-
I'm a fresh graduate and I want to become a data scientist or machine learning engineer. Can you please give me some guidance? I've been working as a software engineer for the last two years, but now I want to switch to data science. Can you help or share some guidance? I've been applying to many data science jobs but …
Read More -
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 LLM is a machine learning model trained on massive amounts of text using transformer-based architectures (or their variations). These models can …
Read More -
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 "Attention Is All You Need" by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia …
Read More -
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 the model's bias and variance, known as the bias-variance trade-off. These two types of errors can affect a model's performance. Bias, the first type of …
Read More -
Before jumping into Deep Learning, one must know the classical/traditional Machine Learning algorithms, because understanding traditional machine learning algorithms can provide a strong foundation in machine learning concepts. These algorithms often involve simple, intuitive concepts that can be helpful in …
Read More -
There are 4 main approaches you can consider for model compression. They are: Quantization Pruning Knowledge Distillation Low-Rank Factorization Quantization Quantization is the most general and commonly used model compression method. Quantization reduces a model’s size by using fewer bits to represent its parameters. …
Read More -
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, consider a data set containing two features, age(x1) and income(x2), where age ranges from 0–100, while income ranges from 0–20,000 and higher. Income …
Read More -
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 background. There are two main approaches to image segmentation: one-stage and two-stage. One-stage image segmentation methods aim to directly predict a …
Read More -
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 learning for research typically focuses on exploring new ideas and techniques, and on advancing the state of the art in the field. In contrast, machine …
Read More -
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 your own computation graphs, while Keras is a high-level framework that provides a pre-defined set of layers and routines for building deep learning …
Read More -
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 amount of text data, including books, articles, and websites, to generate coherent and relevant text based on a given context. GPT-3 is a …
Read More -
What is Vanishing Gradient Problem Neural networks are trained using stochastic gradient descent. This involves first calculating the prediction error made by the model and using the error to estimate a gradient used to update each weight in the network so that less error is made next time. This error gradient is …
Read More -
There are several types of ensemble techniques in machine learning, including: Bagging, Boosting, Stacking, Blending, Bootstrapped ensembles, Bayesian model averaging. Bagging Bagging (short for bootstrapped aggregating) is an ensemble technique that involves training multiple models on different subsets of the …
Read More -
Decision Tree, Random Forest (RF), and Gradient Boosting (GB) are three popular algorithms used for supervised learning tasks such as classification and regression. In this blog, we will compare these three algorithms in terms of their features, performance, and usability. Decision Tree is a simple and intuitive …
Read More -
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 some common text cleaning techniques and how to apply them to your text data. The first step in cleaning text for NLP is to remove any noisy or …
Read More -
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 systems include: Content-based recommender systems: These systems recommend items to users based on the characteristics of the items themselves. For …
Read More -
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 transfer learning is that a model that has already learned to recognize patterns in one set of data can be applied to a different but related problem, …
Read More