What is Deep Learning?
Deep learning is a subset of machine learning that uses neural networks with many layers to model complex patterns in data. It powers applications such as image recognition, natural language processing, and autonomous systems.
Key Concepts
- Neurons & Layers: Basic building blocks that transform inputs.
- Activation Functions: Introduce non‑linearity (ReLU, Sigmoid, Tanh).
- Loss & Optimization: Measures error and updates weights (Cross‑entropy, MSE, SGD, Adam).
- Regularization: Prevents overfitting (Dropout, L2).
- Convolutional Neural Networks (CNNs): Ideal for image data.
- Recurrent Neural Networks (RNNs) & Transformers: Designed for sequential data.
Simple Python Example
import tensorflow as tf
from tensorflow.keras import layers, models
# Build a simple feed‑forward network
model = models.Sequential([
layers.Dense(64, activation='relu', input_shape=(784,)),
layers.Dense(64, activation='relu'),
layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
print(model.summary())