Máté Kristóf's repositories
30-Days-Of-Python
30 days of Python programming challenge is a step by step guide to learn Python programming language in 30 days.
actools
Alternative launcher for Assetto Corsa named Content Manager, and some utils as well.
awesome-python
A curated list of awesome Python frameworks, libraries, software and resources
awesome-quant
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
Awesome-Transformer-Attention
An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites
bigquery-oreilly-book
Source code accompanying: BigQuery: The Definitive Guide by Lakshmanan & Tigani to be published by O'Reilly Media
deep-learning-book
Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"
deep-learning-coursera
Deep Learning Specialization by Andrew Ng on Coursera.
detectron2
Detectron2 is FAIR's next-generation research platform for object detection and segmentation.
developer-roadmap
Roadmap to becoming a web developer in 2019
DL-workshop-series
Material used for Deep Learning related workshops for Machine Learning Tokyo (MLT)
finplot
Performant and effortless finance plotting for Python
handson-ml2
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Keras-segmentation-deeplab-v3.1
An awesome semantic segmentation model that runs in real time
lightweight-charts-python
Python framework for TradingView's Lightweight Charts JavaScript library.
mit-deep-learning
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
nevergrad
A Python toolbox for performing gradient-free optimization
Preprocessing-for-deep-learning
This is the notebook associated with the blog post:
Python
All Algorithms implemented in Python
semantic-segmentation-demo
A Full stack Semantic Segmentation project using tensorflow, deeplab and dash
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.
the-gan-zoo
A list of all named GANs!