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>**T5** \- (Text to Text Transfer Transformer) is a large seq2seq **transformer** model ( it has both encoder and decoder). it is pre-trained on the C4 (Colossal Clean Crawled Corpus) dataset and is flexible for fine-tuning on a variety of downstream tasks. It achieves state-of-the-art results on many NLP benchmarks. T5 models can be used ...
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How to automate data science code with Jenkins and Docker: MLOps = ML + DEV + OPS Photo by Annamária Borsos MLOPS = ML + DEV + OPS How many created AI models have been put into production in enterprises ? With investment in data science teams and technologies, the number of AI projects increased significantly and with it a number of missed opportun ...
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So I'm creating a neural network that can make good moves in chess based on the current state of the board.
It's going to have 69 input nodes, 64 of which are assigned to the positions on the chess board and indicate. Which piece (if any) is on that part of the board. Next I'll have 1 indicating en passant available and 4 indicating castling ava ...
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A critical, powerful, precious resource sits mostly idle. Why? Photo by Dušan veverkolog on Unsplash Like a sleeping lion, the GPUs we already use have way more power than meets the eye In the last post, we explored how near-future business transformation is threatened by a GPU supply pinch. We know that GPU is a critical resource for rising techno ...
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A somewhat less math-intensive, step-by-step guide for building a one hidden-layer neural network from the ground up Image by Lindsay Henwood on Unsplash This two-part article takes a more holistic, overarching (and yes, less math-y) approach to building a neural network from scratch. Python for completing the network is also included in each of th ...
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Automatize cross-validated machine-learning training jobs on AWS infrastructure Image by TeeFarm from Pixabay Cross-validation is a powerful technique to build machine learning models that perform well on unseen data. However, it can also be time-consuming as it includes training multiple models. This post will show you how to easily cross-validate ...
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I spent a year building a spellchecker, and all I got is some grumbling to share. When I started rebuilding the world’s most popular spellchecker, I had several goals. But the main one was: understand spellchecking better, share this understanding with others, and, ideally, push spellchecking development a little bit. Photo by Michael Dziedzic on U ...
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Posted by Zarana Parekh, Software Engineer and Jason Baldridge, Staff Research Scientist, Google Research The past decade has seen remarkable progress on automatic image captioning, a task in which a computer algorithm creates written descriptions for images. Much of the progress has come through the use of modern deep learning methods developed fo ...
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https://www.wired.com/story/ai-fueled-dungeon-game-got-much-darker/
If you haven't been following the drama around AI Dungeon, this is a good summary and a good discussion on filter/algo difficulty. ...
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TL;DR: Got scooped by MLP-Mixer, so I'm releasing my writeup/code/models. I hope someone finds them interesting/useful.
Lately I've been trying a couple variants of simple vision transformers to better understand what makes them perform well. About a month ago, I found that you could replace the attention layers with feed-forward layers and get ...
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OpenCV is a library of programming functions mainly aimed at real-time computer vision. We just released a course on the freeCodeCamp.org YouTube channel that will teach you how to use OpenCV in the cloud with Python. Misbah Mohammed created this course. He has a lot of experience with machine
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Posted by Jonathan Mallinson and Aliaksei Severyn, Research Scientists, Google Research Sequence-to-sequence (seq2seq) models have become a favoured approach for tackling natural language generation tasks, with applications ranging from machine translation to monolingual generation tasks, such as summarization, sentence fusion, text simplification, ...
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I haven't had a lot of maths in my uni career, but I want to follow an edX course on Machine Learning (MIT Machine Learning with Python: from Linear Models to Deep Learning), which starts in September. I have had quite a bit of statistics in my uni career, and I'll be following Probability - The Science of Uncertainty and Data and Fundamentals of S ...
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https://i.redd.it/y52s5u8594x61.gif
Hi r/machinelearning,
My team at Activeloop partnered with Google to make Google Objectron available in under ±5 seconds (per dataset category). Google Objectron is one of ...
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Natural language processing (NLP) is a field that focuses on making natural human language usable by computer programs. NLTK, or Natural Language Toolkit, is a Python package that you can use for NLP. A lot of the data that you could be analyzing is unstructured data and contains human-readable text. Before you can analyze that data programmaticall
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Reinforcement learning (RL) has been used successfully for solving tasks which have a well defined reward function – think AlphaZero for Go, OpenAI Five for Dota, or AlphaStar for StarCraft. However, in many practical situations you don’t have a well defined reward function. Even a task as seemingly straightforward as cleaning a room has many subtl
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Stock Sentiment Analysis Using Autoencoders In this notebook, we will use autoencoders to do stock sentiment analysis. Autoencoder consists of encoder and decoder models. Encoders compress the data and decoders decompress it. Once you train an autoencoder neural network, the encoder can be used to train a different machine learning model. For stock
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\*\*DISCLAIMER\*\*: This is completely free and not sponsored in any way. I really just enjoy helping students get started and potentially transition into Data Science
As the title mentions, I'm a Senior Data Scientist at Disney and I'm going to host **another** Data Science Q&A this Thursday at 5:30 PM PST. This time I'll have **Krishna Rao** j ...
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Posted by Thao Nguyen, AI Resident, Google Research A common practice to improve a neural network’s performance and tailor it to available computational resources is to adjust the architecture depth and width. Indeed, popular families of neural networks, including EfficientNet, ResNet and Transformers, consist of a set of architectures of flexible ...
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