Python

Overview Hugging Face Transformers is the most popular library for working with pre-trained language models. This guide covers installation, basic usage, and common NLP tasks. Installation pip install transformers torch Loading a Pre-trained Model from transformers import pipeline # Text classification classifier = pipeline("sentiment-analysis") result = classifier("I love using Hugging Face!") print(result) # [{'label': 'POSITIVE', 'score': 0.9998}] Text Generation generator = pipeline("text-generation", model="gpt2") output = generator("The future of AI is", max_length=50) print(output[0]['generated_text']) Named Entity Recognition ner = pipeline("ner", grouped_entities=True) text = "Apple was founded by Steve Jobs in Cupertino." entities = ner(text) # [{'entity_group': 'ORG', 'word': 'Apple'}, ...] Fine-tuning a Model from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2) training_args = TrainingArguments( output_dir="./results", num_train_epochs=3, per_device_train_batch_size=16, evaluation_strategy="epoch" ) trainer = Trainer(model=model, args=training_args, train_dataset=train_data) trainer.train() Key Resources Hugging Face Documentation Model Hub - 400,000+ pre-trained models Datasets Library

Overview PyTorch is the leading deep learning framework used by researchers and industry. This guide covers the fundamentals you need to build and train neural networks. Tensors import torch # Create tensors x = torch.tensor([[1, 2], [3, 4]], dtype=torch.float32) y = torch.zeros(3, 3) z = torch.randn(2, 3) # Random normal # GPU support if torch.cuda.is_available(): x = x.cuda() Autograd x = torch.tensor([2.0], requires_grad=True) y = x ** 2 + 3 * x + 1 y.backward() print(x.grad) # tensor([7.]) = 2*x + 3 Building a Neural Network import torch.nn as nn class MLP(nn.Module): def __init__(self, input_size, hidden_size, output_size): super().__init__() self.layers = nn.Sequential( nn.Linear(input_size, hidden_size), nn.ReLU(), nn.Dropout(0.2), nn.Linear(hidden_size, output_size) ) def forward(self, x): return self.layers(x) model = MLP(784, 256, 10) Training Loop optimizer = torch.optim.Adam(model.parameters(), lr=0.001) criterion = nn.CrossEntropyLoss() for epoch in range(10): for batch_x, batch_y in dataloader: optimizer.zero_grad() outputs = model(batch_x) loss = criterion(outputs, batch_y) loss.backward() optimizer.step() print(f"Epoch {epoch}, Loss: {loss.item():.4f}") Saving and Loading Models # Save torch.save(model.state_dict(), "model.pth") # Load model.load_state_dict(torch.load("model.pth")) model.eval() Key Resources PyTorch Documentation PyTorch Tutorials

Overview ML projects have complex dependencies. Proper environment management prevents conflicts and ensures reproducibility across machines. Tool Comparison Tool Speed Best For venv Fast Simple projects conda Slow CUDA, scientific uv Very fast Modern projects poetry Medium Package development venv (Built-in) # Create environment python -m venv .venv # Activate source .venv/bin/activate # Linux/Mac .venv\Scripts\activate # Windows # Install packages pip install torch transformers # Save dependencies pip freeze > requirements.txt # Reproduce pip install -r requirements.txt Conda Best for CUDA and scientific packages. ...