AlgoGenesis is a centralized open-source platform dedicated to providing optimized and well-documented algorithm implementations in C. Perfect for both beginners and advanced users, this repository serves as a comprehensive learning resource for solving algorithmic challenges.

Automated machine learning can facilitate the early diagnosis and timely treatment of diabetic retinopathy. Following the 1st Diabetic Retinopathy: Segmentation and Grading Challenge held with ISBI in 2018, we would like to promote the progress further through 2nd challenge using a new dataset, Deep Diabetic Retinopathy Image Dataset (DeepDRiD). The challenge is subdivided into three tasks as follows: A. Dual-View Disease Grading: Classification of fundus images according to the severity level of diabetic retinopathy using dual view retinal fundus images. B. Image Quality Estimation: Fundus quality assessment for overall image quality, artifacts, clarity, and field definition. C. Transfer Learning: Explore the generalizability of a Diabetic Retinopathy (DR) grading system. The robust and generalizable models are expected to be developed to solve clinical issues in reality.

💻 Phitron Project 🚋 in C Programming 🚞 Through Real ✈ World Projects 🚀 Phitron course 🚂 projects written 🛳 in C designed ⛴ to build strong 🚙 fundamentals ⛽ through practical 🚢 coding challenges🚨and real 🏩 world problem 🏘 solving C 🏟 programming 🏥 concepts loops 🏰 arrays pointers 🍑 functions structures 🍅 and file 🥙 handling⚽

Solving Challenges in C Programming repository, featuring over 100+ curated resources to boost your skills. Crafted by Mahendra Mahara, explore challenges, PDFs, and mini projects. Contribute, connect, and stay updated to excel in C programming. Embark on a journey of learning and mastery.

🏆 Solutions for Azerbaijani Informatics Olympiad (RFO) - Competitive programming problems solved in Python and C++. Learning resources for algorithmic challenges and olympiad preparation.

⚡ Solutions to coding challenges on HackerRank (Python, Java, C++, SQL, Problem Solving & Math) <\>

Strengthen your C# skills with functional programming, test-driven techniques by solving modern C# challenges

In this code repository you can find my alternative solutions to all the coderbyte coding challenges that I have solved so far using modern C++ language features (C++11, C++14, C++ 17 language standards). For a couple of solutions I used a handful of C++ 17 language features as well in order to implement some template metaprogramming techniques).

As an Ignite Coder, solved numerous problems using C programming, demonstrating expertise in algorithms, problem-solving, and efficient coding for technical challenges.

🚀 Current DSA Explorer in C++ 🔍 Current Focus: Actively solving Data Structures and Algorithms problems on LeetCode and GeeksForGeeks. 📈 Progress Snapshot: Currently tackling challenges of varying difficulty levels for continuous skill enhancement. Focusing on efficient C++ solutions to sharpen problem-solving skills.

Leetcode and other coding challenges solved in Python, C++ and Go.

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Project Overview Welcome to the Convolutional Neural Networks (CNN) project in the AI Nanodegree! In this project, you will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed. Sample Output Along with exploring state-of-the-art CNN models for classification, you will make important design decisions about the user experience for your app. Our goal is that by completing this lab, you understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Your imperfect solution will nonetheless create a fun user experience! Project Instructions Instructions Clone the repository and navigate to the downloaded folder. git clone https://github.com/udacity/dog-project.git cd dog-project Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/dogImages. Download the human dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/lfw. If you are using a Windows machine, you are encouraged to use 7zip to extract the folder. Download the VGG-16 bottleneck features for the dog dataset. Place it in the repo, at location path/to/dog-project/bottleneck_features. (Optional) If you plan to install TensorFlow with GPU support on your local machine, follow the guide to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step. (Optional) If you are running the project on your local machine (and not using AWS), create (and activate) a new environment. Linux (to install with GPU support, change requirements/dog-linux.yml to requirements/dog-linux-gpu.yml): conda env create -f requirements/dog-linux.yml source activate dog-project Mac (to install with GPU support, change requirements/dog-mac.yml to requirements/dog-mac-gpu.yml): conda env create -f requirements/dog-mac.yml source activate dog-project NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/dog-windows.yml to requirements/dog-windows-gpu.yml): conda env create -f requirements/dog-windows.yml activate dog-project (Optional) If you are running the project on your local machine (and not using AWS) and Step 6 throws errors, try this alternative step to create your environment. Linux or Mac (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 source activate dog-project pip install -r requirements/requirements.txt NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 activate dog-project pip install -r requirements/requirements.txt (Optional) If you are using AWS, install Tensorflow. sudo python3 -m pip install -r requirements/requirements-gpu.txt Switch Keras backend to TensorFlow. Linux or Mac: KERAS_BACKEND=tensorflow python -c "from keras import backend" Windows: set KERAS_BACKEND=tensorflow python -c "from keras import backend" (Optional) If you are running the project on your local machine (and not using AWS), create an IPython kernel for the dog-project environment. python -m ipykernel install --user --name dog-project --display-name "dog-project" Open the notebook. jupyter notebook dog_app.ipynb (Optional) If you are running the project on your local machine (and not using AWS), before running code, change the kernel to match the dog-project environment by using the drop-down menu (Kernel > Change kernel > dog-project). Then, follow the instructions in the notebook. NOTE: While some code has already been implemented to get you started, you will need to implement additional functionality to successfully answer all of the questions included in the notebook. Unless requested, do not modify code that has already been included. Evaluation Your project will be reviewed by a Udacity reviewer against the CNN project rubric. Review this rubric thoroughly, and self-evaluate your project before submission. All criteria found in the rubric must meet specifications for you to pass. Project Submission When you are ready to submit your project, collect the following files and compress them into a single archive for upload: The dog_app.ipynb file with fully functional code, all code cells executed and displaying output, and all questions answered. An HTML or PDF export of the project notebook with the name report.html or report.pdf. Any additional images used for the project that were not supplied to you for the project. Please do not include the project data sets in the dogImages/ or lfw/ folders. Likewise, please do not include the bottleneck_features/ folder.

I have solved coding challenges in Codesignal/codefight in C++, Javascript, Java, Python, Go

md‑abdullah‑al‑maruf/beecrowd is a well-organized, beginner-friendly collection of C-language implementations of Beecrowd (URI) problems. It's ideal for those who want to practice problem-solving systematically, learn C syntax, and understand common patterns in coding challenges.

These are the C# Programs which I practice, learn and Solving Challenge files studied from different sources.

Contains solved coding challenges in C++ and Python

A repo based on the HackerRank Tutorials Course "Cracking the Coding Interview Challenges". All problems are solved by C++.

coding challenges, problem solving, data structures and algorithms in C++

Here you'll find coding challenges in C#. I hope it's useful for people who enjoy solving puzzles and learning new tricks along the way.

A repo based on the HackerRank Tutorials Course "10 Days of Statistics Challenges". All problems are solved by C++.

https://www.kaggle.com/c/jane-street-market-prediction/overview “Buy low, sell high.” It sounds so easy…. In reality, trading for profit has always been a difficult problem to solve, even more so in today’s fast-moving and complex financial markets. Electronic trading allows for thousands of transactions to occur within a fraction of a second, resulting in nearly unlimited opportunities to potentially find and take advantage of price differences in real time. In a perfectly efficient market, buyers and sellers would have all the agency and information needed to make rational trading decisions. As a result, products would always remain at their “fair values” and never be undervalued or overpriced. However, financial markets are not perfectly efficient in the real world. Developing trading strategies to identify and take advantage of inefficiencies is challenging. Even if a strategy is profitable now, it may not be in the future, and market volatility makes it impossible to predict the profitability of any given trade with certainty. As a result, it can be hard to distinguish good luck from having made a good trading decision. In the first three months of this challenge, you will build your own quantitative trading model to maximize returns using market data from a major global stock exchange. Next, you’ll test the predictiveness of your models against future market returns and receive feedback on the leaderboard. Your challenge will be to use the historical data, mathematical tools, and technological tools at your disposal to create a model that gets as close to certainty as possible. You will be presented with a number of potential trading opportunities, which your model must choose whether to accept or reject. In general, if one is able to generate a highly predictive model which selects the right trades to execute, they would also be playing an important role in sending the market signals that push prices closer to “fair” values. That is, a better model will mean the market will be more efficient going forward. However, developing good models will be challenging for many reasons, including a very low signal-to-noise ratio, potential redundancy, strong feature correlation, and difficulty of coming up with a proper mathematical formulation.

Recent development in Graphic Processing Units (GPUs) has opened a new challenge in harnessing their computing power as a new general-purpose computing paradigm with its CUDA parallel programming. However, porting applications to CUDA remains a challenge to average programmers. We have developed a restructuring software compiler (RT-CUDA) with best possible kernel optimizations to bridge the gap between high-level languages and the machine dependent CUDA environment. RT-CUDA is based upon a set of compiler optimizations. RT-CUDA takes a C-like program and convert it into an optimized CUDA kernel with user directives in a con.figuration .file for guiding the compiler. While the invocation of external libraries is not possible with OpenACC commercial compiler, RT-CUDA allows transparent invocation of the most optimized external math libraries like cuSparse and cuBLAS. For this, RT-CUDA uses interfacing APIs, error handling interpretation, and user transparent programming. This enables efficient design of linear algebra solvers (LAS). Evaluation of RT-CUDA has been performed on Tesla K20c GPU with a variety of basic linear algebra operators (M+, MM, MV, VV, etc.) as well as the programming of solvers of systems of linear equations like Jacobi and Conjugate Gradient. We obtained significant speedup over other compilers like OpenACC and GPGPU compilers. RT-CUDA facilitates the design of efficient parallel software for developing parallel simulators (reservoir simulators, molecular dynamics, etc.) which are critical for Oil & Gas industry. We expect RT-CUDA to be needed by many industries dealing with science and engineering simulation on massively parallel computers like NVIDIA GPUs.

A repo based on the HackerRank Tutorials Course "30 Days of Code Challenges". All problems are solved by C++.

Simple C++ bypass for Cloudflare's anti-bot / anti-ddos page (JavaScript challenge solver).

Research on solving TSP and mTSP problems using Visual Reasoning and Multi-Agent Approach in Multimodal Large Language Models

C implementation of the Parallel Crossbred algorithm for solving Fukuoka MQ challenges on GPUs.

Sudoku Solver in C#, as a Code Review . StackExchange weekend challenge

My (many) attempts to solve the 1brc (One Billion Row Challenge) in C

Collection of Data Structures and Algorithms (DSA) practice code in C++ for learning and problem-solving. Includes code snippets, explanations, and solutions to common DSA challenges.