Data Science Dojo

DECEMBER 8, 2023

Zero-shot, one-shot, and few-shot learning are redefining how machines adapt and learn, promising a future where adaptability and generalization reach unprecedented levels. Source: Photo by Hal Gatewood on Unsplash In this exploration, we navigate from the basics of supervised learning to the forefront of adaptive models.

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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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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. Inspired by human brain structure, they are designed to perform as powerful tools for pattern recognition, classification, and prediction tasks.

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Dataconomy

MAY 29, 2023

In their quest for effectiveness and well-informed decision-making, businesses continually search for new ways to collect information. QR codes can contain a huge amount of information, such as text, URLs, contact details, and more. In the realm of AI and ML, QR codes find diverse applications across various domains.

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Pickl AI

OCTOBER 21, 2024

Summary: Zero-Shot Learning (ZSL) empowers AI systems to recognize and classify new categories without needing labelled examples. By leveraging auxiliary information such as semantic attributes, ZSL enhances scalability, reduces data dependency, and improves generalisation. Poorly defined attributes may lead to inaccurate predictions.

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Dataconomy

AUGUST 31, 2023

The classification model learns from the training data, identifying the distinguishing characteristics between each class, enabling it to make informed predictions. Classification in machine learning can be a versatile tool with numerous applications across various industries.

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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. Moreover, the model training process is capable of adapting to new languages and data effectively.

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Data Science Dojo

AUGUST 19, 2024

These professionals venture into new frontiers like machine learning, natural language processing, and computer vision, continually pushing the limits of AI’s potential. Supervised learning: This involves training a model on a labeled dataset, where each data point has a corresponding output or target variable.

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Dataconomy

APRIL 18, 2023

Understanding the basics of artificial intelligence Artificial intelligence is an interdisciplinary field of study that involves creating intelligent machines that can perform tasks that typically require human-like cognitive abilities such as learning, reasoning, and problem-solving.

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Becoming Human

MAY 9, 2024

Artificial intelligence, machine learning, natural language processing, and other related technologies are paving the way for a smarter “everything.” NLP in finance includes semantic analysis, information extraction, and text analysis. How Does Data Labeling Work in Finance?

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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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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. Achieving higher performance is often challenging, as the context window restricts the model’s ability to process long sequences of information effectively.

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Dataconomy

APRIL 3, 2023

ML models are designed to learn from data and make predictions or decisions based on that data. Types of ML There are three main types of machine learning: Supervised learning: In supervised learning, the algorithm is trained on labeled data.

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Dataconomy

APRIL 3, 2023

ML models are designed to learn from data and make predictions or decisions based on that data. Types of ML There are three main types of machine learning: Supervised learning: In supervised learning, the algorithm is trained on labeled data.

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Pickl AI

SEPTEMBER 16, 2024

This section will explore the top 10 Machine Learning algorithms that you should know in 2024. Linear Regression Linear regression is one of the simplest and most widely used algorithms in Machine Learning. It is a supervised learning algorithm that predicts a continuous target variable based on one or more predictor variables.

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Towards AI

JULY 23, 2023

In the first part of the series, we talked about how Transformer ended the sequence-to-sequence modeling era of Natural Language Processing and understanding. Semi-Supervised Sequence Learning As we all know, supervised learning has a drawback, as it requires a huge labeled dataset to train.

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Women in Big Data

DECEMBER 13, 2023

The Bay Area Chapter of Women in Big Data (WiBD) hosted its second successful episode on the NLP (Natural Language Processing), Tools, Technologies and Career opportunities. Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that helps computers understand, interpret and manipulate human language.

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Pickl AI

JULY 12, 2024

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. There are three main types of Machine Learning: supervised learning, unsupervised learning, and reinforcement learning.

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AWS Machine Learning Blog

JULY 18, 2023

It’s important to take extra precautions to protect your device and sensitive information. As technology is improving, the detection of spam emails becomes a challenging task due to its changing nature. Text classification is essential for applications like web searches, information retrieval, ranking, and document classification.

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Pickl AI

OCTOBER 10, 2024

Foundation Models Foundation models are large-scale Machine Learning models pre-trained on vast datasets using self-supervised learning techniques. Example Google’s Bard utilizes the Pathways Language Model (PaLM) to enhance conversational capabilities. How do Large Language Models Work?

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Becoming Human

MAY 11, 2023

Empowering Startups and Entrepreneurs | InvestBegin.com | investbegin The success of ChatGPT can be attributed to several key factors, including advancements in machine learning, natural language processing, and big data. NLP is a field of AI that focuses on enabling computers to understand and process human language.

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Towards AI

FEBRUARY 20, 2024

Types of Machine Learning There are three main categories of Machine Learning, Supervised learning, Unsupervised learning, and Reinforcement learning. Supervised learning: This involves learning from labeled data, where each data point has a known outcome. Models […]

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How to Learn Machine Learning

MAY 14, 2023

Acquiring Essential Machine Learning Knowledge Once you have a strong foundation in mathematics and programming, it’s time to dive into the world of machine learning. Additionally, you should familiarize yourself with essential machine learning concepts such as feature engineering, model evaluation, and hyperparameter tuning.

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Pickl AI

MAY 29, 2024

Multi-Task Learning Deep Learning is a towering pillar in the vast landscape of artificial intelligence, revolutionising various domains with remarkable capabilities. Deep Learning algorithms have become integral to modern technology, from image recognition to Natural Language Processing.

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Pickl AI

SEPTEMBER 3, 2024

The Importance of Data Annotation It is essential in the realm of Artificial Intelligence and Machine Learning. It lays the groundwork for training models, ensuring accuracy, and facilitating supervised learning. By providing context and structure, annotated data enables machines to learn effectively and make informed decisions.

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Pickl AI

SEPTEMBER 16, 2024

DBNs learn to represent data by modelling complex distributions through a hierarchical structure. They extract features from data by capturing higher-level abstractions, making them suitable for complex tasks like image recognition and natural language processing. However, their approach differs.

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ODSC - Open Data Science

AUGUST 29, 2023

Natural Language Processing Engineer Natural Language Processing Engineers who specialize in prompt engineering are linguistic architects when it comes to AI communication. As AI models become more sophisticated and versatile, the demand for tailored, context-aware interactions grows.

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Pickl AI

OCTOBER 2, 2024

This analysis involves various processes such as sequencing, mapping, and interpreting genetic information to understand biological functions, disease mechanisms, and evolutionary relationships. Key Components of Genomic Analysis DNA Sequencing: The process of determining the exact sequence of nucleotides in a DNA molecule.

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Pickl AI

OCTOBER 24, 2024

These components, including reasoning, learning, and problem-solving, work together to enable machines to mimic human cognitive abilities, improving their capacity to think and act independently. Reasoning : AI systems analyse data and draw logical conclusions, helping them make informed decisions.

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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 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

NOVEMBER 18, 2024

The main types are supervised, unsupervised, and reinforcement learning, each with its techniques and applications. Supervised Learning In Supervised Learning , the algorithm learns from labelled data, where the input data is paired with the correct output. spam email detection) and regression (e.g.,

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Towards AI

JANUARY 30, 2024

GPT4, Stable Diffusion, Llama, BERT, Gemini Large Language Models (LLMs) Foundation models, trained on the “Transformer Architecture”, that can perform a wide array of Natural Language Processing (NLP) tasks like text generation, classification, summarisation etc. LLMs don’t claim to reproduce accurate information.

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Pickl AI

JULY 17, 2023

Unsupervised Learning is a Machine Learning technique where the users do not require to supervise the model. Significantly, the technique allows the model to work independently by discovering its patterns and previously undetected information. Therefore, it mainly deals with unlabelled data.

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Pickl AI

AUGUST 7, 2023

Data Labelling is the process of adding meaning to different datasets ensuring that it can be used properly to train a Machine Learning model. Labeled data in Machine Learning is typically used in the case of Supervised Learning where the labeled data is input to a model. How does Data Labelling Work?

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Pickl AI

NOVEMBER 13, 2024

Introduction Inspired by the human brain, neural networks are at the core of modern Artificial Intelligence , driving breakthroughs in image recognition, natural language processing, and more. This process ensures that networks learn from data and improve over time.

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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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