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Unleashing the Full Potential of Deep Learning Models: A Guide to Quantization Techniques
Photo by Lucas Pezeta Precision at a fraction of the size: Experience the power of quantization for your deep learning models Introduction Model quantization is a technique for reducing the precision of the weights and activations of a neural network model. This process can be used to decrease the model’s memory footprint and computational complexity,…
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A vaccine has been launched for beginners in the AI domain
A solution to the problem of facing a problem properly, without letting feel a problem as a problem. Problem at a glance A lot of difficulties are faced by beginners in AIML and Data Science. They don’t get proper directions on how to deal with a specific problem and hence they get frustrated and are not…
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Entropy and Information gain
Photo by Kyle Roxas Some moments occur when we notice that we have seen this term somewhere and we don’t remember what it exactly means. Entropy and information gain belong to the same categories of terminologies. We will try to understand entropy and information gain in a very simple and uncomplicated way. A basic understanding…
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Shout out to GAN (Start ver.)
Writing after a long time, Here’s the basic + in-depth mix explanation of Generative adversarial networks and Style-Based Generative Adversarial networks. Introduction GAN is simply a generative model that generates new data from the input data. They are used to perform unsupervised operations. They work majorly with image data and also audio data. The Generative…
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Logistic regression (Lucid Explanation)
Let’s understand the logistic regression without any elaboration and in a short manner, that won’t waste your time rather than it will deliver you just the needed information. So don’t even have a cup of coffee with you, before sipping the coffee you’ll grasp logistic regression. Introduction Logistic regression is the foundational algorithm essential for…
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