ODSC - Open Data Science

NOVEMBER 28, 2024

Calculating Receptive Field for Convolutional Neural Networks Convolutional neural networks (CNNs) differ from conventional, fully connected neural networks (FCNNs) because they process information in distinct ways. Receptive fields are the backbone of CNN efficacy.

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Marktechpost

NOVEMBER 27, 2024

At its core, machine learning algorithms seek to identify patterns within data, enabling computers to learn and adapt to new information. 2) Logistic regression Logistic regression is a classification algorithm used to model the probability of a binary outcome. Think about how a child learns to recognize a cat. game playing, robotics).

Machine Learning 113

Machine Learning Algorithm Neural Network Convolutional Neural Networks 113

Marktechpost

JUNE 3, 2024

This article lists top Intel AI courses, including those on deep learning, NLP, time-series analysis, anomaly detection, robotics, and edge AI deployment, providing a comprehensive learning path for leveraging Intel’s AI technologies.

Convolutional Neural Networks 122

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ODSC - Open Data Science

FEBRUARY 10, 2025

adults use only work when they can turn audio data into words, and then apply natural language processing (NLP) to understand it. Mono sound channels are the best option for speech intelligibility , so theyre ideal for NLP applications, but stereo inputs will improve copyright detection use cases. The voice assistants that 62% of U.S.

Machine Learning 52

Machine Learning Convolutional Neural Networks Neural Network Natural Language Processing 52

Marktechpost

APRIL 12, 2024

This article explores some of the most influential deep learning architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Transformers, and Encoder-Decoder architectures, highlighting their unique features, applications, and how they compare against each other.

Deep Learning 137

Deep Learning Convolutional Neural Networks Neural Network Natural Language Processing 137

Pickl AI

JANUARY 29, 2025

Whether you’re interested in image recognition, natural language processing, or even creating a dating app algorithm, theres a project here for everyone. Cat vs. Dog Classification This project involves building a Convolutional Neural Network (CNN) to classify images as either cats or dogs.

Deep Learning 52

Deep Learning Neural Network Natural Language Processing Computer Vision 52

Pickl AI

APRIL 6, 2025

Application Differences : Neural Networks for simple tasks, Deep Learning for complex ones. AI Capabilities : Enables image recognition, NLP, and predictive analytics. Neural Networks: The Foundation A neural network is a computing system inspired by the biological neural networks that constitute animal brains.

Neural Network 52

Neural Network Deep Learning Natural Language Processing Convolutional Neural Networks 52

Unite.AI

OCTOBER 22, 2024

This term refers to how much time, memory, or processing power an algorithm requires as the size of the input grows. AI models like neural networks , used in applications like Natural Language Processing (NLP) and computer vision , are notorious for their high computational demands.

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Neural Network Convolutional Neural Networks Deep Learning Natural Language Processing 147

Viso.ai

OCTOBER 9, 2024

Human-machine interaction is an important area of research where machine learning algorithms with visual perception aim to gain an understanding of human interaction. State-of-the-art emotion AI Algorithms Outlook, current research, and applications What Is AI Emotion Recognition? About us: Viso.ai What is Emotion AI?

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Convolutional Neural Networks Neural Network Computer Vision Emotion AI 98

IBM Journey to AI blog

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data. What is text mining?

Data Mining 170

Data Mining Convolutional Neural Networks Categorization Machine Learning 170

Artificial Corner

JANUARY 23, 2025

Subscribe now #3 Natural Language Processing Course in Python This is a short yet useful 2-hour NLP course for anyone interested in the field of Natural Language Processing. NLP is a branch of artificial intelligence that allows machines to understand human language. It makes machine learning model building easy for beginners.

Data Science 95

Data Science Python Convolutional Neural Networks Natural Language Processing 95

Marktechpost

JUNE 3, 2024

Numerous groundbreaking models—including ChatGPT, Bard, LLaMa, AlphaFold2, and Dall-E 2—have surfaced in different domains since the Transformer’s inception in Natural Language Processing (NLP). Iterative improvement algorithms and stochastic algorithms are the two main categories under which heuristic algorithms fall.

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ODSC - Open Data Science

FEBRUARY 13, 2023

The field of data science changes constantly, and some frameworks, tools, and algorithms just can’t get the job done anymore. We will take a gentle, detailed tour through a multilayer fully-connected neural network, backpropagation, and a convolutional neural network.

Neural Network 98

Neural Network Convolutional Neural Networks Machine Learning Deep Learning 98

The MLOps Blog

MARCH 6, 2025

While transformer-based models are in the limelight of the NLP community, a quiet revolution in sequence modeling is underway. Around 2020, their ability to efficiently handle long sequences spurred significant progress in adapting them for natural language processing (NLP).

Neural Network 59

Neural Network Convolutional Neural Networks Natural Language Processing NLP 59

John Snow Labs

APRIL 30, 2024

In this article, we will explore the significance of table extraction and demonstrate the application of John Snow Labs’ NLP library with visual features installed for this purpose. We will delve into the key components within the John Snow Labs NLP pipeline that facilitate table extraction. How does Visual NLP come into action?

Deep Learning 52

Deep Learning NLP Convolutional Neural Networks Data Extraction 52

Heartbeat

NOVEMBER 21, 2023

From object detection and recognition to natural language processing, deep reinforcement learning, and generative models, we will explore how deep learning algorithms have conquered one computer vision challenge after another. One of the most significant breakthroughs in this field is the convolutional neural network (CNN).

Computer Vision 52

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NLP People

JULY 24, 2019

We took the opportunity to review major research trends in the animated NLP space and formulate some implications from the business perspective. The article is backed by a statistical and – guess what – NLP-based analysis of ACL papers from the last 20 years. But what is the substance behind the buzz?

NLP 52

NLP Neural Network Deep Learning Natural Language Processing 52

Topbots

OCTOBER 4, 2023

Generated with Bing and edited with Photoshop Predictive AI has been driving companies’ ROI for decades through advanced recommendation algorithms, risk assessment models, and fraud detection tools. spam vs. not spam), while generative NLP models can create new text based on a given prompt (e.g., whether a customer will churn).

Generative AI 119

Generative AI Natural Language Processing Machine Learning Convolutional Neural Networks 119

Unite.AI

MARCH 26, 2024

The development of Large Language Models (LLMs) built from decoder-only transformer models has played a crucial role in transforming the Natural Language Processing (NLP) domain, as well as advancing diverse deep learning applications including reinforcement learning , time-series analysis, image processing, and much more.

Natural Language Processing 147

Natural Language Processing Neural Network Convolutional Neural Networks Large Language Models 147

Becoming Human

MARCH 16, 2023

This post includes the fundamentals of graphs, combining graphs and deep learning, and an overview of Graph Neural Networks and their applications. Through the next series of this post here , I will try to make an implementation of Graph Convolutional Neural Network. So, let’s get started! What are Graphs?

Neural Network 87

Neural Network Auto-classification Convolutional Neural Networks Auto-complete 87

Sebastian Ruder

JANUARY 19, 2021

The selection of areas and methods is heavily influenced by my own interests; the selected topics are biased towards representation and transfer learning and towards natural language processing (NLP). This is less of a problem in NLP where unsupervised pre-training involves classification over thousands of word types.

NLP 52

NLP ML Computer Vision Natural Language Processing 52

Viso.ai

DECEMBER 20, 2023

Supervised learning is a type of machine learning algorithm that learns from a set of training data that has been labeled training data. Typical computer vision tasks of supervised learning algorithms include object detection, visual recognition, and classification. from a set of images. to an image. for image data compression).

Computer Vision 98

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AWS Machine Learning Blog

FEBRUARY 24, 2023

This satisfies the strong MME demand for deep neural network (DNN) models that benefit from accelerated compute with GPUs. These include computer vision (CV), natural language processing (NLP), and generative AI models. There are two notebooks provided in the repo: one for load testing CV models and another for NLP.

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Explosion

AUGUST 22, 2017

A lot of work has gone into designing optimisation algorithms that are less sensitive to initialisation. import spacy nlp = spacy.load("en_core_web_sm") doc = nlp(u"search for pictures of playful rodents") spacy.displacy.serve(doc) This parse is wrong – it’s analysed “search” as a noun, where it should be a verb.

NLP 52

NLP Convolutional Neural Networks Neural Network Natural Language Processing 52

John Snow Labs

JUNE 2, 2023

Spark NLP’s deep learning models have achieved state-of-the-art results on sentiment analysis tasks, thanks to their ability to automatically learn features and representations from raw text data. Spark NLP has multiple approaches for detecting the sentiment (which is actually a text classification problem) in a text.

Deep Learning 76

Deep Learning NLP Convolutional Neural Networks Neural Network 76

Marktechpost

DECEMBER 12, 2024

The early 2000s witnessed a resurgence, fueled by advancements in hardware like GPUs, innovative algorithms such as ReLU activation and dropout, and the availability of massive datasets. They utilize convolutional layers to extract spatial features by applying filters to the input data.

Neural Network 57

Neural Network Convolutional Neural Networks Natural Language Processing Artificial Intelligence 57

Pickl AI

AUGUST 14, 2024

Neural networks come in various forms, each designed for specific tasks: Feedforward Neural Networks (FNNs) : The simplest type, where connections between nodes do not form cycles. This capability makes them particularly effective for tasks such as image and speech recognition, where traditional algorithms may struggle.

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

Pickl AI

APRIL 9, 2025

Language Understanding: Processing and interpreting human language (Natural Language Processing – NLP). A simple example could be an early chess-playing program that evaluated moves based on predefined rules and search algorithms. Perception: Understanding the world through sensory input (like vision or sound).

Deep Learning 52

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Viso.ai

DECEMBER 21, 2023

From the development of sophisticated object detection algorithms to the rise of convolutional neural networks (CNNs) for image classification to innovations in facial recognition technology, applications of computer vision are transforming entire industries.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

Pickl AI

SEPTEMBER 16, 2024

Summary : Deep Belief Networks (DBNs) are Deep Learning models that use Restricted Boltzmann Machines and feedforward networks to learn hierarchical features and model complex data distributions. They are effective in image recognition, NLP, and speech recognition. They can also model semantic structures and syntactic patterns.

Deep Learning 52

Deep Learning Neural Network Convolutional Neural Networks Natural Language Processing 52

Viso.ai

FEBRUARY 25, 2023

Vision Transformer (ViT) have recently emerged as a competitive alternative to Convolutional Neural Networks (CNNs) that are currently state-of-the-art in different image recognition computer vision tasks. Transformer models have become the de-facto status quo in Natural Language Processing (NLP).

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

Viso.ai

FEBRUARY 23, 2024

Prompt-based Segmentation combines the power of natural language processing (NLP) and computer vision to create an image segmentation model. Modern video segmentation algorithms improve their results by utilizing frame pixels and causal information. One such development is prompt-based segmentation.

Computer Vision 98

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Viso.ai

DECEMBER 1, 2023

In short, supervised learning is where the algorithm is given a set of training data. End-to-end data collection and image annotation with Viso Suite On the other hand, unsupervised learning is where the algorithm is given raw data that is not annotated. In computer vision, this process is called image annotation.

AI Modeling 98

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Heartbeat

MARCH 8, 2023

Heartbeat these past few weeks has had lots of great articles covering the latest research, NLP use-cases, and Comet tutorials. Happy Reading, Emilie, Abby & the Heartbeat Team Natural Language Processing With SpaCy (A Python Library) — by Khushboo Kumari This post goes over how the most cutting-edge NLP software, SpaCy , operates.

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The MLOps Blog

NOVEMBER 14, 2023

Example of a deep learning visualization: small convolutional neural network CNN, notice how the thickness of the colorful lines indicates the weight of the neural pathways | Source How is deep learning visualization different from traditional ML visualization? All of these visualizations do not only satisfy curiosity.

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Pickl AI

FEBRUARY 7, 2025

ML algorithms use statistical methods to identify patterns in data, allowing systems to make predictions or decisions without human intervention. Basic Concepts of Machine Learning Machine Learning revolves around training algorithms to learn from data. The model learns from the input-output pairs and predicts outcomes for new data.

Deep Learning 52

Deep Learning Machine Learning Neural Network Algorithm 52

Pickl AI

DECEMBER 9, 2024

Types of inductive bias include prior knowledge, algorithmic bias, and data bias. Types of Inductive Bias Inductive bias plays a significant role in shaping how Machine Learning algorithms learn and generalise. This bias allows algorithms to make informed guesses when faced with incomplete or sparse data.

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