IBM Journey to AI blog

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. Machine learning algorithms like Naïve Bayes and support vector machines (SVM), and deep learning models like convolutional neural networks (CNN) are frequently used for text classification.

Data Mining 170

Data Mining Convolutional Neural Networks Categorization Machine Learning 170

Chatbots Life

APRIL 16, 2024

This is useful in natural language processing tasks. By applying generative models in these areas, researchers and practitioners can unlock new possibilities in various domains, including computer vision, natural language processing, and data analysis.

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Neural Network Convolutional Neural Networks Natural Language Processing Machine Learning 59

Topbots

OCTOBER 4, 2023

Here are a few examples across various domains: Natural Language Processing (NLP) : Predictive NLP models can categorize text into predefined classes (e.g., Here are a few examples across various domains: Natural Language Processing (NLP) : Predictive NLP models can categorize text into predefined classes (e.g.,

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Generative AI Natural Language Processing Machine Learning Convolutional Neural Networks 119

Marktechpost

JUNE 27, 2023

The technology may have meaningful interactions with consumers because it uses machine learning and natural language processing. The system’s adaptability makes it useful in many contexts, including but not limited to customer care, virtual agents, and chatbots.

AI Tools 103

AI Tools Convolutional Neural Networks Artificial Intelligence Artificial Intelligence 103

Mlearning.ai

MAY 30, 2023

As we know that Convolutional Neural Network (CNN) is used for structured arrays of data such as image data. RNNs contain an internal memory that enables them to capture and use context from prior inputs, in contrast to standard feedforward neural networks, which process data strictly sequentially.

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Neural Network Convolutional Neural Networks Natural Language Processing Machine Learning 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.,

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Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

How to Learn Machine Learning

AUGUST 15, 2024

Projects to Tackle for Your Machine Learning Portfolio To build a comprehensive portfolio, consider including projects from various domains and complexity levels: Image Classification : Develop a model to classify images using convolutional neural networks (CNNs).

Machine Learning 64

Machine Learning Convolutional Neural Networks Neural Network Artificial Intelligence 64

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).

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Computer Vision Deep Learning NLP Neural Network 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).

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Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

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.

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Neural Network Explainability Convolutional Neural Networks Natural Language Processing 52

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.

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Neural Network Convolutional Neural Networks Deep Learning Machine Learning 72

Heartbeat

JANUARY 12, 2023

Other practical examples of deep learning include virtual assistants, chatbots, robotics, image restoration, NLP (Natural Language Processing), and so on. Convolution, pooling, and fully connected layers are just a few components that make up a convolutional neural network.

Deep Learning 52

Deep Learning Convolutional Neural Networks Neural Network Machine Learning 52

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. Thus, positioning him as one of the top AI influencers in the world.

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Pickl AI

APRIL 9, 2025

Language Understanding: Processing and interpreting human language (Natural Language Processing – NLP). While AI has broader applications such as robotics and natural language processing (NLP), DL excels in focused areas like image detection and generative AI models.

Deep Learning 52

Deep Learning Artificial Intelligence Artificial Intelligence Explainability 52

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. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al.

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BERT Natural Language Processing Large Language Models NLP 83

Pragnakalp

JANUARY 1, 2024

Since then, in the realms of AI and machine learning, transformer models have emerged as a groundbreaking approach to various language-related tasks. The post Introduction to Mistral 7B appeared first on Pragnakalp Techlabs: AI, NLP, Chatbot, Python Development.

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Neural Network Natural Language Processing Convolutional Neural Networks NLP 52

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

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. Describe the Architecture of Transformer Models.

Deep Learning 52

Deep Learning Neural Network Natural Language Processing Convolutional Neural Networks 52

Pickl AI

SEPTEMBER 25, 2024

Natural Language Processing (NLP) NLP applications powered by Deep Learning have transformed how machines understand human language. Chatbots and Virtual Assistants These AI-driven tools utilise Deep Learning to provide customer support through natural language conversations.

Deep Learning 52

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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.

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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

Viso.ai

JULY 9, 2024

Efficient, quick, and cost-effective learning processes are crucial for scaling these models. Transfer Learning is a key technique implemented by researchers and ML scientists to enhance efficiency and reduce costs in Deep learning and Natural Language Processing.

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Neural Network BERT Deep Learning Computer Vision 52

Pickl AI

SEPTEMBER 18, 2023

These AI systems can generate new data or content rather than simply analyzing or processing existing data. Natural language processing, computer vision, music composition, art generation, and other applications frequently employ generative AI models. CycleGAN: Transforms images from one domain to another (e.g.,

Generative AI 52

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Viso.ai

JULY 9, 2024

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. For example, in Natural Language Processing (NLP), the model works by predicting the next word in a sequence.

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ML Review

JUNE 4, 2018

Recent Intersections Between Computer Vision and Natural Language Processing (Part Two) This is the second instalment of our latest publication series looking at some of the intersections between Computer Vision (CV) and Natural Language Processing (NLP). Sequence to Sequence Learning with Neural Networks.

Neural Network 52

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Mlearning.ai

DECEMBER 13, 2023

Describe the architecture of a Convolutional Neural Network (CNN) in detail. Layers in CNN: A typical CNN has three main types of layers: convolutional layers, pooling layers, and fully connected layers. Convolutional Layers: They apply filters to the input image to detect features like edges and corners.

Deep Learning 52

Deep Learning Neural Network Explainability Convolutional Neural Networks 52