#deep-learning

Overcoming RNN's 'dementia' and how Google flipped the world with 'Attention Is All You Need'. From Query-Key-Value library analogies to Multi-Head Attention and Vision Transformers.

I share my experience with overfitting in machine learning. I was fooled by 99% training accuracy, only to fail in production. Learn how I used Dropout, Regularization, and Data Augmentation to build 'real intelligence' instead of a memorization machine.

Understanding Transformer architecture through practical experience

Understanding RNN and LSTM principles through practical project experience

Understanding neural network principles through practical project experience. From factory line analogies to backpropagation and hyperparameter tuning.

Understanding AI, Machine Learning, Deep Learning, and Generative AI. Deep dive into Transformer architecture, RAG vs Fine-tuning, Ethical AI, and a practical roadmap for developers transitioning to AI Engineering.

Unlock the secrets of Computer Vision. A comprehensive guide to CNN architecture: Convolution, Pooling, Padding, and Stride explained simply. Learn how networks like AlexNet and ResNet revolutionized AI, and discover how machines leverage hierarchical feature extraction to 'see' the world, from identifying cats to driving cars.