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What is Layer (deep learning)?

A layer in deep learning is a fundamental building block of neural networks, where computations such as feature extraction and pattern recognition occur. Deep learning models consist of multiple layers, including input, hidden, and output layers, which enable AI systems to learn complex relationships in data for tasks like image recognition and NLP.

Foundation Models
Foundation models are large-scale AI models trained on vast amounts of data to serve as a base for various applications, including NLP, computer vision, and generative AI. Examples include GPT (language models) and CLIP (multimodal AI), enabling transfer learning across domains.
Data Augmentation
Data augmentation is a technique used in machine learning to artificially expand a dataset by applying transformations such as rotation, scaling, flipping, or noise injection. It helps improve model performance by increasing data diversity, particularly in image, speech, and text recognition tasks. Data augmentation is essential in deep learning to reduce overfitting and enhance model generalization.
Deep learning speech synthesis
Deep learning speech synthesis is an AI-driven technique that generates human-like speech from text. It utilizes deep neural networks, such as transformers and recurrent neural networks (RNNs), to produce natural-sounding voices for applications like virtual assistants, text-to-speech software, and automated customer service.