🧠 Neural Networks Quick Reference

Your cheat sheet for choosing the right network

🔗 ANN - Artificial Neural Network

Best for: Simple classification, basic patterns
Data type: Tabular data, small datasets
Training time: Fast (minutes)
Complexity: ⭐⭐ (Low)
Examples:
  • Email spam detection
  • Credit scoring
  • Simple recommendations

🏗️ DNN - Deep Neural Network

Best for: Complex patterns, high accuracy
Data type: Large datasets, any format
Training time: Slow (hours/days)
Complexity: ⭐⭐⭐⭐ (High)
Examples:
  • Image recognition
  • Voice assistants
  • Game AI

🔄 RNN - Recurrent Neural Network

Best for: Sequential data, time series
Data type: Text, speech, time series
Training time: Medium (hours)
Complexity: ⭐⭐⭐⭐ (High)
Examples:
  • Language translation
  • Stock prediction
  • Chatbots

👁️ CNN - Convolutional Neural Network

Best for: Images, visual data
Data type: Images, spatial data
Training time: Slow (hours/days)
Complexity: ⭐⭐⭐⭐⭐ (Very High)
Examples:
  • Face recognition
  • Medical imaging
  • Self-driving cars

🎯 Decision Flowchart

What type of data do you have?
Numbers/Tables
↓
Simple? → ANN
Complex? → DNN
Text/Sequences
↓
Use RNN
(LSTM/GRU)
Images/Visual
↓
Use CNN

📊 Detailed Comparison

Aspect ANN DNN RNN CNN
Data Type Tabular, small Any, large Sequential Images
Training Speed Fast Slow Medium Slow
Accuracy Good Excellent Very Good Excellent
Memory Usage Low High Medium High
Interpretability Medium Low Low Low
Data Required Small Large Medium Large

💡 Pro Tips

🚀 Getting Started

Always start with the simplest model (ANN) that could work for your problem, then increase complexity if needed.

📊 Data Matters

More data usually means better results, especially for DNNs and CNNs. Quality > Quantity though!

⚡ Performance

If you need real-time predictions, consider simpler models (ANN) or optimize complex ones.

🔧 Preprocessing

Always normalize your data! Neural networks work best with scaled inputs (0-1 or standardized).

🎯 Overfitting

Use dropout, early stopping, and validation sets to prevent overfitting, especially with complex models.

🔄 Iteration

Machine learning is iterative. Start simple, measure results, then improve step by step.

🛠️ Quick Setup Commands

Install required packages:

pip install tensorflow numpy matplotlib pandas scikit-learn seaborn

Basic imports for any project:

import numpy as np import matplotlib.pyplot as plt import tensorflow as tf from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler

Run examples:

# ANN Example python examples/ann_iris_example.py # DNN Example python examples/dnn_mnist_example.py # RNN Example python examples/rnn_stock_example.py # CNN Example python examples/cnn_pets_example.py

🎓 Remember the Golden Rule

"The best model is the simplest one that solves your problem adequately."

Start simple, measure performance, then add complexity only if needed!