Deep Learning with Python Tutorial

Welcome to the tutorial on deep learning with Python! This guide will take you through the basics of deep learning, starting from the installation of necessary libraries to building and training your first neural network.

Prerequisites

Before you start, make sure you have the following prerequisites:

Python installed on your system
Basic knowledge of Python programming
Familiarity with libraries like NumPy, Pandas, and Matplotlib

Install Required Libraries

First, you need to install the required libraries. You can use pip to install them:

pip install numpy pandas matplotlib tensorflow

Introduction to 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.

Types of Deep Learning Models

Convolutional Neural Networks (CNNs): Used for image recognition and classification.
Recurrent Neural Networks (RNNs): Used for sequence data like language and time series.
Generative Adversarial Networks (GANs): Used for generating new data with similar statistics to real-world data.

Building Your First Neural Network

Now, let's build a simple neural network using TensorFlow and Keras.

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Create the model
model = Sequential()
model.add(Dense(128, input_dim=784, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation='softmax'))

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

# Train the model
model.fit(x_train, y_train, epochs=10, batch_size=32)