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Revolutionizing Image Classification: Training Large Convolutional Neural Networks on the ImageNet Dataset

Marktechpost

The success of this model reflects a broader shift in computer vision towards machine learning approaches that leverage large datasets and computational power. Previously, researchers doubted that neural networks could solve complex visual tasks without hand-designed systems. when predictions from five CNNs were averaged.

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Game-Changer: How the World’s First GPU Leveled Up Gaming and Ignited the AI Era

NVIDIA

Deep learning — a software model that relies on billions of neurons and trillions of connections — requires immense computational power. By 2011, AI researchers had discovered NVIDIA GPUs and their ability to handle deep learning’s immense processing needs. This marked a seismic shift in technology.

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Meet the Research Scientist: Shirley Ho

NYU Center for Data Science

What sets Dr. Ho apart is her pioneering work in applying deep learning techniques to astrophysics. Ho’s innovative approach has led to several groundbreaking achievements: Her team at Carnegie Mellon University was the first to apply 3D convolutional neural networks in astrophysics.

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Using JPEG Compression to Improve Neural Network Training

Unite.AI

A new research paper from Canada has proposed a framework that deliberately introduces JPEG compression into the training scheme of a neural network, and manages to obtain better results – and better resistance to adversarial attacks. In contrast, JPEG-DL (right) succeeds in distinguishing and delineating the subject of the photo.

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The Evolution of ImageNet and Its Applications

Viso.ai

Image classification employs AI-based deep learning models to analyze images and perform object recognition, as well as a human operator. It is one of the largest resources available for training deep learning models in object recognition tasks. 2011 – A good ILSVRC image classification error rate is 25%.

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The History of Artificial Intelligence (AI)

Pickl AI

The advent of big data, coupled with advancements in Machine Learning and deep learning, has transformed the landscape of AI. Techniques such as neural networks, particularly deep learning, have enabled significant breakthroughs in image and speech recognition, natural language processing, and autonomous systems.

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Testing the Robustness of LSTM-Based Sentiment Analysis Models

John Snow Labs

Using LSTM networks’ inherent ability to store historical knowledge over long periods, the model architecture will be developed to efficiently capture the rich contextual cues and intricacies found in the IMDB dataset. Sentiment Analysis Using Simplified Long Short-term Memory Recurrent Neural Networks. abs/2005.03993 Andrew L.