Tag: ML
Scaling down Deep Learning
Scaling down Deep Learning Scaling down Deep Learning "Constructing the MNIST-1D dataset. As with the original MNIST dataset, the task is to learn to classify the digits 0-9. Unlike the MNIST dataset, which consists of 28x28 images, each of the …
Mismatches between Traditional Optimization Analyses and Modern Deep Learning
Mismatches between Traditional Optimization Analyses and Modern Deep Learning Mismatches between Traditional Optimization Analyses and Modern Deep Learning "Today’s post (based upon our paper with Kaifeng Lyu at NeurIPS20) identifies other surp …
A deep ranking personalization framework
A deep ranking personalization framework linkedin/gdmix Generalized Deep Mixed Model (GDMix) is a framework to train non-linear fixed effect and random effect models. This kind of models are widely used in personalization of search and recommen …
ML.NET September Updates
ML.NET September Updates ML.NET September Updates | .NET Blog ML.NET is an open-source, cross-platform machine learning framework for .NET developers. It enables integrating machine learning into your .NET apps without requiring you to leave th …
A generative modeling approach for benchmarking and training shallow quantum circuits
A generative modeling approach for benchmarking and training shallow quantum circuits A generative modeling approach for benchmarking and training shallow quantum circuits Hybrid quantum-classical algorithms provide ways to use noisy intermedia …
A 3-Layer Feed-Forward Neural Net In C#, with Graphical Display
A 3-Layer Feed-Forward Neural Net In C#, with Graphical Display A 3-Layer Feed-Forward Neural Net In C#, with Graphical Display C# implementation of a simple classification problem (recognizing digits from the MNIST dataset). Working through ex …
Interpretable machine learning models (imodels)
Interpretable machine learning models (imodels) csinva/interpretability-implementations-demos Scikit-learn style wrappers/implementations of different interpretable models. Docs available here. The interpretable models within the imodels folder …
An Introduction to Adversarial Examples in Deep Learning
An Introduction to Adversarial Examples in Deep Learning Weights & Biases This report provides an intuitive introduction to adversarial examples, discusses a wide variety of different adversarial attacks and, most notably, provides advice o …
Core ML
Core ML Core ML is optimized for on-device performance of a broad variety of model types by leveraging Apple hardware and minimizing memory footprint and power consumption. Source: https://developer.apple.com/machine-learning/core-ml/ …
Introduction — Spinning Up documentation
Introduction — Spinning Up documentation Introduction — Spinning Up documentation Welcome to Spinning Up in Deep RL! This is an educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL). …