Explosion

APRIL 1, 2017

Prodigy features many of the ideas and solutions for data collection and supervised learning outlined in this blog post. It’s a cloud-free, downloadable tool and comes with powerful active learning models. Transfer learning and better annotation tooling are both key to our current plans for spaCy and related projects.

Supervised Learning 40

Supervised Learning Machine Learning Machine Learning Algorithm 40

AssemblyAI

MAY 17, 2023

The below animation demonstrates this process: 0:00 / 1× Since the Language Model is, by definition, a probability distribution over word sequences, we generate text by simply recursively asking for the most likely next word given all of our previous words. Yes, it really is that simple. Can we do better?

Supervised Learning 119

Supervised Learning AI AI Machine Learning 119

Explosion

MAY 17, 2023

That’s definitely new. 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.

Supervised Learning 135

Supervised Learning Natural Language Processing Clustering Machine Learning 135

Towards AI

FEBRUARY 20, 2024

I am starting a series with this blog, which will guide a beginner to get the hang of the ‘Machine learning world’. Photo by Andrea De Santis on Unsplash So, What is Machine Learning? Definition says, machine learning is the ability of computers to learn without explicit programming.

Machine Learning 109

Machine Learning Machine Learning ML ML 109

Applied Data Science

MARCH 11, 2022

Our internal agents are playing games until they learn how to cooperate and trick us into believing we are an individual. Gamification There are many definitions for what a game is. Techniques developed in NLP, such as the Transformer architecture, are useful in very diverse fields such as computer vision and reinforcement learning.

AI 130

AI AI Data Science Machine Learning 130

Data Science Blog

MARCH 14, 2023

2 Denn heute spielt die Definition darüber, was Big Data eigentlich genau ist, wirklich keine Rolle mehr. GPT-3 wurde mit mehr als 100 Milliarden Wörter trainiert, das parametrisierte Machine Learning Modell selbst wiegt 800 GB (quasi nur die Neuronen!) Neben Supervised Learning kam auch Reinforcement Learning zum Einsatz.

Big Data 147

Big Data Big Data Apache Hadoop Data Science 147

Towards AI

FEBRUARY 15, 2024

I definitely recommend watching this one for all learners out here! Ramcharan12345 is looking to collaborate with AI devs who can leverage spaCy for NLP, utilize scikit-learn for supervised learning on historical data for symptom mapping, and implement TensorFlow/Keras for neural network-based risk prediction.

AI 105

AI AI Supervised Learning Analytics 105

Snorkel AI

APRIL 18, 2023

Typically, you let the experts read some articles, label them, and then use them as training data and train the supervised learning model. To address all these problems, we looked into weak supervised learning. Once we label a fraction of documents, we use that as training data to train the supervised learning model.

Supervised Learning 52

Supervised Learning Clustering AI AI 52

Snorkel AI

APRIL 18, 2023

Typically, you let the experts read some articles, label them, and then use them as training data and train the supervised learning model. To address all these problems, we looked into weak supervised learning. Once we label a fraction of documents, we use that as training data to train the supervised learning model.

Supervised Learning 52

Supervised Learning Clustering AI AI 52

Dataconomy

MARCH 27, 2023

Definition and purpose of RPA Robotic process automation refers to the use of software robots to automate rule-based business processes. Unsupervised learning:  This involves using unlabeled data to identify patterns and relationships within the data. Technology: Includes a range of technologies, including ML and deep learning.

ML 133

ML ML Machine Learning Machine Learning 133

Data Science 101

OCTOBER 31, 2019

A definition from the book ‘Data Mining: Practical Machine Learning Tools and Techniques’, written by, Ian Witten and Eibe Frank describes Data mining as follows: “ Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Classification. Regression.

Data Mining 77

Data Mining Data Mining Data Mining Data Science 77

Chatbots Life

MAY 12, 2023

Such models can also learn from a set of few examples The process of presenting a few examples is also called In-Context Learning , and it has been demonstrated that the process behaves similarly to supervised learning. Although the model acts as a highly-skilled, the profession definitely carries a lot of risks.

AI 90

AI AI ML ML 90

Snorkel AI

JULY 5, 2023

A large percentage of ML projects are based on supervised learning, which is very dependent on good feature selection. But then, Robert, what do you think are some of the challenges applied folks in the supervised learning space face when trying to productionize these use cases? That actually brings us to a good point.

Supervised Learning 52

Supervised Learning AI AI ML 52

Snorkel AI

JULY 5, 2023

A large percentage of ML projects are based on supervised learning, which is very dependent on good feature selection. But then, Robert, what do you think are some of the challenges applied folks in the supervised learning space face when trying to productionize these use cases? That actually brings us to a good point.

AI 52

AI AI Supervised Learning ML 52

Snorkel AI

JULY 5, 2023

A large percentage of ML projects are based on supervised learning, which is very dependent on good feature selection. But then, Robert, what do you think are some of the challenges applied folks in the supervised learning space face when trying to productionize these use cases? That actually brings us to a good point.

AI 52

AI AI Supervised Learning ML 52

Pickl AI

NOVEMBER 18, 2024

Key Takeaways Machine Learning Models are vital for modern technology applications. Types include supervised, unsupervised, and reinforcement learning. Key steps involve problem definition, data preparation, and algorithm selection. Ethical considerations are crucial in developing fair Machine Learning solutions.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Algorithm 52

Pickl AI

JANUARY 2, 2025

Introduced by Claude Shannon in 1948, entropy revolutionised how we measure information and remains central to modern Data Science, including Machine Learning. Lets delve into its mathematical definition and key properties. Lets explore its definition, connection to entropy, and practical applications.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Clustering 52

Explosion

AUGUST 3, 2017

You’ll collect more user actions, giving you lots of smaller pieces to learn from, and a much tighter feedback loop between the human and the model. By definition, you can’t directly control what the process returns. Sometimes the meaning representation will directly address a useful question.

Supervised Learning 52

Supervised Learning Python Machine Learning Machine Learning 52

DagsHub

JANUARY 6, 2025

So, here is a list of components in an image labeling guideline:  Clear Definitions for Each Label/Class: A good guideline should provide a precise definition of each label or class to be assigned to an image. In this technique, a model is trained on an initial labeled dataset.

Machine Learning 81

Machine Learning Machine Learning Data Quality Supervised Learning 81

Pickl AI

JULY 12, 2024

This section delves into its foundational definitions, types, and critical concepts crucial for comprehending its vast landscape. Machine Learning algorithms are trained on large amounts of data, and they can then use that data to make predictions or decisions about new data.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

OCTOBER 24, 2024

Summary: This article compares Artificial Intelligence (AI) vs Machine Learning (ML), clarifying their definitions, applications, and key differences. While AI aims to replicate human intelligence across various domains, ML focuses on learning from data to improve performance.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Machine Learning Machine Learning 52

Dataconomy

MARCH 27, 2023

Definition and purpose of RPA Robotic process automation refers to the use of software robots to automate rule-based business processes. Unsupervised learning:  This involves using unlabeled data to identify patterns and relationships within the data. Technology: Includes a range of technologies, including ML and deep learning.

ML 70

ML ML Machine Learning Machine Learning 70

IBM Journey to AI blog

SEPTEMBER 20, 2023

They use self-supervised learning algorithms to perform a variety of natural language processing (NLP) tasks in ways that are similar to how humans use language (see Figure 1). Large language models (LLMs) have taken the field of AI by storm.

Clustering 95

Clustering AI AI Data Science 95

ODSC - Open Data Science

AUGUST 30, 2023

So domain-specific LLMs can assist newcomers by providing explanations, definitions, and context, reducing the learning curve. Often jargon and other contextual pieces of information don’t translate well when making the move to a new industry. The same can also be said with popular tools.

Data Science 52

Data Science Supervised Learning AI AI 52

Defined.ai blog

SEPTEMBER 14, 2022

Currently, most models are trained via supervised learning, which relies on well-annotated data from humans to create training examples. Data annotation is especially important when considering the amount of unstructured data that exists in the form of text, images, video, and audio.

Machine Learning 52

Machine Learning Machine Learning Supervised Learning Data Preparation 52

Pickl AI

SEPTEMBER 17, 2024

Key Terms and Definitions To fully grasp the concepts of dimensionality reduction, it’s essential to understand the key terms and definitions associated with this field. We aim to retain the essential information by reducing dimensions while discarding less relevant details.

Machine Learning 52

Machine Learning Machine Learning Data Analysis Data Analysis 52

PyImageSearch

FEBRUARY 6, 2023

Data Analysis When working with data, especially supervised learning, it is often a best practice to check data imbalance. If you haven’t coded an image classification network before, the section is definitely for you! Figure 2: Analyzing a few categories from the vegetable image classification dataset.

Deep Learning 137

Deep Learning Deep Learning Python Data Analysis 137

Mlearning.ai

APRIL 16, 2023

There are various types of regressions used in data science and machine learning. Figure 1: Types of regression (own graphic) Definition of Simple Linear Regression The dependent variable is continuous. Conclusion This article described regression which is a supervising learning approach.

Machine Learning 52

Machine Learning Machine Learning Python Data Science 52

Snorkel AI

FEBRUARY 17, 2023

How do you train machine learning algorithms generally for any data set? Then we generalized that for the entire field of supervised learning. They’re borderline out of distribution, but they’re definitely errors that shouldn’t be in the dataset. And for any model for binary classification?

Machine Learning 52

Machine Learning Machine Learning AI AI 52

Snorkel AI

FEBRUARY 17, 2023

How do you train machine learning algorithms generally for any data set? Then we generalized that for the entire field of supervised learning. They’re borderline out of distribution, but they’re definitely errors that shouldn’t be in the dataset. And for any model for binary classification?

Machine Learning 52

Machine Learning Machine Learning AI AI 52

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.

Database 124

Database AI AI Natural Language Processing 124

The MLOps Blog

JANUARY 5, 2023

For instance, something from a factory maybe some uses case where there’s a limited amount of data, then I think the current approach, and especially in the growing field of self-supervised learning, is very helpful here. You can actually improve embeddings and train embeddings in a self-supervised way. Any thoughts?

Machine Learning 52

Machine Learning Machine Learning Database ML 52

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. Exo-feedback is by definition difficult for a designer to predict.

Machine Learning 54

Machine Learning Machine Learning Supervised Learning ML 54

Pickl AI

OCTOBER 18, 2024

Complexity in Goal Definition: Requires domain knowledge for accurate goal setting. These agents can be reactive, deliberative, or learning-based, enabling them to perform tasks autonomously, make decisions, and adapt to changing conditions in various applications. How Do AI Agents Learn?

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence AI AI 52

Pickl AI

FEBRUARY 22, 2024

Students learn Maximum Likelihood Estimation, the three M’s of Statistics (Mean, Median, Mode), and critical topics like Central Limit Theorem, Confidence Intervals, Hypothesis Testing, and Linear Regression. The course may be helpful for engineering students who want to learn Statistical methods well.

Data Science 52

Data Science Hypothesis Testing Data Analysis Data Analysis 52

Mlearning.ai

NOVEMBER 29, 2023

The downside of overly time-consuming supervised learning, however, remains. Classic Methods of Time Series Forecasting Multi-Layer Perceptron (MLP) Univariate models can be used to model univariate time series prediction machine learning problems. In its core, lie gradient-boosted decision trees.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

DagsHub

JANUARY 27, 2025

Unsupervised learning has shown a big potential in large language models but high-quality labelled data remains the gold standard for AI systems to be accurate and aligned with human language and understanding. Text labeling has enabled all sorts of frameworks and strategies in machine learning.

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing Supervised Learning 52