Deep Learning / learning platform

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h2o-3 🌿
4692 (+2) ⭐

Open Source Fast Scalable Machine Learning Platform For Smarter Applications: Deep Learning, Gradient Boosting & XGBoost, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

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polyaxon 🌿
2365 (+1) ⭐

A platform for reproducible and scalable machine learning and deep learning on kubernetes

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4 (+0) ⭐

This repository shows how I build my RTX2060 + Ubuntu16.4 + CUDA10.0 + cuDNN7.4 + TensorFlow1.13.1 platfrom.

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2 (+0) ⭐

Data Platform for Large Scale Data Processing and AI & Machine Learning/Deep Learning

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122 (+0) ⭐

GUI based deep learning platform

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anndotnet 🌿
106 (+0) ⭐

ANNdotNET - deep learning tool on .NET Platform.

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7 (+0) ⭐

Deep learning platform written in C++

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pulp-dronet 🌿
317 (+0) ⭐

A deep learning-powered visual navigation engine to enables autonomous navigation of pocket-size quadrotor - running on PULP

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11 (+0) ⭐

e-farmerce aims to solve the problem of Automated Crop Identification using Satellite Imagery and Deep Learning.

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2 (+0) ⭐

The Deep Learning Seminar is for both graduate and undergraduate students that have special interests on Deep Learning (DL). In this seminar series, the students will collaborate in an intensive examination of topics related to understanding the basic concepts, models and algorithms of DL. The basic module of the Deep Learning seminar is designed as a discussion seminar. Emphasis will be on close reading and discussion of the assigned readings. Each volunteer participant will be responsible for leading the seminar discussion on assigned weeks. All participants are expected to come prepared to discuss and debate the readings each week. Participants will develop their understanding of the material through class presentations and discussions. An advanced module of source code review is designed for the participants that want to develop their skills on implementing existing DL models and designing new models. Emphasis will be on close review and discussion of the DL models. Each participant will be responsible for leading the seminar discussion on mathematic formulas, algorithms and the source code of the assigned model. In particular, this seminar will use Apache SINGA (http://singa.apache.org/), an open source DL platform for code review.

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