Ashim Lamichhane's repositories
dataStructureAndAlgorithm
Data Structure using C and C++ | Design And Analysis of Algorithms
applied-ml
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
awesome-public-datasets
A topic-centric list of HQ open datasets.
core
:house_with_garden: Open source home automation that puts local control and privacy first.
Data-Analyst-Portfolio-Projects
This Repo Contain Data Analyst Projects
Data-science
Collection of useful data science topics along with code and articles
deeplearning-models
A collection of various deep learning architectures, models, and tips
Django-CRM
Open Source CRM based on Django
election-import-nepal
importing json to mysql
gpt-engineer
Specify what you want it to build, the AI asks for clarification, and then builds it.
Grokking-Deep-Learning
this repository accompanies the book "Grokking Deep Learning"
Inventory-Management-System-Django
A Inventory management system written in DJango
machine-learning-systems-design
A booklet on machine learning systems design with exercises
machinelearning
Machine learning and artificial intelligence
MarketAnalysis
Portfolio Theory, Options Theory, & Quant Finance
ML-Course-Notes
🎓 Sharing course notes on all topics related to machine learning, NLP, and AI.
nepse-api
📈 Nepse daily data API (Updated ✌)
Open-Source-Python-POS-and-Accounting-Software
Open source software for POS and Automated Accounting.
RSPapers
A Curated List of Must-read Papers on Recommender System.
sharesansar_datascrape
Sharesansar Nepal NEPSE daily share price data scraping with Python. Scrapes all daily floor sheet from sharesansar site.
simple-stock-management
Server component of the Simple Stock Management stock & inventory web app. Designed for small businesses & non-profits.
Stats-Maths-with-Python
General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python
stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.