Heartbeat

SEPTEMBER 20, 2023

Pixabay: by Activedia Image captioning combines natural language processing and computer vision to generate image textual descriptions automatically. These algorithms can learn and extract intricate features from input images by using convolutional layers. We pay our contributors, and we don't sell ads.

Natural Language Processing 52

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Artificial Corner

JANUARY 23, 2025

The course will show you how to set up Python, teach you how to print your first “Hello World”, and explain all the core concepts in Python. Remember that Python is a programming language with applications not only in data science but in many fields. Data scientists use NLP techniques to interpret text data for analysis.

Data Science 95

Data Science Python Convolutional Neural Networks Natural Language Processing 95

John Snow Labs

MAY 23, 2023

Visual question answering (VQA), an area that intersects the fields of Deep Learning, Natural Language Processing (NLP) and Computer Vision (CV) is garnering a lot of interest in research circles. A VQA system takes free-form, text-based questions about an input image and presents answers in a natural language format.

Natural Language Processing 40

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Unite.AI

JULY 31, 2023

Self-supervised learning has already shown its results in Natural Language Processing as it has allowed developers to train large models that can work with an enormous amount of data, and has led to several breakthroughs in fields of natural language inference, machine translation, and question answering.

Computer Vision 246

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Mlearning.ai

MAY 11, 2023

ChatGPT is an AI language model that has taken the world by storm since its release in 2020. Indeed, this AI is a powerful natural language processing tool that can be used to generate human-like language, making it an ideal tool for creating chatbots, virtual assistants, and other applications that require natural language interactions.

Neural Network 52

Neural Network Explainability Convolutional Neural Networks Natural Language Processing 52

Marktechpost

JUNE 27, 2023

Raw Shorts To assist organizations in making explainer films, animations, and promotional movies for the web and social media, Raw Shorts provides a text-to-video creator and a video editor driven by artificial intelligence. voiceover, it offers over 120 realistic text-to-speech voices in 20 languages.

AI Tools 103

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Heartbeat

MAY 2, 2023

Neural Networks Neural networks are a popular deep learning algorithm that are inspired by the structure and function of the human brain. They have several use cases, from image recognition to natural language processing and self-driving vehicles. We pay our contributors, and we don’t sell ads.

Deep Learning 52

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ODSC - Open Data Science

MARCH 10, 2025

By 2017, deep learning began to make waves, driven by breakthroughs in neural networks and the release of frameworks like TensorFlow. Sessions on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) started gaining popularity, marking the beginning of data sciences shift toward AI-driven methods.

Data Science 52

Data Science Neural Network Convolutional Neural Networks Large Language Models 52

Pickl AI

AUGUST 14, 2024

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. Models such as Long Short-Term Memory (LSTM) networks and Transformers (e.g.,

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

Pickl AI

SEPTEMBER 3, 2024

Here are some core responsibilities and applications of ANNs: Pattern Recognition ANNs excel in recognising patterns within data , making them ideal for tasks such as image recognition, speech recognition, and natural language processing. Frequently Asked Questions What are the main types of Artificial Neural Network?

Neural Network 52

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Heartbeat

JUNE 6, 2023

GCNs have been successfully applied to many domains, including computer vision and social network analysis. In recent years, researchers have also explored using GCNs for natural language processing (NLP) tasks, such as text classification , sentiment analysis , and entity recognition.

NLP 59

NLP Convolutional Neural Networks Neural Network Natural Language Processing 59

Heartbeat

MAY 30, 2023

Natural Language Processing : DRL has been used for enhancing chatbots, machine translations, speech recognition, etc DRL for robotics; image from TechXplore We could go on and on about Deep Reinforcement Learning’s applications, from training self-driving cars to creating game-playing agents that outperform human players.

Neural Network 72

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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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Heartbeat

AUGUST 15, 2023

Advanced techniques have been devised to address these challenges, such as deep learning, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). RNNs have been extensively employed for natural language processing tasks such as speech recognition and language translation.

Python 52

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Snorkel AI

MARCH 1, 2023

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. This can make it challenging for businesses to explain or justify their decisions to customers or regulators.

BERT 83

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Heartbeat

AUGUST 29, 2023

Deep learning is a branch of machine learning that makes use of neural networks with numerous layers to discover intricate data patterns. Deep learning models use artificial neural networks to learn from data. Speech and Audio Processing : Speaker identification, speech recognition, creation of music, etc.

Software Development 52

Software Development Deep Learning Neural Network Convolutional Neural Networks 52

Viso.ai

MARCH 26, 2024

This enhances the interpretability of AI systems for applications in computer vision and natural language processing (NLP). Furthermore, attention mechanisms work to enhance the explainability or interpretability of AI models. In a way, you can consider it offering a window into the “thought processes” of AI.

Deep Learning 52

Deep Learning Computer Vision Neural Network Natural Language Processing 52

Mlearning.ai

DECEMBER 13, 2023

Explain the vanishing and exploding gradient problems. Vanishing Gradient: When training a neural network, sometimes the updates to the weights get too small. Difference: Traditional neural networks usually have a single goal, like classification or regression. Explain the concept of batch normalization.

Deep Learning 52

Deep Learning Neural Network Explainability Convolutional Neural Networks 52

Mlearning.ai

MARCH 30, 2023

Let me explain this in simple words. The name transfer learning comes from the process of using the learning from one problem to solve another. Learning is nothing but just getting the value of weights and biases for a neural network that gives you the desired result.

Neural Network 52

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Pickl AI

SEPTEMBER 23, 2024

Gain insights into neural networks, optimisation methods, and troubleshooting tips to excel in Deep Learning interviews and showcase your expertise. In image recognition, Convolutional Neural Networks (CNNs) can accurately identify objects and faces in images. Explain the Concept of Forward Propagation.

Deep Learning 52

Deep Learning Neural Network Natural Language Processing Convolutional Neural Networks 52

Viso.ai

DECEMBER 27, 2023

In a computer vision example of contrast learning, we aim to train a tool like a convolutional neural network to bring similar image representations closer and separate the dissimilar ones. It typically uses a convolutional neural network (CNN) architecture, like ResNet , for extracting image features.

Convolutional Neural Networks 52

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Heartbeat

JULY 6, 2023

The process of locating and identifying objects of interest in an image or video is part of the object detection method, a popular computer vision technique. Object detection is typically achieved through the use of deep learning models, particularly Convolutional Neural Networks (CNNs).

Computer Vision 59

Computer Vision Convolutional Neural Networks Deep Learning Neural Network 59

AWS Machine Learning Blog

FEBRUARY 10, 2023

We describe how we designed an accurate, explainable ML model to make coverage classification from player tracking data, followed by our quantitative evaluation and model explanation results. This explains the uncertainty of the model’s classification: the TE is sticking with the SS by design, creating biases in the data.

Computer Vision 90

Computer Vision Machine Learning ML Convolutional Neural Networks 90

Pickl AI

AUGUST 1, 2024

Unlike traditional Machine Learning, which often relies on feature extraction and simpler models, Deep Learning utilises multi-layered neural networks to automatically learn features from raw data. This capability allows Deep Learning models to excel in tasks such as image and speech recognition, natural language processing, and more.

Deep Learning 52

Deep Learning Neural Network Machine Learning Natural Language Processing 52

Pickl AI

JULY 22, 2024

Natural Language Processing (NLP) In NLP , probabilistic models enhance text understanding and generation. Techniques like hidden Markov Models (HMMs) and Bayesian networks help in tasks such as part-of-speech tagging, named entity recognition, and machine translation. Explore: What is Tokenization in NLP?

Machine Learning 52

Machine Learning Natural Language Processing Convolutional Neural Networks NLP 52

Heartbeat

FEBRUARY 22, 2023

These models have been used to achieve state-of-the-art performance in many different fields, including image classification, natural language processing, and speech recognition. This article delves into using deep learning to enhance the effectiveness of classic ML models. We pay our contributors, and we don’t sell ads.

Deep Learning 52

Deep Learning Machine Learning Convolutional Neural Networks Computer Vision 52

Explosion

NOVEMBER 9, 2016

Over the last six months, a powerful new neural network playbook has come together for Natural Language Processing. This post explains the components of this new approach, and shows how they’re put together in two recent systems. 2016) model and a convolutional neural network (CNN).

Deep Learning 52

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Heartbeat

AUGUST 22, 2023

Introduction Text classification is the process of automatically assigning a set of predefined categories or labels to a piece of text. It’s an essential task in natural language processing (NLP) and machine learning, with applications ranging from sentiment analysis to spam detection. You can get the dataset here.

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

Pickl AI

JULY 14, 2023

Deep Learning, a subfield of ML, gained attention with the development of deep neural networks. Moreover, Deep Learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), achieved remarkable breakthroughs in image classification, natural language processing, and other domains.

Neural Network 52

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AWS Machine Learning Blog

MARCH 13, 2024

Select model architecture: There are many different types of models to choose from, including recurrent neural networks (RNNs), transformer models, and convolutional neural networks (CNNs). Preprocessing may involve cleaning the data, tokenizing text, and converting words to numerical representations.

Machine Learning 132

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Viso.ai

MARCH 26, 2024

Arguably, one of the most pivotal breakthroughs is the application of Convolutional Neural Networks (CNNs) to financial processes. 2: Automated Document Analysis and Processing No.3: This has the potential to revolutionize many processes by accelerating processing times while improving accuracy and security.

Computer Vision 52

Computer Vision Neural Network Machine Learning Convolutional Neural Networks 52

Pickl AI

NOVEMBER 13, 2024

Despite challenges like vanishing gradients, innovations like advanced optimisers and batch normalisation have improved their efficiency, enabling neural networks to solve complex problems. Backpropagation in Neural Networks is vital in training these systems by efficiently updating weights to minimise errors.

Neural Network 52

Neural Network Algorithm Convolutional Neural Networks Natural Language Processing 52

Pickl AI

NOVEMBER 28, 2024

This blog will explain gradient descent, its types, and its significance in training Deep Learning models. Applications of Gradient Descent in Deep Learning It plays a crucial role in training neural networks, including Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs).

Deep Learning 52

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Viso.ai

JULY 9, 2024

The difference between a generative vs. a discriminative problem explained. Discriminative models include a wide range of models, like Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Support Vector Machines (SVMs), or even simpler models like random forests.

Generative AI 52

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Viso.ai

JUNE 18, 2024

The goal is for the model to distinguish archaic shell-ring constructions from modern buildings or natural features. Using a Mask R–CNN ( convolutional neural network ) model, they were able to achieve a detection accuracy of 75% and 79.5% for archeological shell rings and mounds, respectively.

Computer Vision 52

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Heartbeat

MARCH 5, 2024

Additionally, interdisciplinary collaborations with other fields, such as robotics and natural language processing, contribute to developing more robust computer vision systems. What Is Image Augmentation? We're committed to supporting and inspiring developers and engineers from all walks of life.

Computer Vision 52

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Heartbeat

OCTOBER 12, 2023

The Rise of Large Language Models The emergence and proliferation of large language models represent a pivotal chapter in the ongoing AI revolution. These models, powered by massive neural networks, have catalyzed groundbreaking advancements in natural language processing (NLP) and have reshaped the landscape of machine learning.

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