October 2023

Label encoding is a data preprocessing technique used in machine learning to convert categorical values into numerical form, facilitating their use in algorithms that require numerical input. In label encoding, each category is assigned a unique integer based on alphabetical ordering, frequency, or any other criteria deemed appropriate for the task at hand. For instance, […]

Utilizing GPUs in machine learning is highly advantageous because they are designed to handle multiple operations simultaneously, offering a substantial boost in computational power, especially for tasks that can be parallelized. Machine learning models, particularly deep learning networks, involve a multitude of matrix multiplications and other calculations that are inherently parallelizable. A GPU, with its

By reading this article, you will learn the best way to convert a scikit-learn dataset to a pandas DataFrame object in Python. If you only want the quick 10 second instructions, here they are: Scikit-learn, often referred to as sklearn, is an open-source machine learning library in Python that provides simple and efficient tools for

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.

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

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

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

What is gradient descent? The short answer is, that it is an iterative algorithm for finding a local minimum of any function that is ‘smooth enough’ (a bit more precise definition for the smoothness is that the function is continuously differentiable). Another way to answer the same question briefly is by saying that gradient descent