The RWTH extensible training framework for universal recurrent neural networks

DL course co-developed by YSDA, HSE and Skoltech

Aesara is a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.

Soundscape analysis with BirdNET.

Handwritten Digit Recognition using Machine Learning and Deep Learning

Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.

Application of SVM classifier and NN classifiers for sentiment classification of Russian Twitter messages

It is an open-source Python library developed at MILA, that allows efficient definition, optimization, and evaluation of mathematical expressions, especially matrix-valued ones. It is widely used for deep learning and other machine learning tasks due to its ability to perform symbolic computation.

Tools for the symbolic manipulation of PyMC models, Theano, and TensorFlow graphs.

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

Implementation of "Weighted voxel: a novel voxel representation for 3D reconstruction" (Xie et al., ICIMCS 2018).

The On-Ramp to Deep Learning

Machine Learning on Raspberry Pi Zero and Zero-W

A curated list of applied machine learning and data science notebooks and libraries across different industries.

deep learning

A Theano implementation of the biaffine parser in paper "Deep Biaffine Attention for Neural Dependency Parsing".

A light weight deep learning framework, on top of Theano, offering better balance between flexibility and abstraction

Easy multiple sclerosis white matter lesion segmentation using convolutional deep neural networks.

Deep Reinforcement Learning for Keras.

Deep Learning using Theano, tensor flow, keras in Python. Self Learning with YouTube videos and text books.