shakirsofi Goto Github PK

50.038-computational-data-science

am205_examples

Harvard Applied Math 205: Code Examples

attentionlstm-simple

books-for-iit-m-data-science

Subject wise prescribed books for IIT-M Data Science

conditional_vae

data-augmentation-review

List of useful data augmentation resources. You will find here some not common techniques, libraries, links to GitHub repos, papers, and others.

data-science-ipython-notebooks

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.

dlwp-cs

Deep learning models for global weather prediction on a cubed sphere

hankel_completion

misc

ml-100-intro-to-dsml

Course material for ML-100-Intoduction-To-Data Science-and-Machine-Learning (Summer 2017)

molecular_generation

Deep Learning And Applied Artificial Intelligence Project 2019/2020 - Molecular Synthesis & Reconstruction

my_cv

mydatasets

Contain files and datasets.

nlp_course

YSDA course in Natural Language Processing

optim

💎A site, that contains systematic optimization methods and theory review

paper-computer-vision

Computer vision papers

pytorch101

Exploration to learn from pytorch documentation and courses available online

qcomputing

quantum-computing-collection-of-resources

A Well Maintained Repository On Quantum Computing Resources [Code+Theory] Updated Regularly During My Time At IBM, Qubit x Qubit And The Coding School's Introduction To Quantum Computing Course '21

shakirsofi

Config files for my GitHub profile.

sktime

A unified framework for machine learning with time series

tensor_data_analysis

tensor_train_forecasting

tensorizingdmd

TensorizingDMD

the-art-of-linear-algebra

Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"

time-series_imaging_forecasting

In this project we have explored the use of imaging time series to enhance forecasting results with Neural Networks. The approach has revealed itself to be extremely promising as, both in combination with an LSTM architecture and without, it has out-performed the pure LSTM architecture by a solid margin within our test datasets.

toposeg

Segmentation with topological priors

vmls-companions

These are companion notebooks written in Julia and Python for: "Introduction to Applied Linear Algebra" by Boyd and Vandenberghe.