Welcome to the basics of deep learning tutorial. This guide will help you understand the fundamentals of deep learning, a key technology in the field of artificial intelligence.

What is Deep Learning?

Deep learning is a subset of machine learning that structures algorithms in layers to create an "artificial neural network" that can learn and make intelligent decisions on its own.

Key Concepts

  • Neural Networks: Deep learning relies on neural networks, which are inspired by the human brain.
  • Layers: Neural networks consist of layers, with each layer learning different features.
  • Activation Functions: These functions help determine whether a neuron should be activated or not.

Getting Started

Install Python

To start with deep learning, you need to install Python. You can download it from the official Python website.

Libraries

To develop deep learning models, you will need libraries like TensorFlow and Keras. Install them using pip:

pip install tensorflow
pip install keras

Practical Example

Let's try a simple example with the MNIST dataset, which contains handwritten digits.

import numpy as np
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D

# Load data
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# Preprocess data
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
input_shape = (28, 28, 1)

# Normalize data
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

# Convert class vectors to binary class matrices
y_train = np_utils.to_categorical(y_train, 10)
y_test = np_utils.to_categorical(y_test, 10)

# Build model
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# Train model
model.fit(x_train, y_train, batch_size=128, epochs=10, verbose=1, validation_data=(x_test, y_test))

# Evaluate model
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

This example builds a simple convolutional neural network (CNN) to classify handwritten digits.

Resources

For more information, check out the following resources:

Deep Learning