Browsing: Guide

Image by Author   # Introduction   BitNet b1.58, developed by Microsoft researchers, is a native low-bit language model. It is trained from scratch using ternary weights…

In this tutorial, we build a complete pipeline for single-cell RNA sequencing analysis using Scanpy. We start by installing the required libraries and loading the PBMC…

Have you ever wanted faster type checking for Python without slowing down your workflow? Tools like MyPy can catch type errors, but they often feel slow or…

There’s a reason we’re called WIRED. If there’s one thing most of today’s gadgets have in common, it’s that they typically need to be plugged in…

Suppose there is a smart computer in your cell phone. It responds instantly, knows your language, and is completely functional even without the internet. This AI…

In this tutorial, we explore how we use Daft as a high-performance, Python-native data engine to build an end-to-end analytical pipeline. We start by loading a…

Image by Editor   # Introduction  Data science projects usually begin as exploratory Python notebooks but need to be moved to production settings at some stage, which…

In this tutorial, we design an end-to-end, production-style analytics and modeling pipeline using Vaex to operate efficiently on millions of rows without materializing data in memory.…

best_C = best[“params”][“C”] best_solver = best[“params”][“solver”] final_pipe = Pipeline([ (“scaler”, StandardScaler()), (“clf”, LogisticRegression( C=best_C, solver=best_solver, penalty=”l2″, max_iter=2000, random_state=42 )) ]) with mlflow.start_run(run_name=”final_model_run”) as final_run: final_pipe.fit(X_train, y_train)…

Try the following – ask any Excel user for his/ her favourite Excel formula. More often than not, you will hear just this one name -VLOOKUP.…