Automatic colorization using deep neural networks. "Colorful Image Colorization." In ECCV, 2016.
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Updated
Dec 19, 2022 - Python
Deep learning is an AI function and subset of machine learning, used for processing large amounts of complex data.
Automatic colorization using deep neural networks. "Colorful Image Colorization." In ECCV, 2016.
Deep learning software for colorizing black and white images with a few clicks.
The deeplearning algorithms implemented by tensorflow
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
A general list of resources to image text localization and recognition 场景文本位置感知与识别的论文资源与实现合集 シーンテキストの位置認識と識別のための論文リソースの要約
PyTorch implementation of Deformable Convolution
Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow
deeplearning.ai , By Andrew Ng, All video link
CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning
Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.
PyTorch1.x tutorials, examples and some books I found 【不定期更新】整理的PyTorch 1.x 最新版教程、例子和书籍
real-time fire detection in video imagery using a convolutional neural network (deep learning) - from our ICIP 2018 paper (Dunnings / Breckon) + ICMLA 2019 paper (Samarth / Bhowmik / Breckon)
RAD: Reinforcement Learning with Augmented Data
Machine Learning notebooks for refreshing concepts.
Revisions and implementations of modern Convolutional Neural Networks architectures in TensorFlow and Keras
Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes
【不定期更新】收集整理的一些网站中(如知乎、Quora、Reddit、Stack Exchange等)与深度学习、机器学习、强化学习、数据科学相关的有价值的问题
Ladder network is a deep learning algorithm that combines supervised and unsupervised learning.
Always sparse. Never dense. But never say never. A Sparse Training repository for the Adaptive Sparse Connectivity concept and its algorithmic instantiation, i.e. Sparse Evolutionary Training, to boost Deep Learning scalability on various aspects (e.g. memory and computational time efficiency, representation and generalization power).