Here is where all my notebooks from Google Colab and Kaggle meet! (It even includes local notebooks) Also, GitHub is more suitable for tweaking than other platforms... The reason for creating most of these notebooks is curiosity, which sometimes goes to YouTube for Persian speaking audience!
I preferred it to be categorized in this section rather than creating a separate folder... Now the name of the notebook (which may be different in different places) is mentioned along with a short description to make the choice easier.
Note: Sometimes it is difficult to classify because a notebook can contain several subjects...
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Exploratory data analysis on Titanic dataset: Nothing, just a simple EDA along with building models using FLAML on one of the most well-known datasets!
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Exploring the car evaluation dataset: Another well-known dataset that we want to model using an AutoML framework...
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Conditional formatting in Pandas: Style your tables and make them more attractive with these simple steps! — Exploration in the dataset of used cars
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Working with large datasets in Pandas: Pandas is not ideal for big data, but there are ways to improve performance.
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Netflix EDA: Analyzing an almost famous Netflix dataset inspired by different ideas...
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Flagpedia web scraping: Just a simple project for my YouTube channel.
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Scraping an Iranian sock store: Just to practice how to use hidden APIs.
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Cleaning data using shell scripting: In this notebook, we try to clean a dirty dataset with some basic Linux (Unix family in general) tools.
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Analysis of mobile phone price: Cleaning and exploring inside the mobile phone price dataset.
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Developers tech stack scraper: A scraper to extract interesting data from software developers!
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Geany themes scraper: Save time and energy by automatically downloading something!
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Parts of a failed project: Three parts of a cryptocurrency trader bot project...
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About meta-heuristic algorithms: Papers related to meta-heuristic algorithms...
- Accuracy score measuring: Implementation of some well-known metrics to get the accuracy of your model.
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Multi-layer perceptron from scratch: A good and useful experience of how a neural network works by implementing it after researching from different sources, which took about a week!
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Logic gates in PyTorch: Implementation of some logic gates such as XOR, OR, and AND in PyTorch.
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Convolutional neural network for MNIST: Building a basic CNN in PyTorch for handwritten digit recognition (with detailed explanations).
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ZIP code reader using CNN: Moving beyond single digits! This project simulates ZIP codes by concatenating MNIST images and builds a CNN in PyTorch to recognize the full sequence at once.
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Colorization GAN: Image colorization using a Generative Adversarial Network trained on CIFAR-10.
- Merge sort: Merge sort is one of the fastest comparison-based sorting algorithms, which works on the principle of the divide and conquer approach.
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Morse code translator: A tool to encode your messages into Morse code and decode it.
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Lexical analyzer: Simply define your programming language tokens and then tokenize your code with it.
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Balanced punctuations: Find out if the punctuations are pair or not!
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Gradio demo: Gradio is one of the fastest ways to make a quick demo for your damn functions with a friendly web interface so that anyone can use it!
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How to generate fake data: Here we will learn how to add some fake data to the PostgreSQL database to make us look more cool!
No license specified for this project yet.