Browse our interactive tools categorized for easy navigation and learning.
Understand how to normalize and standardize data for AI models.
Dive into linear regression concepts with interactive visualizations.
Discover how loss functions and metrics are used to evaluate AI models.
Visualize how gradient descent optimizes AI models.
Learn how SGD works and why it's important for training neural networks.
Get introduced to tensors, the building blocks of modern AI.
Learn the basics of perceptrons, the building blocks of neural networks.
Understand how neural networks learn through backpropagation.
Explore the architecture of multi-layer networks and their working.
Learn the foundations of deep learning and its applications.
Learn how to prepare datasets for training deep learning models.
Explore activation functions like ReLU, sigmoid, and tanh.
Visualize optimization techniques like SGD, Adam, and more.
Understand how loss functions work to optimize models.
Understand deep learning concepts through interactive visual demos.
Learn how to manage, train, and evaluate models in deep learning.
Explore visual AI tasks through demos and interactive tools.
Learn how to prepare image data for AI tasks.
Understand how images are transformed for training neural networks.
Understand convolution kernels and their role in CNNs.
Learn about pooling operations for feature reduction in CNNs.
Explore audio-based AI tasks through interactive tools.
Learn the basics of audio data and processing techniques.
Understand how speech is sampled and processed for AI models.
Learn how AI detects spam using text data.
Understand how Q&A systems are built using AI.
Explore word embeddings for natural language processing.
Learn about position embeddings in transformer models.
Explore how attention mechanisms work in NLP models.
Visualize attention mechanisms using heatmaps.
Visualize how BERT processes text for various NLP tasks.
