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Master generative AI and large language model development, fine-tuning, and deployment for various applications. LLM architecture fundamentals: 1. Transformer architecture: self-attention mechanism, multi-head attention, positional encoding. 2. Model scaling: parameter count (GPT-3: 175B), training ...
Build speech recognition systems using deep learning for automatic speech recognition and audio processing applications. Audio preprocessing: 1. Signal processing: sampling rate 16kHz, windowing (Hamming, Hann), frame size 25ms, frame shift 10ms. 2. Feature extraction: MFCC (13 coefficients), log-me...
Design and implement deep learning architectures for various applications with optimization and regularization techniques. Neural network fundamentals: 1. Architecture design: input layer sizing, hidden layers (2-5 for most tasks), output layer activation functions. 2. Activation functions: ReLU for...
