Rahul Das RahulDas-dev

Managing Multiple Python Versions in Windows Terminal

This is a useful approach for switching between Python versions easily. I've organized the steps for better clarity:

Step 1: Remove Python Paths from PATH Variable.

Remove all existing Python installation paths from the system path and your accounts path variable.

Step 2: Create Bat Files for Each Python Version.

Create a bat file (e.g., python_39.bat) for each Python version.

Save or Load Complex nested Pydantic model into or from sqlalclemy-fileds

This gist demonstrates a custom SQLAlchemy column type that allows you to save and load Pydantic models directly in your SQLAlchemy database. It simplifies the process of storing and retrieving complex pydantic data models in your database without manual conversion to JSON or dictionaries.

kindly Check Git Repo or follow steps bellow

Steps

Building custom Column Type
Building a nested data structure using Pydantic model
Building a Sqlalchemy Table models using 1 and 2

‼️ Links to Useful Books on Rust + List of Useful Resources for Learning Rust

`Copied from Udemy Course` Rust Programming Course: From Beginner to Expert by Nouman Azam 😉

🦀 Resources Covering Basic Concepts of the Language 🦀

Rust Programming Language,

Online Link: https://doc.rust-lang.org/book/

Loading dataset

import pandas as pd
from sklearn.datasets import  fetch_openml

x = fetch_openml(data_id=1461, as_frame=True, parser='pandas') 
dataset = x['frame']
print(f'dataset Shape {dataset.shape}')
dataset.head()

	import numpy as np
	import seaborn as sns
	from matplotlib import pyplot as plt
	from matplotlib.offsetbox import AnchoredText
	from scipy import stats


	from ipywidgets import interact

	import numpy as np

	# Frobenius norm (X) = Tr(X.X_T) X_T = Transpose of X
	# d(Frobenius norm)/dX = 2X


	# M ~ L*R
	# R = l*r - M
	# Loss_fuction = NORM(R) to be minimized
	# Loss_fuction = NORM(R)

	import numpy as np

	### Loss function : L(w₁,w₂) = 0.75(w₁ −2)² + 0.35(w₂−4)²
	### Gradient of Loss function : ∇ L = [0.75×2×(w₁ −2), 0.35×2×(w₂−4)]ᵀ
	### Hyper Parameters : Learning Rate η = 0.1 , iteration or epoch N = 100

	loss_f = lambda w1,w2: 0.75(w1-2)2 + 0.35(w2-4)**2
	grad_f = lambda w1,w2: np.array([1.5(w1-2),0.7(w2-4)])

	eta, epoch = 0.1 ,100