Intro to Neural Networks
Description
Neural Networks are the foundation of deep learning, inspired by the structure and function of the human brain. They consist of interconnected layers of nodes (neurons) that process data in stages to learn patterns and make predictions.
What is a Neural Network?
A neural network is composed of layers:
- Input Layer: Receives the raw data (features).
- Hidden Layers: Perform computations through neurons and activation functions to detect patterns.
- Output Layer: Produces the final prediction or classification.
Neural networks use weighted connections and learn by adjusting these weights during training using backpropagation and gradient descent.
Example
Simple Neural Network Using Keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
# Define a simple feedforward neural network
model = Sequential([
Dense(32, activation='relu', input_shape=(10,)),
Dense(16, activation='relu'),
Dense(1, activation='sigmoid') # For binary classification
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.summary()
This basic network takes 10 input features and outputs a binary prediction (e.g., spam or not spam).
Real-World Applications
Neural Network Applications
- Computer Vision: Object recognition, facial detection, image captioning
- Natural Language Processing: Language translation, speech recognition, text generation
- Finance: Fraud detection, risk modeling, algorithmic trading
- Healthcare: Diagnosis from medical images, treatment recommendation systems
Resources
The following resources will be manually added later:
Video Tutorials
PDF/DOC Materials
Interview Questions
1. What is a neuron in a neural network?
A neuron receives inputs, applies weights and a bias, passes the result through an activation function, and outputs a signal to the next layer.
2. What is the role of the activation function?
Activation functions introduce non-linearity into the model, enabling neural networks to learn complex relationships in data.
3. What is backpropagation?
Backpropagation is an algorithm used to minimize the error by adjusting weights based on the gradient of the loss function with respect to each weight.
4. What is the difference between shallow and deep networks?
Shallow networks have fewer layers (typically one hidden layer), while deep networks consist of multiple hidden layers for learning hierarchical features.