Heartbeat

JULY 20, 2023

Limited availability of labeled datasets: In some domains, there is a scarcity of datasets with fine-grained annotations, making it difficult to train segmentation networks using supervised learning algorithms. When evaluated on the MS COCO dataset test-dev 2017, YOLOv8x attained an impressive average precision (AP) of 53.9%

Machine Learning 52

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AWS Machine Learning Blog

AUGUST 16, 2023

Training machine learning (ML) models to interpret this data, however, is bottlenecked by costly and time-consuming human annotation efforts. One way to overcome this challenge is through self-supervised learning (SSL). The types of land cover in each image, such as pastures or forests, are annotated according to 19 labels.

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ML ML AWS Data Scientist 71

Explosion

JANUARY 12, 2018

spaCy In 2017 spaCy grew into one of the most popular open-source libraries for Artificial Intelligence. Highlights included: Developed new deep learning models for text classification, parsing, tagging, and NER with near state-of-the-art accuracy. spaCy’s Machine Learning library for NLP in Python. Released Prodigy v1.0,

Machine Learning 52

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Flipboard

MARCH 13, 2023

OpenAI has pioneered a technique to shape its models’ behaviors using something called reinforcement learning with human feedback (RLHF). Having a human periodically check on the reinforcement learning system’s output and give feedback allows reinforcement learning systems to learn even when the reward function is hidden. “I’m

Deep Learning 163

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Hacker News

MAY 3, 2023

The core process is a general technique known as self-supervised learning , a learning paradigm that leverages the inherent structure of the data itself to generate labels for training. Fine-tuning may involve further training the pre-trained model on a smaller, task-specific labeled dataset, using supervised learning.

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AssemblyAI

MAY 23, 2023

" These models are trained using self-supervised learning , a technique that utilizes the data's inherent structure to generate labels for training. Almost all current LMs are based on a highly successful architecture, the Transformer model , introduced in 2017.

Supervised Learning 52

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Snorkel AI

MARCH 1, 2023

Foundation models are large AI models trained on enormous quantities of unlabeled data—usually through self-supervised learning. What is self-supervised learning? Self-supervised learning is a kind of machine learning that creates labels directly from the input data. Find out in the guide below.

Natural Language Processing 83

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Mlearning.ai

JULY 31, 2023

2017) paper, vector embeddings have become a standard for training text-based DL models. Data2Vec: A General Framework For Self-Supervised Learning in Speech, Vision and Language. It is none other than the legendary Vector Embeddings! Without further ado, let’s dive right in! A vector embedding is an object (e.g., and Auli, M.,

Deep Learning 52

Deep Learning Deep Learning Supervised Learning Algorithm 52

Mlearning.ai

AUGUST 6, 2023

Towards the end of my studies, I incorporated basic supervised learning into my thesis and picked up Python programming at the same time. I also started on my data science journey by attending the Coursera specialization by Andrew Ng —  Deep Learning. That was in 2017.

Data Science 52

Data Science Machine Learning Machine Learning ML 52

Applied Data Science

MARCH 11, 2022

In contrast to classification, a supervised learning paradigm, generation is most often done in an unsupervised manner: for example an autoencoder , in the form of a neural network, can capture the statistical properties of a dataset. One does not need to look into the math to see that it’s inherently more difficult.

AI 130

AI AI Data Science Machine Learning 130

Google Research AI blog

JANUARY 18, 2023

They were followed in 2017 by VQ-VAE, proposed in “ Neural Discrete Representation Learning ”, a vector-quantized variational autoencoder. These two architectures helped lay the foundation for pixel-level generation using deep neural networks. Combining this with PixelCNN yielded high-quality images.

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AWS Machine Learning Blog

AUGUST 7, 2023

The Transformer architecture 3 (Vaswani, 2017) was a breakthrough improvement on the encoder-decoder; it introduced the concept of self-attention , which allowed the model to focus its attention on different words on the input and output phrases.

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AWS ML ML Data Science 75