Data Science Dojo

JUNE 11, 2024

These include image recognition, natural language processing, autonomous vehicles, financial services, healthcare, recommender systems, gaming and entertainment, and speech recognition. They are capable of learning and improving over time as they are exposed to more data.

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Pickl AI

NOVEMBER 24, 2024

These powerful neural networks learn to compress data into smaller representations and then reconstruct it back to its original form. In this blog, we will explore what autoencoders are, how they work, their various types, and real-world applications. Can I Use Autoencoders for Supervised Learning Tasks?

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IBM Journey to AI blog

DECEMBER 20, 2023

And retailers frequently leverage data from chatbots and virtual assistants, in concert with ML and natural language processing (NLP) technology, to automate users’ shopping experiences. Semi-supervised learning The fifth type of machine learning technique offers a combination between supervised and unsupervised learning.

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Dataversity

DECEMBER 16, 2022

2022 was a big year for AI, and we’ve seen significant advancements in various areas – including natural language processing (NLP), machine learning (ML), and deep learning. Unsupervised and self-supervised learning are making ML more accessible by lowering the training data requirements.

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Dataconomy

MARCH 14, 2023

In recent years, natural language processing and conversational AI have gained significant attention as technologies that are transforming the way we interact with machines and each other. It is a significant initial step towards the objective of supporting 1,000 languages.

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Dataconomy

AUGUST 21, 2023

At its core, a Large Language Model (LLM) is a sophisticated machine learning entity adept at executing a myriad of natural language processing (NLP) activities. This includes tasks like text generation, classification, engaging in dialogue, and even translating text across languages. What is LLM in AI?

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Towards AI

FEBRUARY 20, 2024

Machine learning(ML) is evolving at a very fast pace. 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? The computer model analyses different features with the label.

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Pickl AI

NOVEMBER 13, 2024

The answer lies in the various types of Machine Learning, each with its unique approach and application. In this blog, we will explore the four primary types of Machine Learning: Supervised Learning, UnSupervised Learning, semi-Supervised Learning, and Reinforcement Learning.

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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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AWS Machine Learning Blog

JULY 18, 2023

Word2vec is useful for various natural language processing (NLP) tasks, such as sentiment analysis, named entity recognition, and machine translation. Set the learning mode hyperparameter to supervised. BlazingText has both unsupervised and supervised learning modes. Start training the model.

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Towards AI

JANUARY 30, 2024

While I post the list in this blog, I’m also maintaining a “live” list that is detailed and will keep on updating. Supervised Fine Tuning Fine Tuning is a supervised learning process, where you take a labelled dataset of prompt-completion pairs to adjust the weights of an LLM. Examples: GPT 3.5,

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DrivenData Labs

FEBRUARY 3, 2025

This blog post discusses circumstances of youth suicide, which can be upsetting and difficult to discuss. His research focuses on applying natural language processing techniques to extract information from unstructured clinical and medical texts, especially in low-resource settings.

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Explosion

MAY 17, 2023

A lot of people are building truly new things with Large Language Models (LLMs), like wild interactive fiction experiences that weren’t possible before. But if you’re working on the same sort of Natural Language Processing (NLP) problems that businesses have been trying to solve for a long time, what’s the best way to use them?

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Mlearning.ai

AUGUST 4, 2023

Fine-tuning large language models (LLMs) has become a powerful technique for achieving impressive performance in various natural language processing tasks. Additionally, few-shot learning requires maintaining a large context window, which can be resource-intensive and impractical for memory-constrained environments.

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Hacker News

MARCH 25, 2022

Transformers made self-supervised learning possible, and AI jumped to warp speed,” said NVIDIA founder and CEO Jensen Huang in his keynote address this week at GTC. Transformers are in many cases replacing convolutional and recurrent neural networks (CNNs and RNNs), the most popular types of deep learning models just five years ago.

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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. This process results in generalized models capable of a wide variety of tasks, such as image classification, natural language processing, and question-answering, with remarkable accuracy.

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Pickl AI

AUGUST 14, 2024

They consist of interconnected nodes that learn complex patterns in data. Different types of neural networks, such as feedforward, convolutional, and recurrent networks, are designed for specific tasks like image recognition, Natural Language Processing, and sequence modelling.

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IBM Journey to AI blog

JUNE 14, 2023

With a foundation model, often using a kind of neural network called a “transformer” and leveraging a technique called self-supervised learning, you can create pre-trained models for a vast amount of unlabeled data. But that’s all changing thanks to pre-trained, open source foundation models.

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Pickl AI

SEPTEMBER 16, 2024

Summary: This blog highlights ten crucial Machine Learning algorithms to know in 2024, including linear regression, decision trees, and reinforcement learning. Introduction Machine Learning (ML) has rapidly evolved over the past few years, becoming an integral part of various industries, from healthcare to finance.

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Pickl AI

OCTOBER 21, 2024

This innovative approach is transforming applications in computer vision, Natural Language Processing, healthcare, and more. Introduction Zero-Shot Learning (ZSL) is revolutionising Artificial Intelligence by enabling models to classify new categories without prior training data.

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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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Pickl AI

AUGUST 1, 2023

As technology continues to impact how machines operate, Machine Learning has emerged as a powerful tool enabling computers to learn and improve from experience without explicit programming. In this blog, we will delve into the fundamental concepts of data model for Machine Learning, exploring their types.

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Pickl AI

OCTOBER 10, 2024

Summary: This blog delves into five prominent AI models: foundation models, Large Language Models, multimodal models, diffusion models, and generative adversarial networks. This blog explores five prominent AI models, detailing their functions, applications, and real-world examples. How do Large Language Models Work?

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Pickl AI

SEPTEMBER 17, 2024

Summary: This blog covers 15 crucial artificial intelligence interview questions, ranging from fundamental concepts to advanced techniques. In this blog post, we will explore 15 essential artificial intelligence interview questions that cover a range of topics, from fundamental principles to cutting-edge techniques.

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IBM Journey to AI blog

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. This blog post will clarify some of the ambiguity. Learn more about watsonx.ai

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IBM Journey to AI blog

SEPTEMBER 20, 2023

Large language models (LLMs) are a class of foundational models (FM) that consist of layers of neural networks that have been trained on these massive amounts of unlabeled data. Large language models (LLMs) have taken the field of AI by storm.

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AWS Machine Learning Blog

MAY 8, 2024

This supervised learning algorithm supports transfer learning for all pre-trained models available on Hugging Face. He has experience in working on a diverse range of machine learning problems within the domain of natural language processing, computer vision, and time series analysis.

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Mlearning.ai

APRIL 21, 2023

Text annotation Text annotation is the process of adding structured information to unstructured text data in order to make it more understandable and useful for downstream applications. The resulting annotated text data can then be used to train and improve the accuracy of LLM models for specific natural language processing tasks.

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Pickl AI

SEPTEMBER 16, 2024

This blog will delve into deep belief network examples, highlighting their role in feature extraction and dimensionality reduction. DBNs learn to represent data by modelling complex distributions through a hierarchical structure. The network learns from labelled data during fine-tuning to minimise the prediction error.

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Flipboard

DECEMBER 6, 2023

Large language models A large language model refers to any model that undergoes training on extensive and diverse datasets, typically through self-supervised learning at a large scale, and is capable of being fine-tuned to suit a wide array of specific downstream tasks.

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Pickl AI

AUGUST 7, 2023

They require human expertise to tell a machine about the different data types to help Artificial Intelligence learn it. Both labeled and unlabeled data are used in Machine Learning for different purposes. Read the blog below to learn more about how data labeling works and gain an understanding of the use cases.

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IBM Journey to AI blog

AUGUST 1, 2023

Foundation models can be trained to perform tasks such as data classification, the identification of objects within images (computer vision) and natural language processing (NLP) (understanding and generating text) with a high degree of accuracy. The platform comprises three powerful products: The watsonx.ai

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Pickl AI

JULY 17, 2023

There are two types of Machine Learning techniques, including supervised and unsupervised learning. The following blog will focus on Unsupervised Machine Learning Models focusing on the algorithms and types with examples. Unsupervised learning is preferable as it is easier to get unlabeled data than labeled data.

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AWS Machine Learning Blog

OCTOBER 4, 2023

Train an ML model on the preprocessed images, using a supervised learning approach to teach the model to distinguish between different skin types. Jake Wen is a Solutions Architect at AWS, driven by a passion for Machine Learning, Natural Language Processing, and Deep Learning.

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Pickl AI

SEPTEMBER 3, 2024

Summary: Data annotation is crucial for training Machine Learning models by adding meaningful labels to raw data. This blog covers the importance, types, techniques, best practices, and challenges, highlighting its role in enhancing AI accuracy and decision-making across various industries.

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Dataconomy

APRIL 4, 2023

Reminder : Training data refers to the data used to train an AI model, and commonly there are three techniques for it: Supervised learning: The AI model learns from labeled data, which means that each data point has a known output or target value. Let’s take a closer look at their purposes briefly.

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Dataconomy

APRIL 4, 2023

Reminder : Training data refers to the data used to train an AI model, and commonly there are three techniques for it: Supervised learning: The AI model learns from labeled data, which means that each data point has a known output or target value. Let’s take a closer look at their purposes briefly.

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