Deep Learning with Python Tutorials

Welcome to our tutorials section on deep learning with Python! Whether you're a beginner or an experienced machine learning practitioner, these tutorials will help you understand the fundamentals of deep learning and apply them to real-world problems.

Getting Started

Before diving into the tutorials, make sure you have the following prerequisites:

• Basic knowledge of Python programming
• Familiarity with machine learning concepts
• Access to a Python environment with necessary libraries installed (e.g., TensorFlow, Keras, NumPy)

Tutorials

1. Introduction to Deep Learning

In this tutorial, we'll cover the basics of deep learning, including the history, key concepts, and applications.

• Deep Learning 101

2. Building Your First Neural Network

Learn how to build a simple neural network using Python and TensorFlow.

• Build Your First Neural Network

3. Convolutional Neural Networks (CNNs)

Explore the world of CNNs and their applications in image recognition and classification.

• Convolutional Neural Networks

4. Recurrent Neural Networks (RNNs)

Discover the power of RNNs in processing sequential data and their applications in natural language processing.

• Recurrent Neural Networks

5. Generative Adversarial Networks (GANs)

Learn about GANs and their applications in generating realistic images and other data.

• Generative Adversarial Networks

Resources

For further reading and resources, check out the following links:

• TensorFlow Documentation
• Keras Documentation
• NumPy Documentation

Deep Learning