AssemblyAI

MAY 17, 2023

Let’s first take a look at the process of supervised learning as motivation. Supervised learning The term supervised learning describes, at a high-level, one paradigm in which data can be used to train an AI model. They can summarize documents, translate between languages, answer questions, and more.

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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

AUGUST 22, 2023

Using such data to train a model is called “supervised learning” On the other hand, pretraining requires no such human-labeled data. This process is called “self-supervised learning”, and is identical to supervised learning except for the fact that humans don’t have to create the labels.

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DrivenData Labs

JUNE 14, 2023

Self-supervision: As in the Image Similarity Challenge , all winning solutions used self-supervised learning and image augmentation (or models trained using these techniques) as the backbone of their solutions. We believe VSC 2022 makes good social impact and is of great value to the deep learning research community.

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Pickl AI

SEPTEMBER 10, 2024

Despite its limitations, the Perceptron laid the groundwork for more complex neural networks and Deep Learning advancements. Introduction The Perceptron is one of the foundational concepts in Artificial Intelligence and Machine Learning.

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Pickl AI

JULY 12, 2024

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. Jupyter notebooks allow you to create and share live code, equations, visualisations, and narrative text documents.

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AssemblyAI

SEPTEMBER 12, 2023

Though once the industry standard, accuracy of these classical models had plateaued in recent years, opening the door for new approaches powered by advanced Deep Learning technology that’s also been behind the progress in other fields such as self-driving cars. The data does not need to be force-aligned.

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Towards AI

FEBRUARY 19, 2025

Machine Learning Basics Machine learning (ML) enables AI agents to learn patterns from data without explicit programming. There are three main types: Supervised Learning: Training a model with labeled data. Unsupervised Learning: Finding hidden structures in unlabeled data. Hugging Face Documentation 3.

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Pickl AI

NOVEMBER 28, 2024

Without linear algebra, understanding the mechanics of Deep Learning and optimisation would be nearly impossible. These techniques span different types of learning and provide powerful tools to solve complex real-world problems. Neural networks are the foundation of Deep Learning techniques.

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Google Research AI blog

MAY 25, 2023

It builds on advances in deep learning efficiency and leverages reinforcement learning from human feedback to provide more relevant responses and increase the model’s ability to follow instructions. We use deep learning technology to achieve voice preservation and lip matching and enable high-quality video translation.

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IBM Journey to AI blog

AUGUST 1, 2023

It’s the underlying engine that gives generative models the enhanced reasoning and deep learning capabilities that traditional machine learning models lack. They can also perform self-supervised learning to generalize and apply their knowledge to new tasks.

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Pickl AI

AUGUST 1, 2023

It includes text documents, social media posts, customer reviews, emails, and more. Here are seven benefits of text mining: Information Extraction Text mining enables the extraction of relevant information from unstructured text sources such as documents, social media posts, customer feedback, and more.

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Pickl AI

OCTOBER 20, 2024

Step-by-Step Guide to Learning AI in 2024 Learning AI can seem daunting at first, but by following a structured approach, you can build a solid foundation and gain the skills needed to thrive in this field. This step-by-step guide will take you through the critical stages of learning AI from scratch. Let’s dive in!

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ODSC - Open Data Science

OCTOBER 4, 2023

Evaluation Techniques for Large Language Models Rajiv Shah, PhD | Machine Learning Engineer | Hugging Face Selecting the right LLM for your needs has become increasingly complex. In this workshop, you’ll see how to build both a simple QA bot as well as an automated workflow agent. Sign me up!

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Heartbeat

FEBRUARY 15, 2023

It is a supervised learning methodology that predicts if a piece of text belongs to one category or the other. As a machine learning engineer, you start with a labeled data set that has vast amounts of text that have already been categorized. Follow the official documentation for additional help with getting started with R.

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DataRobot Blog

DECEMBER 15, 2022

supervised learning and time series regression). In the background, models are being trained in parallel for efficiency and speed—from Tree-based models to Deep Learning models (which will be chosen based on your historical data and target variable) and more. Generate Model Compliance Documentation.

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DagsHub

DECEMBER 23, 2024

Optimized Expert Time Active Learning ensures expert time is spent on cases where their expertise adds the most value. Key Characteristics Static Dataset : Works with a predefined set of unlabeled examples Batch Selection : Can select multiple samples simultaneously for labeling because of which it is widely used by deep learning models.

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Explosion

JANUARY 12, 2018

Highlights included: Developed new deep learning models for text classification, parsing, tagging, and NER with near state-of-the-art accuracy. Relaunched the spaCy website with almost 25k words of documentation and a dozen new usage tutorials, including an extensive spaCy 101 guide for beginners.

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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). His specialty is Natural Language Processing (NLP) and is passionate about deep learning.

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IBM Journey to AI blog

AUGUST 15, 2023

Foundation models are AI models trained with machine learning algorithms on a broad set of unlabeled data that can be used for different tasks with minimal fine-tuning. The model can apply information it’s learned about one situation to another using self-supervised learning and transfer learning.

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BAIR

APRIL 29, 2022

At the same time as the emergence of powerful RL systems in the real world, the public and researchers are expressing an increased appetite for fair, aligned, and safe machine learning systems. However the unique ability of RL systems to leverage temporal feedback in learning complicates the types of risks and safety concerns that can arise.

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Mlearning.ai

JUNE 1, 2023

Using PyTorch Deep Learning Framework and CNN Architecture Photo by Andrew S on Unsplash Motivation Build a proof-of-concept for Audio Classification using a deep-learning neural network with PyTorch framework. This is inherently a supervised learning problem. Data Source here.

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Pickl AI

APRIL 2, 2024

Unstructured Data: Data without a predefined structure, like text documents, social media posts, or images. Machine Learning: Subset of AI that enables systems to learn from data without being explicitly programmed. Supervised Learning: Learning from labeled data to make predictions or decisions.

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Pickl AI

JANUARY 2, 2025

This section explores how entropy contributes to supervised learning , evaluates uncertainty or impurity in datasets, and finds applications across various Machine Learning algorithms and tasks. For instance, in document clustering, entropy can evaluate how well documents within a cluster share common topics.

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Mlearning.ai

JULY 31, 2023

In today’s modern era, we use data in various forms, such as videos, images, audio, and documents. Things become more complex when we apply this information to Deep Learning (DL) models, where each data type presents unique challenges for capturing its inherent characteristics. A vector embedding is an object (e.g., and Auli, M.,

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Google Research AI blog

JUNE 18, 2023

Barron Program Advisory Board includes: Cordelia Schmid , Richard Szeliski Panels History and Future of Artificial Intelligence and Computer Vision Panelists include: Chelsea Finn Scientific Discovery and the Environment Panelists include: Sara Beery Best Paper Award candidates MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient (..)

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Dataconomy

MARCH 27, 2023

Natural language processing (NLP):  ML algorithms can be used to understand and interpret human language, enabling organizations to automate tasks such as customer support and document processing. Unsupervised learning:  This involves using unlabeled data to identify patterns and relationships within the data.

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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. By default, ultralytics includes several models for each task with varying sizes.

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Snorkel AI

JULY 5, 2023

Sheer volume—I think where this came about is when we had the rise of deep learning, there was a much larger volume of data used, and of course, we had big data that was driving a lot of that because we found ourselves with these mountains of data. And in supervised learning, it has to be labeled data. AR : Yeah.

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Snorkel AI

JULY 5, 2023

Sheer volume—I think where this came about is when we had the rise of deep learning, there was a much larger volume of data used, and of course, we had big data that was driving a lot of that because we found ourselves with these mountains of data. And in supervised learning, it has to be labeled data. AR : Yeah.

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Snorkel AI

JULY 5, 2023

Sheer volume—I think where this came about is when we had the rise of deep learning, there was a much larger volume of data used, and of course, we had big data that was driving a lot of that because we found ourselves with these mountains of data. And in supervised learning, it has to be labeled data. AR : Yeah.

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DagsHub

NOVEMBER 11, 2024

Textual Data Textual data is one of the most common forms of unstructured data and can be in the format of documents, social media posts, emails, web pages, customer reviews, or conversation logs. So, we must understand the different unstructured data types and effectively process them to uncover hidden patterns.

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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. A language model can be fine-tuned on medical documents for specialized tasks in the medical field.

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The MLOps Blog

FEBRUARY 22, 2024

Important note: Continual learning aims to allow the model to effectively learn new concepts while ensuring it does not forget already acquired information. Plenty of CL techniques exist that are useful in various machine-learning scenarios. Model personalization via continual learning in a document classification process.

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Pickl AI

SEPTEMBER 12, 2024

Decision Trees: A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks. Deep Learning : A subset of Machine Learning that uses Artificial Neural Networks with multiple hidden layers to learn from complex, high-dimensional data.

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ODSC - Open Data Science

FEBRUARY 14, 2023

The model was fine-tuned to reduce false, harmful, or biased output using a combination of supervised learning in conjunction to what OpenAI calls Reinforcement Learning with Human Feedback (RLHF), where humans rank potential outputs and a reinforcement learning algorithm rewards the model for generating outputs like those that rank highly.

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Dataconomy

MARCH 27, 2023

Natural language processing (NLP):  ML algorithms can be used to understand and interpret human language, enabling organizations to automate tasks such as customer support and document processing. Unsupervised learning:  This involves using unlabeled data to identify patterns and relationships within the data.

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