From Tensors to
Production AI
A deep dive into PyTorch — building neural networks, optimizing for production, and implementing state-of-the-art architectures
The Building Blocks of Deep Learning
Each concept builds on the last — from tensors to production-ready architectures
Learning Progression
Tensor Operations
PyTorch tensors are the foundation - n-dimensional arrays with GPU acceleration and automatic differentiation support.
The key breakthrough: tensors track their computational history, enabling automatic gradient calculation through the entire network.
# Tensors with autograd
x = torch.tensor([1.0, 2.0], requires_grad=True)
y = x.pow(2).sum() # y = x₁² + x₂²
y.backward() # Compute gradients
print(x.grad) # tensor([2., 4.]) = [2x₁, 2x₂]Neural Network Architecture
Layer composition using nn.Module - the elegant abstraction that makes complex networks simple to build and train.
nn.Sequential chains layers linearly, but real power comes from custom forward() methods where you control data flow.
class CNN(nn.Module):
def __init__(self):
super().__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, 3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(2)
)
def forward(self, x):
return self.features(x)Training Loop Mastery
The training loop is where theory meets practice - forward pass, loss computation, backward pass, and optimization step.
zero_grad() before backward() is crucial - PyTorch accumulates gradients by default, which is useful for gradient accumulation but dangerous if forgotten.
for epoch in range(epochs):
for batch_x, batch_y in dataloader:
optimizer.zero_grad() # Clear gradients
predictions = model(batch_x) # Forward pass
loss = criterion(predictions, batch_y)
loss.backward() # Compute gradients
optimizer.step() # Update weightsTransfer Learning
Leverage pre-trained models to achieve state-of-the-art results with minimal data. Fine-tune the final layers while freezing earlier features.
Early layers learn universal features (edges, textures). Only fine-tune later layers for your specific task to avoid catastrophic forgetting.
# Load pre-trained ResNet
model = models.resnet50(pretrained=True)
# Freeze early layers
for param in model.parameters():
param.requires_grad = False
# Replace final layer for your task
model.fc = nn.Linear(2048, num_classes)
# Only train the new layer
optimizer = optim.Adam(model.fc.parameters())Optuna Hyperparameter Tuning
Automated hyperparameter optimization using Bayesian search - finds optimal configurations that human intuition would miss.
Optuna's pruning feature stops unpromising trials early, making hyperparameter search 10x faster than grid search.
def objective(trial):
lr = trial.suggest_float('lr', 1e-5, 1e-1, log=True)
batch = trial.suggest_int('batch_size', 16, 128)
layers = trial.suggest_int('n_layers', 1, 5)
model = build_model(layers)
accuracy = train_and_eval(model, lr, batch)
return accuracy
study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=100)Skip Connections (ResNet)
Residual connections allow gradients to flow directly through the network, enabling training of 100+ layer networks.
The identity mapping (x + F(x)) means the network only needs to learn the residual - much easier than learning the full transformation.
class ResidualBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.conv = nn.Sequential(
nn.Conv2d(channels, channels, 3, padding=1),
nn.BatchNorm2d(channels),
nn.ReLU(),
nn.Conv2d(channels, channels, 3, padding=1),
nn.BatchNorm2d(channels)
)
def forward(self, x):
return F.relu(x + self.conv(x)) # Skip!Measurable Impact
Actual metrics from my notebook experiments — not theoretical, but proven
DenseNet Transfer Learning
21-class land use classification with only 100 images per class
MNIST Classifier
Digit recognition with fully connected neural network
Optuna HPO
Automated hyperparameter optimization with pruning
Biggest Challenge
Understanding why my gradients kept exploding in deep networks — until I realized BatchNorm wasn't just optional. The moment skip connections clicked, everything changed.
What I'd Do Differently
Start with transfer learning from day one. I spent weeks training from scratch before discovering that a pre-trained backbone + small head gets you 90% of the way in 5 minutes.
The Complete Journey
Click each module to explore what I learned, built, and discovered
Neural Network Fundamentals
Built my first neural networks from scratch, understanding how tensors, gradients, and optimization work together.
Production Optimization
Moved beyond accuracy to production concerns: hyperparameter tuning, transfer learning, NLP, and performance profiling.
Advanced Architectures
State-of-the-art architectures, transformers, generative AI, and production deployment with MLOps.
Explore the Code
All 44 notebooks, implementations, and experiments are open source