burntt Goto Github PK

argo-examples

artwork-by-bear

auto-phd-gpt

cdcc-clustering-unsupervised

codeforces.com

Advanced Algorithms and Data Structures: All the problems I solved of all the contests during a three week period of doing a daily competition on codeforces.

developer_to_cto_slides_only

“Developer” is a very generic term with many underlying meanings. As a person having computer science and algorithmics skills, one can fell both curious and lost in front of the large variety of aspects to manage at work, from software engineer to CTO. What do you want to build in IT? And how to make your technology impactful? This course is about discovering the world of IT and understanding how technology should contribute to you and your company.

doge_detector

elegantrl

Scalable and Elastic Deep Reinforcement Learning Using PyTorch. Please star. 🔥

finrl

FinRL: Financial Reinforcement Learning. 🔥

finrl-meta

FinRL­-Meta: A Universe for Data­-Driven Financial Reinforcement Learning. 🔥

finrl_crypto

An open-source framework for reduction of overfitting of DRL agents in Finance

forecastingtrends2024

industrial_machine_learning

informer

Updated Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting (AAAI'21 Best Paper)

informer2020

The GitHub repository for the paper "Informer" accepted by AAAI 2021.

java_programming

Java is one of the most popular programming languages in the software development industry. This course covers topics about the Java programming language as it is applied to networking, databases, web services, mobile applications, and other practical aspects

kitebotbcn

kitehub

kitemania_backend

Database back-end for the Kite Mania webpage based on Java

kitemania_frontend

Mustache plus HTML front end for Kite Mania webpage

llm-attacks

Hack of LLaMa LLM

masters_machine_learning

This course aims to introduce students to the contemporary state of Machine Learning and Artificial Intelligence. It combines theoretical foundations of Machine Learning algorithms with comprehensive practical assignments. The course covers materials from classical algorithms to Deep Learning approaches and recent achievements in the field of Artificial Intelligence. This course is accompanied by Deep Learning in Applications course (Module 12), which brings the most recent achievements in the field and their applications. Programming assignments will be implemented in Python 3. PyTorch framework will be used for Deep Learning practice. Special acknowledgements to Nikolay Karpachev and Ivan Provilkov for contributions into the course materials and structure.

medicalinsurance

mitthesislatex

mlflow

Open source platform for the machine learning lifecycle

mvts-representation-learning-gtrace

my_first_android_app

Simple try of building my first full stack android application!

my_first_spark_app

A full stack SparkApp connected to a MongoDB database and a HTML page

neural_networks_and_computer_vision

Modern computer vision is almost entirely based on deep convolutional neural networks, so this is a natural fit that lets us explore interesting architectures, while at the same time staying focused and not going into too wide of a survey of the entire field of deep learning. Computer vision is also a key element in robotics: vision systems are necessary for navigation, localization and mapping, and scene understanding, which are all key problems for creating industrial and home robots.

optimization_in_machine_learning

Optimization (being referred to not as code optimization but mathematical optimization) empowers practically all modern machine learning and goes well beyond that. After you define a model in machine learning, you tune the model to the data at hand. Mathematically it usually just boils down to finding the minimum of the loss function of the model.