October 2023

Photo of a futuristic office desk in a dimly lit room. Dominating the scene is a sleek monitor that takes up more than 50% of the frame. The monitor displays a brightly glowing bar, separated into two parts by a line. The larger section is a distinct color and is labeled 'Train', while the smaller section has a different color and is labeled 'Test'. The ambient light from the monitor casts a soft glow on its surroundings, causing the surface of the desk to reflect its light.

How to Split a Pandas Dataframe Randomly into Train and Test Sets with Scikit-Learn and Python

By reading this article, you will learn how to use the train_test_split() -function from the scikit-learn library to split your Pandas Dataframe dataset into train and test sets. The training set can then be utilized for training machine learning models, and the test set can be used to test the performance/accuracy of the resulting model. […]

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Save and Load Models to Disk in Keras, Python: A Complete Guide

In machine/deep learning, it’s essential to save your trained models so you can reuse, share, or deploy them without wasting time and computational resources to retrain them from scratch. In Keras, one of the most popular deep-learning libraries, this process is both straightforward and versatile. This article will guide you through the process of saving

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Save and Load Models to Disk in PyTorch, Python: A Complete Guide

Learn the essentials of saving and loading models in PyTorch with our complete guide. Dive into the details of handling the state_dict and understand its pivotal role in managing model parameters, vital for tasks like transfer learning and model sharing. Grasp practical insights into saving classifiers for inference, ensuring a smooth transition from training to

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Save and Load Models on Disk in Scikit-Learn, Python: A Complete Guide

Learn the necessities of model management with our guide. This article navigates through the methodical waters of saving your trained Scikit-Learn classifiers using two separate libraries: Pickle and Joblib. Anticipate a deep dive into the pros, cons, and how-tos of each method, ensuring your models are not only securely stored but also readily accessible for

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