Automatic colorization using deep neural networks. "Colorful Image Colorization." In ECCV, 2016.
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Updated
Nov 27, 2023 - Python
Deep learning is an AI function and a subset of machine learning, used for processing large amounts of complex data. Deep learning can automatically create algorithms based on data patterns.
Automatic colorization using deep neural networks. "Colorful Image Colorization." In ECCV, 2016.
The deeplearning algorithms implemented by tensorflow
Deep learning software for colorizing black and white images with a few clicks.
deeplearning.ai , By Andrew Ng, All video link
Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow
PyTorch tutorials, examples and some books I found 【不定期更新】整理的PyTorch 最新版教程、例子和书籍
Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.
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
Machine Learning notebooks for refreshing concepts.
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)
PyTorch implementation of Deformable Convolution
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
Ladder network is a deep learning algorithm that combines supervised and unsupervised learning.
CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning
Hands-On Deep Learning Algorithms with Python, By Packt
RAD: Reinforcement Learning with Augmented Data
Deep Learning Library. For education. Based on pure Numpy. Support CNN, RNN, LSTM, GRU etc.
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).
Examples of Machine Learning code using Comet.ml