Remove 2012 Remove Deep Learning Remove Explainability
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Ronald T. Kneusel, Author of “How AI Works: From Sorcery to Science” – Interview Series

Unite.AI

This is your third AI book, the first two being: “Practical Deep Learning: A Python-Base Introduction,” and “Math for Deep Learning: What You Need to Know to Understand Neural Networks” What was your initial intention when you set out to write this book? Different target audience.

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GoogLeNet Explained: The Inception Model that Won ImageNet

Viso.ai

GoogLeNet’s deep learning model was deeper than all the previous models released, with 22 layers in total. Increasing the depth of the Machine Learning model is intuitive, as deeper models tend to have more learning capacity and as a result, this increases the performance of a model.

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AI Will Drive Scientific Breakthroughs, NVIDIA CEO Says at SC24

NVIDIA

Milestones like Tokyo Tech’s Tsubame supercomputer in 2008, the Oak Ridge National Laboratory’s Titan supercomputer in 2012 and the AI-focused NVIDIA DGX-1 delivered to OpenAI in 2016 highlight NVIDIA’s transformative role in the field. Since CUDA’s inception, we’ve driven down the cost of computing by a millionfold,” Huang said.

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Optimized Deep Learning Pipelines: A Deep Dive into TFRecords and Protobufs (Part 2)

Heartbeat

jpg': {'class': 132, 'label': 'Hyundai Tucson SUV 2012'}, '00235.jpg': jpg': {'class': 7, 'label': 'Acura ZDX Hatchback 2012'}, '00237.jpg': jpg': {'class': 65, 'label': 'Chevrolet Avalanche Crew Cab 2012'}, '00238.jpg':

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A comprehensive guide to learning LLMs (Foundational Models)

Mlearning.ai

— YouTube Introduction to Natural Language Processing (NLP) NLP 2012 Dan Jurafsky and Chris Manning (1.1) Transformer Neural Networks — EXPLAINED! Attention is all you need) — YouTube Attention Mechanism: Overview — YouTube Encoder-Decoder Deep dive. YouTube BERT Research — Ep.

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Best Machine Learning Datasets

Flipboard

Object detection works by using machine learning or deep learning models that learn from many examples of images with objects and their labels. In the early days of machine learning, this was often done manually, with researchers defining features (e.g., Object detection is useful for many applications (e.g.,

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Robustness of a Markov Blanket Discovery Approach to Adversarial Attack in Image Segmentation: An…

Mlearning.ai

Automated algorithms for image segmentation have been developed based on various techniques, including clustering, thresholding, and machine learning (Arbeláez et al., 2012; Otsu, 1979; Long et al., 2019) proposed a novel adversarial training framework for improving the robustness of deep learning-based segmentation models.