Understanding Machine Learning From Theory to Algorithms
Master machine learning from theory to practice with detailed explanations of core algorithms, evaluation techniques, and a ready-to-use cheat sheet.
Artificial Intelligence (AI)
Explore core AI concepts, industry applications, trends, and tools that power intelligent systems in today’s tech landscape.
3D Computer Vision: Depth Estimation, Point Cloud Processing, and SLAM
3D computer vision: Learn how to recover depth, process point clouds, and implement SLAM systems to enable 3D understanding in robotics, AR/VR, and autonomous applications.
Deep Learning for Video Analytics: Object Tracking, Action Recognition, and Event Detection
Learn how to build deep-learning pipelines for object tracking, action recognition, and event detection in video. This comprehensive guide covers methods, architectures, datasets, metrics, and deployment best practices.
What is Unsupervised Learning?
Discover What is Unsupervised Learning? how unsupervised learning finds hidden patterns in unlabeled data through clustering, association rules, dimensionality reduction, and anomaly detection.
The Ultimate Guide to Artificial Intelligence: Concepts, Applications, and Future Trends
Explore the comprehensive landscape of Artificial Intelligence history, core techniques (ML, deep learning), real-world applications, ethics, tools, and future trends in this comprehensive guide.
MLOps 101: Bringing Machine Learning into Production
MLOps 101: Learn how to operationalize machine learning with MLOps best practices: data ingestion, CI/CD, model deployment, monitoring, and continuous retraining to keep your models reliable and performant.
Getting Started with Computer Vision: Techniques, Tools & Real-World Applications
Learn how to process, model, and deploy computer vision systems from classical filtering and feature extraction to advanced CNNs, YOLO detection, and Vision Transformers in this comprehensive guide.
Language Models Demystified: How GPT, BERT & Friends Work
Explore the mechanics and evolution of modern language models from n-grams and RNNs to transformers like GPT and BERT in this comprehensive guide covering theory, code examples, case studies, and deployment best practices.
What is Training Data in AI? Explained with Simple, Clear Examples
Training data is the foundation of every AI model. Learn what it is, how it’s used, and why quality data is critical for machine learning and fairness.
NLP 101: From Text Preprocessing to Transformer Models
Unlock the fundamentals of NLP from text preprocessing and feature representation to transformer architectures and deployment in this comprehensive guide.