emlearn is an open-source machine learning inference engine designed for microcontrollers and embedded devices. It supports various machine learning models for classification, regression, unsupervised learning, and feature extraction. The engine is portable, with a single header file include, and uses C99 code and static memory allocation. Users can train models in Python and convert them to C code for inference.
Scrapegraph-ai is a Python library for web scraping using AI. It provides a SmartScraper class that allows users to extract information from websites using a prompt. The library uses LLM models like Ollama, OpenAI, Azure, Gemini, and others for information extraction.
A hands-on tutorial in Python for sensor engineers on Bayesian sensor calibration, which combines statistical models and data to optimally calibrate sensors. This technique is crucial in engineering to minimize sensor measurement uncertainty. The tutorial provides Python code to perform such calibration numerically using existing libraries.
This article explains why Convolutional Neural Networks (CNN) are useful for function identification in reverse engineering, and provides a Python implementation using PyTorch to identify the start of functions in binaries.
How to use Burr, an open source framework using simple OpenAI client calls to GPT4, and FastAPI to create a custom email assistant agent. We’ll describe the challenge one faces and then how you can solve for them.
This article explains permutation feature importance (PFI), a popular method for understanding feature importance in explainable AI. The author walks through calculating PFI from scratch using Python and XGBoost, discussing the rationale behind the method and its limitations.
Intro to Streamlit
- Simple and complex Streamlit example
- Data and state management in Streamlit apps
- Data widgets for Streamlit apps
- Deploying Streamlit apps
- Simple and complex Streamlit example
- Data and state management in Streamlit apps
- Data widgets for Streamlit apps
- Deploying Streamlit apps
import EasyCrypt
keystring = "SixteenByteKey!!"
inpstring = "Some super secret string, that I don't want you to see."
# This is the initialisation vector/nonce. I generated it with the below code. As you
# will need it to decrypt later on, you might want to store it and not just generate it each time
# I just generated it like this and printed this one out to store it.
#
# import os
# from binascii import hexlify, unhexlify
# ivstring = hexlify(os.urandom(16)).decode()
ivstring = "aba0a3bde34a03487eda3ec96d5736a8"
crypted = EasyCrypt.encrypt_string(keystring, inpstring, ivstring)
print(crypted)
decrypted = EasyCrypt.decrypt_string(keystring, crypted, ivstring)
print(decrypted)
keystring = "SixteenByteKey!!"
inpstring = "Some super secret string, that I don't want you to see."
# This is the initialisation vector/nonce. I generated it with the below code. As you
# will need it to decrypt later on, you might want to store it and not just generate it each time
# I just generated it like this and printed this one out to store it.
#
# import os
# from binascii import hexlify, unhexlify
# ivstring = hexlify(os.urandom(16)).decode()
ivstring = "aba0a3bde34a03487eda3ec96d5736a8"
crypted = EasyCrypt.encrypt_string(keystring, inpstring, ivstring)
print(crypted)
decrypted = EasyCrypt.decrypt_string(keystring, crypted, ivstring)
print(decrypted)
