Deep Learning with Python Summary

This project provides a comprehensive summary of the book Deep Learning with Python by François Chollet. The summary highlights key concepts and techniques that are particularly useful for time series analysis.

Table of Contents

• Introduction
• Key Concepts
• Deep Learning Techniques
• Applications in Time Series
• Installation
• Usage
• Contributions
• License

Introduction

In this project, I have compiled the most important insights from the book, focusing on the most important topics and those that can be effectively applied to time series data. This resource is intended for those who want to gain a deeper understanding of deep learning principles and their applications.

Key Concepts

• Overview of deep learning
• Neural networks and their architectures
• Keras and TensorFlow libraries
• Best practices for training models

Deep Learning Techniques

• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory (LSTM) networks

Applications in Time Series

• Forecasting techniques
• Handling seasonality and trends
• Model evaluation metrics for time series

Installation

To get started, clone this repository and navigate to the project directory:

git clone https://github.com/letizialib/Deep-learning-with-Python.git
cd Deep-learning-with-Python