Understand training differences between classical ML and neural networks
Learn about epochs, batch processing, and iterative learning
Compare resource requirements and use cases
Master practical implementation strategies
Training Paradigms Overview
🔵 Classical Machine Learning
One-shot learning: Train once on entire dataset
Batch processing: All data processed simultaneously
Direct optimization: Closed-form or iterative solutions
Feature engineering: Manual feature extraction
Deterministic: Same result every time
🟢 Neural Networks
Iterative learning: Multiple passes (epochs) through data
Mini-batch processing: Small chunks of data
Gradient descent: Gradual parameter updates
Automatic features: Learned representations
Stochastic: Results may vary between runs
Key Insight: Classical ML typically trains once and is done, while neural networks require multiple iterations through the data to gradually learn complex patterns.
Training Flow Comparison
Classical ML: Data → Feature Engineering → Algorithm → Model ✅
Classical algorithms typically process the entire dataset at once to find the optimal solution.
# Classical ML Training Examples
# 1. Linear Regression - Closed Form Solution
from sklearn.linear_model import LinearRegression
import numpy as np
X_train = np.random.randn(1000, 5) # All training data
y_train = np.random.randn(1000)
model = LinearRegression()
model.fit(X_train, y_train) # Single training step - DONE!
# 2. SVM - Iterative but still batch processing
from sklearn.svm import SVC
svm = SVC()
svm.fit(X_train, y_train) # Processes entire dataset
# 3. Random Forest - Ensemble of decision trees
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_estimators=100)
rf.fit(X_train, y_train) # Builds all trees using full dataset
✅ Advantages
Simple training process
Fast training (usually)
Deterministic results
No hyperparameter tuning for epochs
Memory efficient for small datasets
❌ Limitations
Limited to engineered features
Cannot handle very large datasets
Less flexible for complex patterns
Requires all data in memory
Limited scalability
Neural Network Training Process
🔄 Iterative Learning with Epochs
Neural networks learn through multiple complete passes (epochs) through the training data, updating weights incrementally.
# Neural Network Training with Epochs
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
# Create dataset and dataloader
dataset = TensorDataset(X_tensor, y_tensor)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
model = nn.Sequential(
nn.Linear(5, 64),
nn.ReLU(),
nn.Linear(64, 1)
)
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.MSELoss()
# Training loop with epochs
num_epochs = 100
for epoch in range(num_epochs): # Multiple passes through data
epoch_loss = 0
for batch_X, batch_y in dataloader: # Process in small batches
# Forward pass
predictions = model(batch_X)
loss = criterion(predictions, batch_y)
# Backward pass and update
optimizer.zero_grad()
loss.backward()
optimizer.step()
epoch_loss += loss.item()
if epoch % 10 == 0:
print(f'Epoch {epoch}, Loss: {epoch_loss/len(dataloader):.4f}')
What are Epochs?
📚 Epoch Definition
An epoch is one complete pass through the entire training dataset. Neural networks typically require many epochs to converge.
1
Epoch
1000
Samples
32
Batch Size
32
Batches per Epoch
Epoch Progression:
# Epoch 1: Model sees all 1000 samples
# Batch 1: samples 0-31 → update weights
# Batch 2: samples 32-63 → update weights
# ...
# Batch 32: samples 992-999 → update weights
# END OF EPOCH 1
# Epoch 2: Model sees all 1000 samples AGAIN
# (shuffled order)
# Batch 1: samples 234-265 → update weights
# Batch 2: samples 67-98 → update weights
# ...
# END OF EPOCH 2
# Continue for 50-100+ epochs until convergence
Why Multiple Epochs? Neural networks learn gradually. Each epoch allows the model to refine its understanding of the data patterns. Early epochs learn basic patterns, later epochs fine-tune complex relationships.
Batch Size in Neural Networks
📦 Batch Size Definition
Number of samples processed together before updating model weights. Critical hyperparameter affecting training dynamics.
Batch Size
Description
Memory Usage
Training Speed
Gradient Quality
Generalization
Small (1-32)
Stochastic/Mini-batch
Low
Fast per update
Noisy gradients
Better
Medium (32-512)
Mini-batch (common)
Moderate
Balanced
Stable gradients
Good
Large (512+)
Large batch
High
Slow per update
Smooth gradients
May overfit
Full Dataset
Batch gradient descent
Very High
Very slow
Perfect gradients
Often poor
# Batch Size Examples
import torch
from torch.utils.data import DataLoader
dataset = TensorDataset(X, y)
# Small batch - more updates per epoch, noisier gradients
small_loader = DataLoader(dataset, batch_size=16, shuffle=True)
# Medium batch - balanced approach (most common)
medium_loader = DataLoader(dataset, batch_size=64, shuffle=True)
# Large batch - fewer updates, smoother gradients
large_loader = DataLoader(dataset, batch_size=256, shuffle=True)
# Impact on training:
# Small batch: 1000/16 = 62.5 updates per epoch
# Medium batch: 1000/64 = 15.6 updates per epoch
# Large batch: 1000/256 = 3.9 updates per epoch
CNN Training Specifics
🖼️ Convolutional Neural Networks
CNNs have additional considerations due to spatial data and memory requirements for feature maps.
🔵 Classical Computer Vision
Hand-crafted features (HOG, SIFT)
Fixed feature extraction
Train classifier on features
Process images one at a time
🟢 CNN Training
Learned hierarchical features
Spatial weight sharing
Backpropagation through conv layers
Batch processing with 4D tensors
# CNN Training Example
import torch.nn as nn
class SimpleCNN(nn.Module):
def __init__(self):
super().__init__()
self.conv_layers = nn.Sequential(
nn.Conv2d(3, 32, 3, padding=1), # Learn 32 filters
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(32, 64, 3, padding=1), # Learn 64 filters
nn.ReLU(),
nn.MaxPool2d(2),
)
self.classifier = nn.Sequential(
nn.Linear(64 * 8 * 8, 128),
nn.ReLU(),
nn.Linear(128, 10)
)
def forward(self, x):
# x shape: (batch_size, 3, 32, 32) - CIFAR-10 images
x = self.conv_layers(x) # Learn spatial features
x = x.view(x.size(0), -1) # Flatten for classifier
return self.classifier(x)
# Training with image batches
dataloader = DataLoader(dataset, batch_size=64, shuffle=True)
# Each batch: torch.Size([64, 3, 32, 32]) - 64 RGB images
for epoch in range(50): # CNNs often need more epochs
for batch_images, batch_labels in dataloader:
# Process 64 images simultaneously
outputs = cnn_model(batch_images)
loss = criterion(outputs, batch_labels)
# ... backprop and update
CNN Memory Consideration: Batch size limited by GPU memory. Feature maps consume significant memory, especially in early layers. Common to use smaller batches (16-64) for high-resolution images.
Training Time & Resource Comparison
🔵 Classical ML
Minutes
Training Time
CPU
Hardware
GB
Memory
🟢 Standard NN
Hours
Training Time
GPU
Hardware
10+ GB
Memory
🔮 Deep CNN
Days
Training Time
Multi-GPU
Hardware
100+ GB
Memory
# Training Time Examples (rough estimates)
# Classical ML - Scikit-learn Random Forest
# Dataset: 100K samples, 20 features
start_time = time.time()
rf = RandomForestClassifier(n_estimators=100)
rf.fit(X_train, y_train) # ~30 seconds to 2 minutes
print(f"Training time: {time.time() - start_time:.2f} seconds")
# Neural Network - PyTorch MLP
# Dataset: 100K samples, 20 features → 128 → 64 → 10 classes
for epoch in range(100): # ~10-30 minutes total
for batch in dataloader:
# ... training code
# CNN - ResNet50 on ImageNet
# Dataset: 1.2M images, 1000 classes
for epoch in range(90): # ~1-2 weeks on single GPU
for batch in dataloader:
# ... training code
Scaling Reality: Classical ML scales linearly with data size. Neural networks scale with data size × epochs × model complexity. This is why neural networks require specialized hardware and parallel processing.
When to Use Each Approach
Scenario
Classical ML
Neural Networks
CNNs
Small Dataset
✅ Preferred
⚠️ Risk of overfitting
❌ Likely to overfit
Tabular Data
✅ Excellent choice
⚠️ Can work well
❌ Not suitable
Image Classification
⚠️ Limited accuracy
✅ Good for simple tasks
✅ State-of-the-art
Large Dataset
⚠️ May be slow
✅ Excellent
✅ Excellent
Limited Computing Resources
✅ Very efficient
⚠️ Moderate resources
❌ High resource needs
Need Interpretability
✅ Highly interpretable
⚠️ Limited interpretability
❌ Black box
Quick Prototyping
✅ Very fast
⚠️ Moderate time
❌ Time consuming
Rule of Thumb:
Classical ML: Start here for structured/tabular data, small datasets, or when you need interpretability
Neural Networks: Use for complex patterns, large datasets, or when classical ML plateaus
CNNs: Essential for computer vision tasks, especially with large image datasets
Key Takeaways
🎯 Training Paradigms
Classical ML
One-shot learning
Full batch processing
Deterministic training
Feature engineering required
Neural Networks
Iterative learning with epochs
Mini-batch processing
Gradient-based optimization
Automatic feature learning
⚡ Performance & Resources
Training Time
Classical ML: Minutes to hours
Neural Networks: Hours to days
CNNs: Days to weeks
Resource Requirements
Classical ML: CPU, low memory
Neural Networks: GPU recommended
CNNs: High-end GPU required
🎓 Learning Path Recommendation
Start with Classical ML: Understand basic concepts, feature engineering, and model evaluation
Progress to Neural Networks: Learn about epochs, batch processing, and gradient descent
Specialize in CNNs: Master computer vision and spatial data processing
Advanced Topics: Explore transformers, reinforcement learning, and generative models
Quick Knowledge Check
🤔 Test Your Understanding
Answer these questions to reinforce your learning:
Question 1: Training Process
Which approach processes the entire dataset at once?
A) Neural Networks
B) Classical ML
C) Both approaches
D) Neither approach
Answer: B) Classical ML
Classical ML typically processes all data in one batch, while neural networks use mini-batches over multiple epochs.
Question 2: Epochs
What is an epoch in neural network training?
A) One weight update
B) One complete pass through the dataset
C) One batch processed
D) One layer of the network
Answer: B) One complete pass through the dataset
An epoch means the model has seen every training sample exactly once.
Practical Implementation Tips
🔵 Classical ML Best Practices
Data Preparation
Handle missing values appropriately
Scale/normalize numerical features
Encode categorical variables
Remove outliers if necessary
Model Selection
Start with simple models (linear/logistic regression)
Use cross-validation for evaluation
Try ensemble methods for better performance
Consider interpretability requirements
🟢 Neural Network Best Practices
Architecture Design
Start with simple architectures
Use appropriate activation functions
Include dropout for regularization
Monitor training/validation curves
Training Strategy
Choose appropriate learning rate
Use learning rate scheduling
Implement early stopping
Save best model checkpoints
🎯 Key Success Factors
Data Quality: Garbage in, garbage out - clean your data thoroughly
Feature Engineering: Critical for classical ML, less important for deep learning
Hyperparameter Tuning: Use grid search, random search, or Bayesian optimization
Evaluation: Use appropriate metrics and validation strategies
Monitoring: Track training progress and watch for overfitting