Agentic AI Tutorial

Welcome to the Agentic AI tutorial! This guide will walk you through building a simple AI-powered assistant using Python and some basic machine learning concepts.

Step 1: Install Requirements

  
                pip install torch torchvision torchaudio tqdm tqdm-progress

Step 2: Import Libraries

  
                import torch  
                import torch.nn as nn  
                import torch.optim as optim  
                import torch.utils.data as data

Step 3: Create a Simple Model

  
                class SimpleModel(nn.Module):  
                    def __init__(self):  
                        super(SimpleModel, self).__init__()  
                        self.fc = nn.Linear(10, 2)  
                    def forward(self, x):  
                        return self.fc(x)

Step 4: Train the Model

  
                model = SimpleModel()  
                optimizer = optim.SGD(model.parameters(), lr=0.01)  
                loss_fn = nn.MSELoss()  
                train_loader = data.DataLoader(dataset, batch_size=32)  
                for epoch in range(10):  
                    for batch in train_loader:  
                        outputs = model(batch)  
                        loss = loss_fn(outputs, labels)  
                        optimizer.zero_grad()  
                        loss.backward()  
                        optimizer.step()

Step 5: Evaluate the Model

  
                with torch.no_grad():  
                    outputs = model(test_data)  
                    predictions = torch.argmax(outputs, dim=1)  
                    accuracy = (predictions == labels).sum().item() / len(labels)

Step 6: Deploy the Model

  
                model.eval()  
                with torch.inference_mode():  
                    inputs = torch.randn(1, 10)  
                    outputs = model(inputs)  
                    print("Output:", outputs)

Conclusion

You've built a basic agentic AI assistant! Now you can expand on this by adding more layers, training with real data, and integrating with APIs or other systems.