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Natural Language Processing (NLP)

Teach computers to understand text: sentiment, translation, LLMs

⬢ LIVELLO 2Tecniche
+$40k-
Impatto sullo stipendio
12 mesi
Tempo di apprendimento
Difficile
Difficoltà
11
Carriere
In sintesi

NLP is teaching computers to understand human language through preprocessing, embeddings, and transformer models (BERT, GPT). Career path: NLP Engineer L1 (sentiment analysis, text classification, $100-150k) → L2 (transformers, fine-tuning, $160-220k) → L3/Research Scientist (LLM pretraining, RLHF, $200-350k) over 12-24 months. Transformers replaced RNNs as the dominant architecture in 2018-2020; GPT/BERT skills command premium salaries due to LLM boom. Tech stack: Python + spaCy/NLTK for preprocessing, Hugging Face Transformers library, PyTorch for training, LangChain for production LLM apps, vector databases (Pinecone/Weaviate) for semantic search and RAG.

Cos'è Natural Language Processing (NLP)

NLP = AI that understands human language. Sentiment analysis, translation, chatbots, LLMs (GPT, BERT). High-demand ML specialty (ChatGPT boom). L1: Text preprocessing, sentiment analysis, word embeddings

🔧 STRUMENTI ED ECOSISTEMA
Hugging FacespaCyNLTKTransformersPyTorchBERTGPTLangChainOpenAI APIPineconesentence-transformersLLaMAWeaviate

💰 Stipendio per regione

RegioneLivello baseMidLivello esperto
USA$120k$180k$280k
UK£70k£110k£160k
EU€75k€120k€180k
CANADAC$125kC$185kC$290k

❓ Domande frequenti

Transformers vs RNNs/LSTMs, why did transformers win?
RNNs (LSTM, GRU) process sequences one token at a time, can't parallelize, forget long-range context. Transformers (BERT, GPT) process all tokens in parallel via self-attention, capture long-range dependencies, train 10-100x faster. Attention is All You Need (2017) proved it. By 2020: transformers = industry standard. Learning RNNs = learning history; building production NLP = transformers only.
BERT vs GPT, which should I learn first?
BERT = bidirectional, pretrained on masked language modeling, best for classification/understanding tasks (sentiment, entity extraction, Q&A). GPT = unidirectional (left-to-right), pretrained on causal language modeling, best for generation (chatbots, summarization, translation). Learn BERT first (conceptually simpler, https://huggingface.co/course/chapter1), then GPT. In 2026: fine-tune BERT for custom classifiers, use GPT for chat/generation via API.
Fine-tuning vs RAG (Retrieval Augmented Generation) vs prompt engineering, when to use each?
Prompt engineering = free, fast, no training (try first). RAG = retrieve relevant documents from a vector database, feed to LLM context, good for knowledge-intensive tasks (Q&A over company docs), no retraining. Fine-tuning = expensive (GPUs), slow (hours-days), but fits the model to your data/style. Pick: (1) try prompt engineering, (2) if context window insufficient, add RAG, (3) if LLM still fails, fine-tune. 80% of use cases = prompt engineering + RAG.
How do I host/deploy an LLM myself vs using an API?
API (OpenAI, Anthropic) = $0.01-0.1 per 1k tokens, lowest latency, no infra. Self-host small LLMs (Llama 7B, Mistral) = $0.50-5/hour GPU, latency 100-500ms, full control, privacy. Rule: API for prototyping and scaling (chat, content generation), self-host for privacy-critical apps (healthcare, finance) or if volume > 10M tokens/month. 2026 trend: smaller specialized models (MistralAI) on your infrastructure, not giant models via API.
Vector databases and embeddings, Pinecone vs Weaviate vs building DIY?
Embeddings = convert text to numbers (768-1536 dims), enable semantic search. Pinecone = managed (easiest, $0.10-1/month), Weaviate = self-host (free, complex), DIY = index with NumPy (only for <10k docs). For production: Pinecone if budget available, Weaviate if on-premise required, DIY only for prototypes. All use sentence-transformers (all-MiniLM-L6-v2) for encoding and cosine similarity for retrieval.
Multilingual NLP, how hard is it to support multiple languages?
Monolingual models (English BERT) fail on other languages. Solutions: (1) multilingual BERT (mBERT, XLM-RoBERTa) for 100+ langs, lower quality, (2) language-specific models (French BERT, German BERT) for top langs, better quality, (3) translate to English (lossy but works). Recommendation: mBERT for MVP, switch to language-specific for each supported language in production. Don't try to build language-universal; use existing multilingual checkpoints from Hugging Face.
How do I evaluate NLP models, metrics beyond accuracy?
Classification: precision/recall/F1 (imbalanced), ROC-AUC (ranking). Generation (summarization, translation): ROUGE (n-gram overlap), BLEU (precision on n-grams), human evaluation (expensive). Token classification (NER): micro/macro F1 (per-token). Semantic similarity: cosine similarity, human correlation. NEVER use accuracy alone for text tasks, almost all are imbalanced. For LLMs: use LLM-as-judge (ask GPT to score quality) for generation tasks.

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