#transformer

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 data (tokens), computational requirements. 3. Architecture variants: encoder-only (BERT), decoder-only (GPT), encoder-decoder (T5). Pre-training strategies: 1. Data preparation: web crawling, deduplication, quality filtering, tokenization (BPE, SentencePiece). 2. Training objectives: next token prediction, masked language modeling, contrastive learning. 3. Infrastructure: distributed training, gradient accumulation, mixed precision (FP16/BF16). Fine-tuning approaches: 1. Supervised fine-tuning: task-specific datasets, learning rate 5e-5 to 1e-4, batch size 8-32. 2. Parameter-efficient fine-tuning: LoRA (Low-Rank Adaptation), adapters, prompt tuning. 3. Reinforcement Learning from Human Feedback (RLHF): reward modeling, PPO training. Prompt engineering: 1. Zero-shot prompting: task description without examples, clear instruction formatting. 2. Few-shot learning: 1-5 examples, in-context learning, demonstration selection strategies. 3. Chain-of-thought: step-by-step reasoning, intermediate steps, complex problem solving. Evaluation methods: 1. Perplexity: language modeling capability, lower is better, domain-specific evaluation. 2. BLEU score: text generation quality, n-gram overlap, reference comparison. 3. Human evaluation: quality, relevance, safety assessment, inter-rater reliability. Deployment considerations: inference optimization, model quantization, caching strategies, latency <1000ms target, cost optimization through batching.

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-mel filterbank, spectrograms, delta features. 3. Noise reduction: spectral subtraction, Wiener filtering, voice activity detection. Deep learning architectures: 1. Recurrent networks: LSTM/GRU for sequential modeling, bidirectional processing, attention mechanisms. 2. Transformer models: self-attention for audio sequences, positional encoding, parallel processing. 3. Conformer: convolution + transformer, local and global context modeling, state-of-the-art accuracy. End-to-end systems: 1. CTC (Connectionist Temporal Classification): alignment-free training, blank symbol, beam search decoding. 2. Attention-based encoder-decoder: seq2seq modeling, attention mechanisms, teacher forcing. 3. RNN-Transducer: streaming ASR, online decoding, real-time transcription. Language modeling: 1. N-gram models: statistical language modeling, smoothing techniques, vocabulary handling. 2. Neural language models: LSTM, Transformer-based, contextual understanding. 3. Shallow fusion: LM integration during decoding, score interpolation, beam search optimization. Advanced techniques: 1. Data augmentation: speed perturbation, noise addition, SpecAugment for robustness. 2. Multi-task learning: ASR + speaker recognition, emotion recognition, shared representations. 3. Transfer learning: pre-training on large datasets, fine-tuning for specific domains. Evaluation: Word Error Rate (WER <5% excellent), Real-Time Factor (RTF <0.1), confidence scoring, speaker adaptation for improved accuracy.

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 hidden layers, sigmoid/softmax for output, leaky ReLU for gradient problems. 3. Weight initialization: Xavier/Glorot for sigmoid/tanh, He initialization for ReLU networks. Convolutional Neural Networks (CNNs): 1. Architecture patterns: LeNet (digit recognition), AlexNet (ImageNet), ResNet (skip connections), EfficientNet (compound scaling). 2. Layer design: Conv2D (3x3 filters standard), MaxPooling (2x2), dropout (0.2-0.5), batch normalization. 3. Transfer learning: pre-trained models (ImageNet), fine-tuning last layers, feature extraction vs. full training. Recurrent Neural Networks (RNNs): 1. LSTM/GRU: sequential data processing, vanishing gradient solution, bidirectional architectures. 2. Attention mechanisms: self-attention, multi-head attention, transformer architecture. Regularization techniques: 1. Dropout: 20-50% during training, prevents overfitting, Monte Carlo dropout for uncertainty. 2. Batch normalization: normalize layer inputs, accelerated training, internal covariate shift reduction. 3. Early stopping: monitor validation loss, patience 10-20 epochs, save best model weights. Training optimization: Adam optimizer (lr=0.001), learning rate scheduling, gradient clipping for RNNs, mixed precision training for efficiency.