Deep Learning Specialization Basics

Welcome to the Basics section of the Deep Learning Specialization documentation. This page provides an overview of the fundamental concepts and techniques in deep learning.

Key Concepts

Neural Networks: The building blocks of deep learning, neural networks are inspired by the human brain and can recognize patterns and make decisions.
Activation Functions: These functions help determine the output of a neural network, enabling it to learn and make predictions.
Backpropagation: A key algorithm used in training neural networks, backpropagation allows the network to adjust its weights and biases based on the error of its predictions.

Learning Resources

For more in-depth learning, you can explore the following resources:

Example

Here's a simple example of a neural network architecture:

Input Layer: 2 neurons
Hidden Layer: 4 neurons
Output Layer: 1 neuron

Training the Network

To train the network, we would typically use a dataset and an optimization algorithm like SGD (Stochastic Gradient Descent).

# Example code snippet in Python
import numpy as np

# Define the network architecture
input_size = 2
hidden_size = 4
output_size = 1

# Initialize the weights and biases
weights = np.random.randn(hidden_size, input_size)
bias = np.random.randn(hidden_size, 1)
output_weights = np.random.randn(output_size, hidden_size)
output_bias = np.random.randn(output_size, 1)

# Forward pass
def forward(x):
    hidden = np.dot(x, weights) + bias
    output = np.dot(hidden, output_weights) + output_bias
    return output

# Backpropagation
def backprop(x, y):
    # Calculate the error
    error = forward(x) - y
    
    # Update the weights and biases
    weights -= np.dot(error, x)
    bias -= np.sum(error, axis=0)
    output_weights -= np.dot(error, hidden)
    output_bias -= np.sum(error, axis=0)

# Example input and target
x = np.array([[1, 2], [3, 4]])
y = np.array([[1]])

# Train the network
for i in range(100):
    backprop(x, y)

Conclusion

Understanding the basics of deep learning is essential for anyone looking to delve into this exciting field. By following the resources provided on this page, you'll be well on your way to mastering the fundamentals of deep learning.