Let's first break it down: what exactly are large language models (LLMs), why do we call them 'large,' and how are they different from other types of language models? An …

Transformer-based models are a types of neural network architecture that uses self-attention mechanisms to process input data. They were introduced in the paper …

Can you explain the bias-variance trade-off and how it relates to model performance? Machine learning and statistics have a fundamental concept that requires balancing …

Before jumping into Deep Learning, one must know the classical/traditional Machine Learning algorithms, because understanding traditional machine learning algorithms can …

There are 4 main approaches you can consider for model compression. They are: Quantization Pruning Knowledge Distillation Low-Rank Factorization Quantization Quantization …

Why do we scale features? For machine learning, every dataset does not require feature scaling, and it is only needed when features have different ranges. For example, …

In computer vision, image segmentation refers to the process of dividing an image into distinct regions or segments, each corresponding to a different object or …

There are several key differences between using machine learning for research and using it for production. One of the main differences is the focus of the work. Machine …

PyTorch and Keras are both open-source deep learning frameworks, but they have some significant differences. PyTorch is a low-level framework that allows you to define …

Author: Sadman Kabir Soumik GPT-3, or Generative Pretrained Transformer 3, is a state-of-the-art language model developed by OpenAI. It has been trained on a massive …

What is Vanishing Gradient Problem Neural networks are trained using stochastic gradient descent. This involves first calculating the prediction error made by the model …

There are several types of ensemble techniques in machine learning, including: Bagging, Boosting, Stacking, Blending, Bootstrapped ensembles, Bayesian model averaging. …

Decision Tree, Random Forest (RF), and Gradient Boosting (GB) are three popular algorithms used for supervised learning tasks such as classification and regression. In …

Cleaning text for natural language processing (NLP) tasks is an important step that can help improve the performance of your model. In this blog post, we will discuss …

There are several different types of recommender systems, each with its own unique characteristics and applications. Some of the most commonly used types of recommender …

Transfer learning is a technique in machine learning that allows a model trained on one task to be reused and fine-tuned for another similar task. The idea behind …