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Activation Functions

Introduce non-linearity

Activation Functions like ReLU and Sigmoid enable neural networks to learn complex patterns by introducing non-linear transformations.

Neural networks with multiple layers

Deep Learning uses multi-layered neural networks to model complex patterns, enabling breakthroughs in vision, speech, and NLP.

Understanding human language

NLP enables machines to interpret, process, and generate human language for tasks like translation, sentiment analysis, and chatbots.

AI for image and video analysis

Computer Vision allows machines to interpret visual data, powering applications like facial recognition, autonomous vehicles, and medical imaging.

Learning through rewards and penalties

Reinforcement Learning trains agents to make decisions by interacting with environments, optimizing actions based on feedback signals.

Models inspired by the human brain

Neural Networks are computational models that mimic the structure and function of biological neural networks to process information.

Specialized for image data

CNNs are deep learning models designed to process and analyze visual data through convolutional layers.

Two networks competing

GANs consist of a generator and a discriminator that compete, enabling the creation of realistic synthetic data.

Using pre-trained models

Transfer Learning leverages knowledge from pre-trained models to improve performance on new tasks with limited data.