Data Science Blog

MARCH 14, 2023

Dann etwa im Jahr 2018 flachte der Hype um Big Data wieder ab, die Euphorie änderte sich in eine Ernüchterung, zumindest für den deutschen Mittelstand. GPT-3 wurde mit mehr als 100 Milliarden Wörter trainiert, das parametrisierte Machine Learning Modell selbst wiegt 800 GB (quasi nur die Neuronen!) ChatGPT basiert auf GPT-3.5

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How to Learn Machine Learning

OCTOBER 10, 2024

Real-Life Examples of Poor Training Data in Machine Learning Amazon’s Hiring Algorithm Disaster In 2018, Amazon made headlines for developing an AI-powered hiring tool to screen job applicants. Data Labeling Accurate labeling is extremely important in supervised learning. Let’s explore some real-world failures.

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ODSC - Open Data Science

MARCH 22, 2023

Previously, he was a senior scientist at Amazon Web Services developing AutoML and Deep Learning algorithms that now power ML applications at hundreds of companies. A recent report by Cloudfactory found that human annotators have an error rate between 7–80% when labeling data (depending on task difficulty and how much annotators are paid).

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IBM Journey to AI blog

JUNE 23, 2023

The introduction of ChatGPT capabilities has generated a lot of interest in generative AI foundation models (these are pre-trained on unlabeled datasets and leverage self-supervised learning with the help of Large Language Models using a neural network ). The ROE ranges also varied by country, from –5% to +13% [1].

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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.,

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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.

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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.

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Explosion

JANUARY 12, 2018

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

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Pickl AI

APRIL 10, 2023

As per the recent report by Nasscom and Zynga, the number of data science jobs in India is set to grow from 2,720 in 2018 to 16,500 by 2025. Top 5 Colleges to Learn Data Science (Online Platforms) 1. The amount increases with experience and varies from industry to industry.

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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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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.

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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.

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Natural Language Processing Python Machine Learning Machine Learning 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.

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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.

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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.

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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).

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