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7 Powerful Python ML Libraries For Data Science And Machine Learning.

Mlearning.ai

Scikit-Learn: Scikit-Learn is a machine learning library that makes it easy to train and deploy machine learning models. It has a wide range of features, including data preprocessing, feature extraction, deep learning training, and model evaluation. How Do I Use These Libraries?

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This AI Paper from Google Presents a Set of Optimizations that Collectively Attain Groundbreaking Latency Figures for Executing Large Diffusion Models on Various Devices

Marktechpost

Due to its many benefits over server-based methods, such as lower latency, increased privacy, and greater scalability, on-device model inference acceleration has recently attracted much interest. In light of the limitations of standard fusion rules, they devised custom implementations capable of running a wider variety of neural operators.

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Deployment of PyTorch Model Using NCNN for Mobile Devices?—?Part 2

Mlearning.ai

Conclusions In this post, I discussed how to integrate the C++ code with the NCNN inference engine into Android for model deployment on the mobile phone. You can easily tailor the pipeline for deploying your deep learning models on mobile devices. Hope these series of posts help. Thanks for reading. 2] Android.

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NLP News Cypher | 07.26.20

Towards AI

GitHub: Tencent/TurboTransformers Make transformers serving fast by adding a turbo to your inference engine!Transformer The sell is that it can support various lengths of input sequences without preprocessing which reduces overhead in computation. ? These 2 repos encompass NLP and Speech modeling.

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Start Up Your Engines: NVIDIA and Google Cloud Collaborate to Accelerate AI Development

NVIDIA

Google for Startups Cloud Program members can join NVIDIA Inception and gain access to technological expertise, NVIDIA Deep Learning Institute course credits, NVIDIA hardware and software, and more.

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Underwater Trash Detection using Opensource Monk Toolkit

Towards AI

Credits A critical component for these robots is to identify different objects and take actions accordingly and this is where Deep Learning and Machine Vision enters the space!!! On an Nvidia V-100 GPU, the detector runs at 15 fps on average.

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Host ML models on Amazon SageMaker using Triton: TensorRT models

AWS Machine Learning Blog

TensorRT is an SDK developed by NVIDIA that provides a high-performance deep learning inference library. It’s optimized for NVIDIA GPUs and provides a way to accelerate deep learning inference in production environments. Triton Inference Server supports ONNX as a model format.

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