Pickl AI

MARCH 27, 2023

Well, it’s Natural Language Processing which equips the machines to work like a human. But there is much more to NLP, and in this blog, we are going to dig deeper into the key aspects of NLP, the benefits of NLP and Natural Language Processing examples. What is NLP?

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Pickl AI

MARCH 13, 2023

INTRODUCTION Machine Learning is a subfield of artificial intelligence that focuses on the development of algorithms and models that allow computers to learn and make predictions or decisions based on data, without being explicitly programmed. The algorithm learns to map the input data to the correct output based on the provided examples.

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Dataconomy

MARCH 13, 2025

These sophisticated algorithms facilitate a deeper understanding of data, enabling applications from image recognition to natural language processing. Deep learning is a subset of artificial intelligence that utilizes neural networks to process complex data and generate predictions. What is deep learning?

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Dataconomy

SEPTEMBER 22, 2023

Artificial intelligence (AI) can be used to automate and optimize the data archiving process. This process can help organizations identify which data should be archived and how it should be categorized, making it easier to search, retrieve, and manage the data. There are several ways to use AI for data archiving.

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Data Science Dojo

FEBRUARY 3, 2023

In this blog post, we’ll explore five project ideas that can help you build expertise in computer vision, natural language processing (NLP), sales forecasting, cancer detection, and predictive maintenance using Python. One project idea in this area could be to build a facial recognition system using Python and OpenCV.

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Data Science Dojo

JANUARY 30, 2024

Exploring Disease Mechanisms : Vector databases facilitate the identification of patient clusters that share similar disease progression patterns. In vec t o r d a ta b a s e s , this process of querying is more optimized and efficient with the use of a sim i l a r i ty metric for searching the most sim i l a r vec t o r to our query.

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Flipboard

DECEMBER 3, 2024

The agent uses natural language processing (NLP) to understand the query and uses underlying agronomy models to recommend optimal seed choices tailored to specific field conditions and agronomic needs. What corn hybrids do you suggest for my field?”.

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Dataconomy

MARCH 17, 2025

Through various statistical methods and machine learning algorithms, predictive modeling transforms complex datasets into understandable forecasts. They often play a crucial role in clustering and segmenting data, helping businesses identify trends without prior knowledge of the outcome.

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Data Science Dojo

AUGUST 3, 2023

It is fast, scalable, and supports a variety of machine learning algorithms. Faiss is a library for efficient similarity search and clustering of dense vectors. They are used in a variety of AI applications, such as image search, natural language processing, and recommender systems.

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Smart Data Collective

JUNE 4, 2021

Data scientists use algorithms for creating data models. Whereas in machine learning, the algorithm understands the data and creates the logic. Learning the various categories of machine learning, associated algorithms, and their performance parameters is the first step of machine learning. Clustering (Unsupervised).

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Dataconomy

FEBRUARY 27, 2025

This integration serves to elevate the efficiency and effectiveness of search processes. Advanced AI integration Natural Language Processing (NLP): Enhances the understanding of unstructured data. Machine Learning (ML) algorithms: Clustering: Identification of similar data subsets.

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Pickl AI

MAY 10, 2023

Machine Learning is a subset of Artificial Intelligence and Computer Science that makes use of data and algorithms to imitate human learning and improving accuracy. Being an important component of Data Science, the use of statistical methods are crucial in training algorithms in order to make classification. What is Classification?

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Towards AI

SEPTEMBER 5, 2024

Featured Community post from the Discord Aman_kumawat_41063 has created a GitHub repository for applying some basic ML algorithms. It offers pure NumPy implementations of fundamental machine learning algorithms for classification, clustering, preprocessing, and regression. This repo is designed for educational exploration.

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Data Science Dojo

AUGUST 30, 2023

It directly focuses on implementing scientific methods and algorithms to solve real-world business problems and is a key player in transforming raw data into significant and actionable business insights. Machine learning algorithms Machine learning forms the core of Applied Data Science.

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Data Science Dojo

MAY 10, 2024

Hence, acting as a translator it converts human language into a machine-readable form. These embeddings when particularly used for natural language processing (NLP) tasks are also referred to as LLM embeddings. Their impact on ML tasks has made them a cornerstone of AI advancements.

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Mlearning.ai

APRIL 10, 2023

How this machine learning model has become a sustainable and reliable solution for edge devices in an industrial network An Introduction Clustering (cluster analysis - CA) and classification are two important tasks that occur in our daily lives. 3 feature visual representation of a K-means Algorithm.

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AWS Machine Learning Blog

SEPTEMBER 26, 2024

During the iterative research and development phase, data scientists and researchers need to run multiple experiments with different versions of algorithms and scale to larger models. However, building large distributed training clusters is a complex and time-intensive process that requires in-depth expertise.

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Hacker News

AUGUST 5, 2024

When Meta introduced distributed GPU-based training , we decided to construct specialized data center networks tailored for these GPU clusters. We have successfully expanded our RoCE networks, evolving from prototypes to the deployment of numerous clusters, each accommodating thousands of GPUs.

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IBM Journey to AI blog

DECEMBER 20, 2023

Each type and sub-type of ML algorithm has unique benefits and capabilities that teams can leverage for different tasks. Instead of using explicit instructions for performance optimization, ML models rely on algorithms and statistical models that deploy tasks based on data patterns and inferences. What is machine learning?

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Data Science Dojo

JULY 17, 2023

It is used for machine learning, natural language processing, and computer vision tasks. TensorFlow First on the AI tool list, we have TensorFlow which is an open-source software library for numerical computation using data flow graphs. It is a cloud-based platform, so it can be accessed from anywhere.

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Dataconomy

MARCH 10, 2025

By utilizing powerful algorithms and techniques, personalization engines analyze vast amounts of data to identify patterns and trends that assist in delivering customized experiences. A/B Testing Methods: These methods allow businesses to compare different versions of content to determine which performs better in engaging users.

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Dataconomy

JUNE 29, 2023

Python machine learning packages have emerged as the go-to choice for implementing and working with machine learning algorithms. The field of machine learning, known for its algorithmic complexity, has undergone a significant transformation in recent years. Why do you need Python machine learning packages?

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Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. Evolution of NLP Models To understand the full impact of the above evolutionary process.

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AWS Machine Learning Blog

MAY 15, 2025

Amazon SageMaker HyperPod offers an effective solution for provisioning resilient clusters to run ML workloads and develop state-of-the-art models. Additionally, its modular design and integration of cutting-edge algorithms, such as FlashAttention-2 and GaLore , facilitate high performance and scalability.

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Data Science Dojo

JUNE 20, 2023

What are large language models? A large language model, referred to as an LLM, is an advanced machine learning algorithm capable of identifying, condensing, translating, predicting, and generating various forms of text and content using extensive datasets.

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Dataconomy

MAY 25, 2023

By leveraging advanced algorithms and machine learning techniques, IoT devices can analyze and interpret data in real-time, enabling them to make informed decisions and take autonomous actions. AI algorithms can uncover hidden correlations within IoT data, enabling predictive analytics and proactive actions.

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AWS Machine Learning Blog

JANUARY 30, 2023

Amazon SageMaker provides a suite of built-in algorithms , pre-trained models , and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning.

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AWS Machine Learning Blog

JANUARY 31, 2024

Embeddings play a key role in natural language processing (NLP) and machine learning (ML). Text embedding refers to the process of transforming text into numerical representations that reside in a high-dimensional vector space. There are multiple techniques to convert a sentence into a vector.

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Data Science Dojo

MAY 10, 2024

Hence, acting as a translator it converts human language into a machine-readable form. These embeddings when particularly used for natural language processing (NLP) tasks are also referred to as LLM embeddings. Their impact on ML tasks has made them a cornerstone of AI advancements.

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AWS Machine Learning Blog

NOVEMBER 25, 2024

Xin Huang is a Senior Applied Scientist for Amazon SageMaker JumpStart and Amazon SageMaker built-in algorithms. He focuses on developing scalable machine learning algorithms. He has published many papers in ACL, ICDM, KDD conferences, and Royal Statistical Society: Series A.

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Dataconomy

MARCH 13, 2023

With advances in machine learning, deep learning, and natural language processing, the possibilities of what we can create with AI are limitless. However, the process of creating AI can seem daunting to those who are unfamiliar with the technicalities involved. Train and evaluate the AI models for accuracy and efficiency.

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Pickl AI

AUGUST 3, 2023

Introduction to Deep Learning Algorithms: Deep learning algorithms are a subset of machine learning techniques that are designed to automatically learn and represent data in multiple layers of abstraction. This process is known as training, and it relies on large amounts of labeled data. How Deep Learning Algorithms Work?

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Pickl AI

JULY 17, 2023

Machine Learning is a subset of artificial intelligence (AI) that focuses on developing models and algorithms that train the machine to think and work like a human. The following blog will focus on Unsupervised Machine Learning Models focusing on the algorithms and types with examples. What is Unsupervised Machine Learning?

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Dataconomy

AUGUST 31, 2023

Natural Language Processing (NLP) : Classification can be applied to text data to categorize messages, emails, or social media posts into different categories, such as spam vs. non-spam, positive vs. negative sentiment, or topic classification. Next, you need to select a model.

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Towards AI

FEBRUARY 20, 2024

Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction Everyone is using mobile or web applications which are based on one or other machine learning algorithms. You might be using machine learning algorithms from everything you see on OTT or everything you shop online.

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Data Science Dojo

NOVEMBER 16, 2023

Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM.

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AWS Machine Learning Blog

OCTOBER 5, 2023

Our high-level training procedure is as follows: for our training environment, we use a multi-instance cluster managed by the SLURM system for distributed training and scheduling under the NeMo framework. Xin Huang is a Senior Applied Scientist for Amazon SageMaker JumpStart and Amazon SageMaker built-in algorithms.

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