jbdatascience
I care about Data Science in general. But I try to focus on some subfields of it (or I will explode ......... ).
Netherlands
Pinned Repositories
AI_Supply_Chain
This is the code for "AI for Supply Chain" by Siraj Raval on Youtube
Bambi_resources
Educational resources
causal_inference_book
R Code for Causal Inference by Hernán and Robins
Data-Analysis
Data Analysis Using Python
Data-science
Collection of useful data science topics along with code and articles
data_science_blogs
A repository to keep track of all the code that I end up writing for my blog posts.
Datasets
Machine learning datasets used in tutorials on MachineLearningMastery.com
Deep-math-machine-learning.ai
A blog which talks about machine learning, deep learning and the Math. and Machine learning algorithms written from scratch.
deepprot
machine learning for protein engineering
fair-classification
Python code for training fair logistic regression classifiers.
jbdatascience's Repositories
jbdatascience/Data-science
Collection of useful data science topics along with code and articles
jbdatascience/deepprot
machine learning for protein engineering
jbdatascience/aitextgen
A robust Python tool for text-based AI training and generation using GPT-2.
jbdatascience/andoma
♞ A chess engine with alpha-beta pruning, piece-square tables, and move ordering
jbdatascience/discopy
a toolbox for computing with monoidal categories
jbdatascience/dm_env
A Python interface for reinforcement learning environments
jbdatascience/ec
jbdatascience/ElegantRL
Lightweight, efficient and stable implementations of deep reinforcement learning algorithms using PyTorch. 🔥
jbdatascience/fake-useragent
up to date simple useragent faker with real world database
jbdatascience/FinRL
A Deep Reinforcement Learning Library for Automated Trading in Quantitative Finance. NeurIPS 2020. 🔥
jbdatascience/GLOM
An implementation of Geoffrey Hinton's paper "How to represent part-whole hierarchies in a neural network" in Pytorch.
jbdatascience/glom-pytorch
An attempt at the implementation of Glom, Geoffrey Hinton's new idea that integrates concepts from neural fields, top-down-bottom-up processing, and attention (consensus between columns), for emergent part-whole heirarchies from data
jbdatascience/GLOM-TensorFlow
An attempt at the implementation of GLOM, Geoffrey Hinton's paper for emergent part-whole hierarchies from data
jbdatascience/gpt-neo
An implementation of model parallel GPT2& GPT3-like models, with the ability to scale up to full GPT3 sizes (and possibly more!), using the mesh-tensorflow library.
jbdatascience/gpt-neox
An implementation of model parallel GPT-3-like models on GPUs, based on the DeepSpeed library. Designed to be able to train models in the hundreds of billions of parameters or larger.
jbdatascience/making_with_ml
jbdatascience/perceiver-pytorch
Implementation of Perceiver, General Perception with Iterative Attention, in Pytorch
jbdatascience/ProtTrans
ProtTrans is providing state of the art pretrained language models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using Transformers Models.
jbdatascience/python-chess
A chess library for Python, with move generation and validation, PGN parsing and writing, Polyglot opening book reading, Gaviota tablebase probing, Syzygy tablebase probing, and UCI/XBoard engine communication
jbdatascience/qiskit-tutorials
A collection of Jupyter notebooks showing how to use the Qiskit SDK
jbdatascience/qnlp_lorenz_etal_2021_resources
Code and resources for the Lorenz et al. (2021) QNLP paper
jbdatascience/recommender_transformer
jbdatascience/self-attention-music-tagging
jbdatascience/shapash
Shapash makes Machine Learning models transparent and understandable by everyone
jbdatascience/SIMPLE
Selfplay In MultiPlayer Environments
jbdatascience/SkinDeep
Get Deinked!!
jbdatascience/stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
jbdatascience/sudoku_solver
Solving a Sudoku Puzzle from a screenshot
jbdatascience/tensorflow-deep-learning
All course materials for the Zero to Mastery Deep Learning with TensorFlow course.
jbdatascience/tsai
Time series Timeseries Deep Learning Pytorch fastai - State-of-the-art Deep Learning with Time Series and Sequences in Pytorch / fastai