Neural Programming
From theory to tensors. Learn to implement, train, and deploy neural networks using the world's most powerful AI frameworks.
PyTorch Fundamentals
PyTorch is the industry standard for research and production. Learn how to define models using `nn.Module` and manage gradients automatically.
import torch.nn as nn
class SimpleNeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(SimpleNeuralNet, self).__init__()
self.l1 = nn.Linear(input_size, hidden_size)
self.relu = nn.ReLU()
self.l2 = nn.Linear(hidden_size, num_classes)
def forward(self, x):
out = self.l1(x)
out = self.relu(out)
out = self.l2(out)
return out
This simple MLP takes an input, passes it through a hidden layer with ReLU activation, and outputs the final classification.
Data Loading & Transforms
AI is only as good as its data. Master `DataLoader` and `Dataset` to efficiently feed images, text, and numerical data into your models.
Image Processing
Learn normalization, resizing, and augmentation techniques using torchvision.
NLP Tokenization
Convert text into numerical tokens using HuggingFace Tokenizers.
The Training Loop
Understand the cycle of training: Forward pass, Loss calculation, Backpropagation, and Optimizer step.
- 1 Zero the gradients
- 2 Forward pass through the model
- 3 Calculate the Loss (MSE, CrossEntropy)
- 4 Backpropagate the error
- 5 Update weights via Optimizer (Adam, SGD)
Mastered the code?
Now it's time to explore the cutting-edge architectures that power GPT and Gemini.
Next: Go Advanced