Activation Functions
Introduce non-linearity
- Activation Functions like ReLU and Sigmoid enable neural networks to learn complex patterns by introducing non-linear transformations.
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Deep Learning
Neural networks with multiple layers
- Deep Learning uses multi-layered neural networks to model complex patterns, enabling breakthroughs in vision, speech, and NLP.
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Natural Language Processing
Understanding human language
- NLP enables machines to interpret, process, and generate human language for tasks like translation, sentiment analysis, and chatbots.
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Computer Vision
AI for image and video analysis
- Computer Vision allows machines to interpret visual data, powering applications like facial recognition, autonomous vehicles, and medical imaging.
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Reinforcement Learning
Learning through rewards and penalties
- Reinforcement Learning trains agents to make decisions by interacting with environments, optimizing actions based on feedback signals.
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Neural Networks
Models inspired by the human brain
- Neural Networks are computational models that mimic the structure and function of biological neural networks to process information.
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Convolutional Neural Networks
Specialized for image data
- CNNs are deep learning models designed to process and analyze visual data through convolutional layers.
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Generative Adversarial Networks
Two networks competing
- GANs consist of a generator and a discriminator that compete, enabling the creation of realistic synthetic data.
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Transfer Learning
Using pre-trained models
- Transfer Learning leverages knowledge from pre-trained models to improve performance on new tasks with limited data.
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