IBM Journey to AI blog

SEPTEMBER 24, 2024

” When Guerena’s team first started working with smartphone images, they used convolutional neural networks (CNNs). ” Guerena’s team is now working on integrating speech-to-text and natural language processing alongside computer vision in the systems they’re building.

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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?

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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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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.

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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

With advancements in machine learning (ML) and deep learning (DL), AI has begun to significantly influence financial operations. 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:

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Viso.ai

JULY 9, 2024

On the other hand, the generative AI task is to create new data points that look like the existing ones. 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.

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