Deep Learning

Deep Learning (DL) is a subset of Machine Learning that uses artificial neural networks with many layers to learn hierarchical representations from raw data. It's not a different field — it's ML taken to extremes of scale and depth.

The key driver of DL's dominance was the convergence of ImageNet (millions of labelled images), NVIDIA CUDA (GPU-accelerated matrix math), and architectural innovations (AlexNet in 2012 → ResNet → Transformer → GPT-4). DL now achieves superhuman performance in vision, language, speech, and game playing.

A single neuron takes multiple inputs, multiplies each by a weight, sums them up, adds a bias, then passes the result through an activation function:

output = activation(w1*x1 + w2*x2 + ... + wn*xn + bias)

# In matrix form:
output = activation(W · x + b)

# In PyTorch:
import torch.nn as nn
neuron = nn.Linear(in_features=3, out_features=1)  # 3 inputs, 1 output

Without activation functions, stacking layers would just be repeated linear transformations — equivalent to a single linear layer. Activation functions introduce non-linearity, enabling networks to learn complex patterns.

A neural network is organised into layers:

• Input layer — receives raw features (pixels, token embeddings, numerical values).
• Hidden layers — learn progressively abstract representations. Early layers learn edges; deep layers learn concepts.
• Output layer — produces the final prediction. One neuron per class for classification; one for regression.

Depth (number of layers) enables learning hierarchical features. A CNN trained on faces learns: edges → curves → eyes/nose → face. The "deep" in Deep Learning refers to networks with many layers — modern LLMs have 96+ layers.

import torch
import torch.nn as nn

class FeedForwardNet(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Dropout(0.3),          # regularisation
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.Linear(hidden_dim, output_dim)
        )

    def forward(self, x):
        return self.net(x)

# Instantiate
model = FeedForwardNet(input_dim=784, hidden_dim=256, output_dim=10)
print(model)  # see architecture summary

Backpropagation is the algorithm that trains neural networks. It's the chain rule of calculus applied recursively through a computation graph to compute gradients of the loss with respect to every weight.

import torch
import torch.nn as nn
import torch.optim as optim

model = FeedForwardNet(784, 256, 10)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
criterion = nn.CrossEntropyLoss()

for epoch in range(50):
    model.train()
    for X_batch, y_batch in train_loader:
        # 1. Forward pass — compute predictions
        logits = model(X_batch)

        # 2. Compute loss
        loss = criterion(logits, y_batch)

        # 3. Backward pass — compute gradients via backprop
        optimizer.zero_grad()    # clear previous gradients
        loss.backward()          # backpropagate through the graph

        # 4. Update weights — gradient descent step
        optimizer.step()

    # Validation pass (no gradient tracking needed)
    model.eval()
    with torch.no_grad():
        val_preds = model(X_val)
        val_loss = criterion(val_preds, y_val)
    print(f"Epoch {epoch+1}: train={loss:.4f} val={val_loss:.4f}")

As gradients are multiplied through many layers, they can shrink exponentially (vanishing gradients) making early layers learn very slowly, or grow exponentially (exploding gradients) causing training to diverge. Solutions:

• Residual connections (ResNets) — skip connections that let gradients flow directly from output to early layers, bypassing the multiplication chain.
• Batch Normalisation — normalises layer inputs, keeping activations in a healthy range and stabilising training significantly.
• Gradient clipping — caps the gradient norm at a maximum value. Standard practice in RNN and Transformer training.
• Weight initialisation — He init for ReLU networks, Xavier/Glorot for tanh. Good init means gradients start in a stable range.
• LSTM / GRU gating — designed specifically to mitigate vanishing gradients in sequential models.

The Transformer, introduced in "Attention is All You Need" (2017), replaced recurrence with self-attention. For each token, self-attention computes a weighted sum over all other tokens — allowing the model to relate "it" to "cat" across an entire paragraph in one step.

# Each token is projected into Query, Key, and Value vectors
Q = X @ W_q   # "what am I looking for?"
K = X @ W_k   # "what do I contain?"
V = X @ W_v   # "what information do I pass on?"

# Attention scores: how much each token should attend to each other
scores = Q @ K.T / sqrt(d_k)      # scaled dot product
weights = softmax(scores)          # normalise to probabilities

# Weighted sum of Values
attention_output = weights @ V     # attended representation

# Multi-Head: run h parallel attention heads, then concatenate
# Each head learns different relationship types (syntax, coreference, etc.)

Key advantages of self-attention over RNNs: processes all tokens in parallel (faster training), no vanishing gradient over long sequences, and explicit attention weights make the model somewhat interpretable.

Training deep networks from scratch requires millions of labelled samples and days of GPU time. Transfer learning solves this: take a model pretrained on a large general dataset, then adapt it to your specific task with far less data and compute.

With billion-parameter LLMs, even fine-tuning is expensive. PEFT methods update a tiny fraction of parameters while keeping the backbone frozen:

• LoRA (Low-Rank Adaptation) — adds small trainable rank-decomposition matrices to attention layers. Trains <1% of parameters with near-full-fine-tune performance. Used to create domain-specific LLMs cheaply.
• Adapter Layers — insert small bottleneck modules between transformer layers. Freeze base model, train adapters only.
• Prompt Tuning — prepend learnable "soft prompt" tokens to the input. The model backbone is frozen; only the prompt embeddings are updated.
• QLoRA — quantise the base model to 4-bit, apply LoRA on top. Fine-tune a 70B model on a single 48GB GPU.

from transformers import (
    AutoTokenizer, AutoModelForSequenceClassification,
    TrainingArguments, Trainer
)
from datasets import load_dataset

# Load a pretrained BERT model
model_name = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(
    model_name, num_labels=2
)

# Tokenise your dataset
dataset = load_dataset("imdb")
def tokenise(batch):
    return tokenizer(batch["text"], truncation=True, padding="max_length")
dataset = dataset.map(tokenise, batched=True)

# Fine-tune
args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=16,
    learning_rate=2e-5,
    weight_decay=0.01,
    evaluation_strategy="epoch",
    save_strategy="epoch",
    load_best_model_at_end=True,
)
trainer = Trainer(model=model, args=args,
    train_dataset=dataset["train"],
    eval_dataset=dataset["test"])
trainer.train()