Welcome to the deep learning tutorial! This guide will help you get started with the basics of deep learning and introduce you to some of the key concepts and tools you'll need to build your own neural networks.
Overview
- Deep Learning: 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.
- Neural Networks: A series of algorithms that can recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
- Frameworks: Libraries and software packages that provide the tools and resources needed to build and train neural networks.
Key Concepts
- Neural Network Layers: The building blocks of a neural network, including input, hidden, and output layers.
- Activation Functions: Functions that introduce non-linear properties to the network, allowing it to learn complex patterns.
- Backpropagation: An algorithm used to train neural networks by adjusting the weights and biases based on the error of the output.
Getting Started
To get started with deep learning, you'll need to install some software and libraries. Here's a simple guide:
- Install Python: Python is a popular programming language for deep learning.
- Install TensorFlow: TensorFlow is an open-source library for machine learning and deep learning.
- Install Keras: Keras is a high-level neural networks API that runs on top of TensorFlow.
For more detailed instructions, please refer to our installation guide.
Resources
- Deep Learning with Python: A comprehensive book on deep learning with Python.
- TensorFlow Documentation: The official documentation for TensorFlow.
- Keras Documentation: The official documentation for Keras.
Neural_Network
Activation_Functions
Backpropagation