An Interactive Journey from Artificial Intelligence to Neural Networks
Deep learning is a subset of machine learning that uses neural networks with multiple layers to learn and make decisions from data, much like how the human brain processes information.
The broader field of making machines smart and capable of performing tasks that typically require human intelligence.
A subset of AI that enables computers to learn and improve from experience without being explicitly programmed.
A subset of ML using neural networks with multiple layers to model and understand complex patterns in data.
# A simple neural network for binary classification
import numpy as np
def sigmoid(x):
return 1 / (1 + np.exp(-x))
# Input data (4 examples, 3 features each)
X = np.array([[0,0,1], [0,1,1], [1,0,1], [1,1,1]])
# Output labels
y = np.array([[0], [1], [1], [0]])
# Initialize weights randomly
weights = np.random.uniform(size=(3,1))
# Training loop
for epoch in range(10000):
# Forward pass
output = sigmoid(np.dot(X, weights))
# Calculate error
error = y - output
# Backward pass (gradient descent)
adjustment = np.dot(X.T, error * output * (1 - output))
weights += adjustment
print("Final weights:", weights.flatten())
The birth of artificial neural networks with the McCulloch-Pitts neuron and the perceptron. Simple linear models that could recognize basic patterns.
Key: First learning algorithms, biological inspirationIntroduction of backpropagation algorithm, enabling training of multi-layer networks. Rise of distributed representations and parallel processing concepts.
Key: Backpropagation, multi-layer networksBreakthrough with deep belief networks and layer-wise pretraining. Modern deep learning with CNNs, RNNs, and transformer architectures achieving human-level performance.
Key: Deep architectures, big data, GPU computingThe term "deep learning" emphasizes the depth of neural networks - the number of layers between input and output. Modern networks can have hundreds of layers!
Each layer extracts increasingly abstract features from the input data
❌ Cannot separate with a line
✅ Easy to separate with a line!
Key Insight: The same data can be impossible or trivial to work with depending on how it's represented. Deep learning automatically learns good representations!
Deep learning performance improves dramatically with more data
Convolutional Neural Networks (CNNs) can identify objects, faces, and scenes in images with superhuman accuracy.
Transformer models like GPT can understand and generate human-like text, translate languages, and answer questions.
AI agents learn to play games, control robots, and make decisions through trial and error, like AlphaGo.
Question 1: What is the main advantage of deep learning over traditional machine learning?
Adjust the parameters and see how they affect the network: