Some deep dives, some quick thoughts.
 Vector Databases AI systems are incredible—until you hit a data retrieval bottleneck. You'd need to search through millions of high-dimensional vectors, fast. Vector databases are built specifically for similarity search, allowing AI apps to find th...
NVIDIA’s Latest Developer Kit Makes Advanced AI Affordable
The architecture behind GPT, Gemini, and Sora
In the world of robotics, humanoid designs often get the spotlight, but functionality tells a different story.
A Beginner's Guide to Interactive 3D Visualization
Breaking Down How Tesla Turns Algorithms into Real-World Self-Driving Systems
If you’ve been working with LLMs like GPT, LLaMA, or any of their cousins, you know how powerful they are. But you also know how messy things can get when you’re trying to wrangle their outputs into something structured and usable. Pydantic AI is a game-changer for anyone looking to bring order to t...
Understanding the critical components that make SpaceX's advancements possible
Discovering the technology behind timing systems and how it relates to coding and data analysis
 I recently took a deep dive into the YOLO (You Only Look Once) architecture. Originally introduced by Joseph Redmon in 2015, I think it completely changed the game for object detection. Instead of treating detection as a classification task on multiple regions, YOLO do...
Let's walk through how to build a CNN from scratch using TensorFlow and Keras to classify images from the CIFAR-10 dataset. CIFAR-10 is a dataset containing 60,000 color images (32x32 pixels) across 10 classes. python import tensorflow as tf from tensorflow.keras import layers, models from tensorflo...
I learned that gradient boosting is like training a student with a series of tutors. Each tutor focuses on the student’s weak spots, helping them improve step by step. In machine learning, gradient boosting works by training weak models (typically decision trees) sequentially, where each new model l...
Let's use DeepLabV3's semantic segmentation to remove the background from a video and replace it with a new background. python import cv2 import torch import torchvision.transforms as transforms from torchvision.models.segmentation import deeplabv3_resnet101 import numpy as np from IPython.display ...
This cheat sheet provides a quick reference for common NLTP tasks using NLTK and spaCy in Python. I. NLTK (Natural Language Toolkit) python import nltk from nltk.tokenize import word_tokenize, sent_tokenize from nltk.corpus import stopwords from nltk.stem import PorterStemmer, WordNetLemmatizer fr...
Continuing from the previous issue on data preprocessing, introducing scikit-learn's Pipeline class. Pipeline is a powerful tool for streamlining machine learning workflows. It allows you to chain multiple data processing steps and a final estimator (model) into a single object. This simplifies the ...
I learned that a huge chunk of a machine learning engineer’s time isn’t spent on building fancy models. it’s spent cleaning and prepping data. It is not so glamorous. But it is actually the secret sauce to making models perform well. This process, called preprocessing, involves filling in missing va...
python import tensorflow as tf from tensorflow.keras import layers, models from tensorflow.keras.datasets import mnist from tensorflow.keras.utils import to_categorical python Load the MNIST dataset (train_images, train_labels), (test_images, test_labels) = mnist.load_data() Preprocess train_i...
python import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader import torchvision.transforms as transforms import torchvision.datasets as datasets python Define a transform to normalize the data transform = transforms.Compose([ transforms.ToTens...
 Behind the scenes, companies track and analyze customer interactions to improve recommendations, optimize user experience, and ultimately, boost sales. The backbone of this system are these five: - Apache Kafka - Apache Hadoop - Apache Spark - Apache Airflow - Elastics...
Window functions are a powerful feature in databases that allow you to perform calculations across a set of rows related to the current row. They are commonly used in SQL queries to analyze and aggregate data within a specific window or range. Syntax The basic syntax for using window functions is...