Remove 2014 Remove Data Preparation Remove Deep Learning
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A Guide to Convolutional Neural Networks

Heartbeat

AlexNet significantly improved performance over previous approaches and helped popularize deep learning and CNNs. GoogLeNet: is a highly optimized CNN architecture developed by researchers at Google in 2014. The data should be split into training, validation, and testing sets.

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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning Blog

Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care. Improve the quality and time to market for deep learning models in diagnostic medical imaging.

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Must-Have Prompt Engineering Skills for 2024

ODSC - Open Data Science

GANs, introduced in 2014 paved the way for GenAI with models like Pix2pix and DiscoGAN. Open Source ML/DL Platforms: Pytorch, Tensorflow, and scikit-learn Hiring managers continue to favor the most popular open-source machine/deep learning platforms including Pytorch, Tensorflow, and scikit-learn.

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Building your own Object Detector from scratch with Tensorflow

Mlearning.ai

In this story, we talk about how to build a Deep Learning Object Detector from scratch using TensorFlow. Most of machine learning projects fit the picture above Once you define these things, the training is a cat-and-mouse game where you need “only” tuning the training hyperparameters in order to achieve the desired performance.

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Effectively solve distributed training convergence issues with Amazon SageMaker Hyperband Automatic Model Tuning

AWS Machine Learning Blog

Recent years have shown amazing growth in deep learning neural networks (DNNs). International Conference on Machine Learning. On large-batch training for deep learning: Generalization gap and sharp minima.” Toward understanding the impact of staleness in distributed machine learning.” PMLR, 2018. [2]