Contribution to Learning R
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Posted by Ethan Dyer and Guy Gur-Ari, Research Scientists, Google Research, Blueshift Team Language models have demonstrated remarkable performance on a variety of natural language tasks — indeed, a general lesson from many works, including BERT, GPT-3, Gopher, and PaLM, has been that neural networks trained on diverse data at large scale in an uns ...
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Posted by Dan Walker and Dan Liebling, Software Engineers, Google Research People don’t write in the same way that they speak. Written language is controlled and deliberate, whereas transcripts of spontaneous speech (like interviews) are hard to read because speech is disorganized and less fluent. One aspect that makes speech transcripts particular ...
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Generating State-of-the-Art Text Embeddings with Hardware Accessible by Everyone OpenAI GPT-3 — Do I really need it to generate state-of-the-art text embeddings? Source: Created by the author In this article, I’ll be demonstrating that large language models such as GPT-3 do not generate the best dense text embeddings for many NLP (natural language ...
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So You’ve Got a Really Big Dataset. Here’s How You Clean It. A detailed, step-by-step guide to data cleaning in Python with sample code. Image from Markus Spiske (Unsplash) You have a dataset in hand after scraping, merging, or just plain downloading it off the internet. You’re thinking about all the beautiful models you could run on it but first, ...
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Shedding light on a commonly misunderstood topic in linear algebra Vector norms are extremely important in certain fields of engineering and mathematics. However, I think the education system often presents norms in a formulaic, “here’s how to calculate it” way as opposed to presenting an intuitive understanding of vector norms. Let’s see if we can ...
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We already have architecture(s) which are supposed to fix one of the biggest issues with transformers, namely that they scale quadratically with input size. The performer scales linearly, which should allow for much bigger context windows, yet looking at recent large language models from major players, all of them seem to be using the old transform ...
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Due to its versatility, flexibility, and comprehensiveness, Python is the perfect fit for machine learning solutions .
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Known for its versatility and stability, Python is increasingly becoming an object of interest for those dabbling in machine learning or willing to carry out a machine learning project. As they quickly notice the difference between a standard software development project and an ML one, they search for tools and solutions that will respond to ML-spe
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I saw a paper called *EvilModel* on how to hide malicious code in a neural network as we have thousands or millions of parameters that we can alter.
This basic technique is based on the modification of the `float32` values (but can be adapted to `float16`) where we modify the fraction bits or part of the fraction.
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Addressing and mitigating the effects of climate change requires a collective effort, bringing our strengths to bear across industry, government, academia, and civil society. The post Introducing the Microsoft Climate Research Initiative appeared first on Microsoft Research.
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Introduction Natural Language Processing (NLP) is a powerful tool in the Machine Learning landscape that can (among other things) allow users to classify sentiment and predict text. Many of recent my blogs have been about data manipulation and data engineering, so I decided change things up to look into showing ... Continue reading: RObservations #
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YOLOv6 has been making a lot of noise in the past 24 hours. Based on its performance - rightfully so.
YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance. It outperforms YOLOv5 in accuracy and inference speed, making it the best OS version of YOLO ...
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In order to share the magic of DALL·E 2 with a broad audience, we needed to reduce the risks associated with powerful image generation models. To this end, we put various guardrails in place to prevent generated images from violating our content policy. This post focuses on pre-training
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AWS in a joint effort with Microsoft have established PyWhy as a fresh GitHub organization to integrate AWS algorithms into DoWhy, a casual ML library from Microsoft, which has moved to PyWhy. By Daniel Dominguez
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