Search Results for "morse code practice" - Page 2

ENAS in PyTorch is a PyTorch implementation of Efficient Neural Architecture Search (ENAS), a method that automates the design of neural network architectures through reinforcement learning and parameter sharing. The repository demonstrates how a controller network can explore a large search space and discover high-performing architectures while dramatically reducing the computational cost traditionally associated with neural architecture search. It is primarily intended as a research and...

...It implements the core logic needed to train an agent using Q-learning with neural networks (i.e. approximating Q-values via deep nets), setting up environment interaction loops, experience replay, network updates, and policy behavior. For learners and researchers interested in reinforcement learning, this repo offers a concrete, runnable example bridging theory and practice: you can execute the code, play with hyperparameters, observe convergence behavior, and see how deep Q-learning learns policies over time in standard environments. Because it’s self-contained and Python-based, it's well-suited for experimentation, modifications, or extension — for instance adapting to custom Gym environments, tweaking network architecture, or combining with other RL techniques.

...It documents that runs are non-deterministic due to certain GPU operations and reports a median accuracy over multiple trials that is slightly below the single-run result in the paper, reflecting expected variance in practice. The project ships lightweight training, data, and analysis scripts, keeping the footprint small while making the experimental pipeline transparent. It is provided as archived, research-grade code intended for replication and study rather than continuous development.

Lihang is an open-source repository that provides educational notes, mathematical derivations, and code implementations based on the book Statistical Learning Methods by Li Hang. The repository aims to help readers understand the theoretical foundations of machine learning algorithms through practical implementations and detailed explanations. It includes notebooks and scripts that demonstrate how key algorithms such as perceptrons, decision trees, logistic regression, support vector machines, and hidden Markov models work in practice.

...This separation lets the model reason about geometry and composition before committing to texture and color, improving spatial fidelity. The repository includes training code, datasets, and evaluation scripts so researchers can reproduce baselines and extend components such as the graph encoder or image generator. In practice, sg2im demonstrates how structured semantics can guide generative models to produce controllable, compositional imagery.

In practice, a higher mAP value indicates a better performance of your neural net, given your ground truth and set of classes. The performance of your neural net will be judged using the mAP criteria defined in the PASCAL VOC 2012 competition. We simply adapted the official Matlab code into Python (in our tests they both give the same results).

...The project focuses on making HanLP’s capabilities accessible through a Python-friendly API surface, so you can integrate NLP steps into data pipelines, notebooks, and downstream ML or information-extraction code. In practice, it serves as a bridge layer: Python calls are translated into the corresponding HanLP operations, so you can keep your application logic in Python while relying on HanLP’s implementations. It is especially useful when you need a pragmatic “get results quickly” NLP layer for segmentation, tagging, entity extraction, parsing, or keyword-style tasks rather than experimenting with model training from scratch.

This is a project I started to learn Python and Tkinter. It all started when I received a PI for Christmas in 2015. Over Christmas breaks I have time away from work to play on projects and I have taken an interest in programming. There are many fine ham radio programs out there. This is just a learning tool. The code was written in Python2.7 and bundled with Pyinstaller. This bundle works for Windows 7 and Windows 10. CWOnly has two screen. The main screen is for operation. There...

AWK~plus is the next generation script practice environment. The AWK Language specifications and a main extension of GNU GAWK. Combination of Dynamic and Static typing. Parallel computing that a lock is free, and is thread safe at a language level.

Software Defined Radio Transceiver project is written in Python while using GNU Radio and USRP. The goal is to create a software HF transceiver with various modes. Go here for more info: http://apps.sourceforge.net/mediawiki/sdr/index.php?title=Main_Page

Cross-platform trainer and library for Morse Code. Features two parts: GUI Morse trainer, and Python library. Library can be downloaded separately and be used in your programs. Library outputs wav files or raw audio data. Regular and Farnsworth modes.

PyMorseRSS is a project using Python to generate Morse code practice files and making them available via RSS. The Morse code practice files are generated according to the Koch method using Farnsworth timing and both MP3 and OGG outputs are supported.

Convert text to International Morse Code. Input is ASCII text. Output can be: - . -..- - on the console, raw 8bit PCM suitable for piping to /dev/audio, .wav files or even (mp3|ogg). Good for headlines on your MP3 player or code practice.

This small python script will generate mp3 and corresponding text files for learning / training the morse code. It follows the method of Koch for teaching the characters and the method described in N0HFFs book for improving the skills even more.