Dawit Asmero's repositories
30-Days-Of-Python
30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace.
davedawitdave
Config files for my GitHub profile.
Python
All Algorithms implemented in Python
awesome-python
A curated list of awesome Python frameworks, libraries, software and resources
DataFrame
C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage
Python-Roadmap
Python Roadmap. Learn Python programming as your first programming language. Python for Absolute Beginners, Non-Tech Professionals, 15+ Projects, 30 Topics, 500+ Practice Questions, with Data Structures & Algorithms
qutip-trail
QuTiP: Quantum Toolbox in Python
LLM
Large Language Models coupled with multiple AI capabilities to generate images and text as well as achieving human level performance on a number of tasks, the world is going through revolution in art (DALL-E, MidJourney, Imagine, etc.), science (AlphaFold), medicine, and other key areas. In-context learning, popularized by the team behind the GPT-3 LLM, brought a new revolution for using LLMs in so many tasks that the LLM is not trained for. This is in contrast to the usual fine-tuning that was required to equip AI models to perform better in tasks they are not trained for. With In-context learning, LLMs are able to readjust their performance on a task depending on the prompt - from structured input that can be considered partly a few-shot training and partly a test input. This has opened up many applications. This week’s challenge is to systematically explore strategies that help generate prompts for LLMs to do classification of web pages according to a few examples of human scores. You will be also required to compare responses and accuracies of multiple LLM models for a given promp
A_B-Hypothesis-Testing
An advertising company is running an online ad for a client with the intention of increasing brand awareness. The advertiser company earns money by charging the client based on user engagements with the ad it designed and serves via different platforms. To increase its market competitiveness, the advertising company provides a further service that quantifies the increase in brand awareness as a result of the ads it shows to online users. The main objective of this project is to test if the ads that the advertising company runs resulted in a significant lift in brand awareness.
Twitter-Data-Analysis
extracting twitter data
handson-ml
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
turkish-tweets-sentiment-analysis
This sentiment analysis project determines whether the tweets posted in the Turkish language on Twitter are positive or negative.