Today’s digital landscape has never been so diverse. Every individual and company selects their preferred tools and operating systems, creating a diverse technological system. However, this diversity often leads to compatibility issues, making it hard to ensure application performance across different environments. This is where Docker plays a key role as an indispensable tool for
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Photography has been around for more than a century. There are many old photos around, and probably your family has some, too. Limited by the camera and film of the time, you may have photos of low resolution, blurry, or with folds or scratches. Restoring these old photos and making them like new ones taken
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Using machine learning to solve real-world problems is exciting. But most eager beginners jump straight to model building—overlooking the fundamentals—resulting in models that aren’t very helpful. From understanding the data to choosing the best machine learning model for the problem, there are some common mistakes that beginners often tend to make. But before we go
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Feature engineering is an important step in the machine learning pipeline. It is the process of transforming data in its native format into meaningful features to help the machine learning model learn better from the data. If done right, feature engineering can significantly enhance the performance of machine learning algorithms. Beyond the basics of understanding
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Deep learning is a subset of machine learning that has become a cornerstone in many technological breakthroughs. At the core of deep learning, it’s a model inspired by the human brain, which we call a neural network. Contrary to the traditional machine learning model, deep learning can automatically find feature representations from data. That’s why
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In machine learning projects, achieving optimal model performance requires paying attention to various steps in the training process. But before focusing on the technical aspects of model training, it is important to define the problem, understand the context, and analyze the dataset in detail. Once you have a solid grasp of the problem and data,
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Reinforcement Learning (RL) is a type of machine learning. It trains an agent to make decisions by interacting with an environment. This article covers the basic concepts of RL. These include states, actions, rewards, policies, and the Markov Decision Process (MDP). By the end, you will understand how RL works. You will also learn how
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Many people write in their jobs. Not everyone is a novel writer; some write technical documentation, business plans, news articles, and even blog posts. In those writings, illustrations are not essential but often good to have. They are decorations, interpretations, or visual explanations of the text. However, you probably do not want to spend too
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Introduction Choosing the right machine learning model for your data is of major importance in any data science project. The model you select will have a significant impact on the insights you derive from your data, and ultimately determine the usefulness of a project. In this article, we aim to provide practical tips to help
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Introduction Here’s a something that new machine learning practitioners figure out almost immediately: not all datasets are created equal. It may now seem obvious to you, but had you considered this before undertaking machine learning projects on a real world dataset? As an example of a single class vastly outnumbering the rest, take for instance
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