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.

ML 71

ML ML AWS Data Scientist 71

Towards AI

JULY 23, 2023

Year and work published Generative Pre-trained Transformer (GPT) In 2018, OpenAI introduced GPT, which has shown, with the implementation of pre-training, transfer learning, and proper fine-tuning, transformers can achieve state-of-the-art performance. But, the question is, how did all these concepts come together?

Natural Language Processing 87

Natural Language Processing Supervised Learning Deep Learning Deep Learning 87

Flipboard

MARCH 13, 2023

Yes, large language models (LLMs) hallucinate , a concept popularized by Google AI researchers in 2018. Hallucinations May Be Inherent to Large Language Models But Yann LeCun , a pioneer in deep learning and the self-supervised learning used in large language models, believes there is a more fundamental flaw that leads to hallucinations.

Deep Learning 163

Deep Learning Deep Learning Supervised Learning AI 163

Explosion

MAY 17, 2023

Once you’re past prototyping and want to deliver the best system you can, supervised learning will often give you better efficiency, accuracy and reliability than in-context learning for non-generative tasks — tasks where there is a specific right answer that you want the model to find. That’s not a path to improvement.

Supervised Learning 135

Supervised Learning Natural Language Processing Clustering Machine Learning 135

IBM Journey to AI blog

AUGUST 1, 2023

They can also perform self-supervised learning to generalize and apply their knowledge to new tasks. An open-source model, Google created BERT in 2018. A specific kind of foundation model known as a large language model (LLM) is trained on vast amounts of text data for NLP tasks.

AI 60

AI AI Machine Learning Machine Learning 60

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

Natural Language Processing Supervised Learning Machine Learning Machine Learning 83

Mlearning.ai

JULY 31, 2023

One example is the Pairwise Inner Product (PIP) loss, a metric designed to measure the dissimilarity between embeddings using their unitary invariance (Yin and Shen, 2018). Yin and Shen (2018) accompany their research with a code implementation on GitHub here. Fortunately, there is; use an embedding loss. Equation 2.3.1. and Auli, M.,

Deep Learning 52

Deep Learning Deep Learning Supervised Learning Algorithm 52

Snorkel AI

MAY 25, 2023

Data scientists and researchers train LLMs on enormous amounts of unstructured data through self-supervised learning. The model then predicts the missing words (see “what is self-supervised learning?” From 2018 to the modern day, NLP researchers have engaged in a steady march toward ever-larger models.

Natural Language Processing 52

Natural Language Processing Python Machine Learning Machine Learning 52

Snorkel AI

MAY 25, 2023

Data scientists and researchers train LLMs on enormous amounts of unstructured data through self-supervised learning. The model then predicts the missing words (see “what is self-supervised learning?” From 2018 to the modern day, NLP researchers have engaged in a steady march toward ever-larger models.

Natural Language Processing 52

Natural Language Processing Python Machine Learning Machine Learning 52

Explosion

JANUARY 12, 2018

We think 2018 can be even better – to stay in the loop, follow us on Twitter.

Machine Learning 52

Machine Learning Machine Learning Supervised Learning Python 52

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. Then, in 2018 Image Transformer used the autoregressive Transformer model to generate images. Combining this with PixelCNN yielded high-quality images. These are complex topics to grapple with.

ML 132

ML ML AI AI 132

Mlearning.ai

APRIL 13, 2023

Before we discuss the above related to kernels in machine learning, let’s first go over a few basic concepts: Support Vector Machine , S upport Vectors and Linearly vs. Non-linearly Separable Data. Support Vector Machine Support Vector Machine ( SVM ) is a supervised learning algorithm used for classification and regression analysis.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Support Vector Machines 52

AssemblyAI

AUGUST 22, 2023

After processing an audio signal, an ASR system can use a language model to rank the probabilities of phonetically-equivalent phrases Starting in 2018, a new paradigm began to emerge. Using such data to train a model is called “supervised learning” On the other hand, pretraining requires no such human-labeled data.

Supervised Learning 97

Supervised Learning AI AI Machine Learning 97

AWS Machine Learning Blog

AUGUST 7, 2023

The transformer architecture was the foundation for two of the most well-known and popular LLMs in use today, the Bidirectional Encoder Representations from Transformers (BERT) 4 (Radford, 2018) and the Generative Pretrained Transformer (GPT) 5 (Devlin 2018).

AWS 75

AWS ML ML Data Science 75