-
What are some common challenges faced in a data science project? Finding and acquiring relevant and high-quality data: This can be a challenge because data is often scattered across multiple sources and may be in different formats. Data scientists must be able to find and access the data they need, and then clean and …
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 -
Blind-75 is curated list of 75 LeetCode Problems created by Yangshun Tay. The list can be found here. I solved all 75 problems using Python. I am providing all my solutions with their time and space complexities below. All codes can be found in this GitHub repository. Two Sum Given an array of integers nums and an …
Read More -
What is System Design in Software Engineering? In software engineering, system design is the process of defining the architecture, modules, interfaces, and data for a system to satisfy specified requirements. System design is a critical part of the software development process, as it provides a blueprint for how the …
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 -
Suppose you have a script in the Google Cloud’s VM Instance that needs to run every day/week/month at a particular time ( e.g., a data pipeline for Machine Learning model training, data crawling, etc.). In that case, this article will guide you through automating the task. Different ways you can automate a script on …
Read More -
Google Cloud Platform (GCP) offers a suite of powerful tools and services for building, training, and deploying machine learning models. One of these tools is Vertex AI, a fully managed platform for building and deploying machine learning models at scale. Vertex AI simplifies the process of training, evaluating, and …
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 -
Transformer-based models are a type 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 -
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 -
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 transformer-based model, which means that it …
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 -
Combine two lists as a dictionary | dict(zip) Program 1keys = ['a', 'b', 'c'] 2values = [1, 2, 3] 3dictionary = dict(zip(keys, values)) 4print(dictionary) Output 1{'a': 1, 'b': 2, 'c': 3} Program 1keys = ('name', 'age', 'location') 2values = …
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