Welcome to the Deep Learning Basics tutorial! This guide will help you understand the fundamentals of deep learning and how to get started with your first project. Let's dive in!
What is Deep Learning? 🤔
Deep learning is a subset of machine learning that uses neural networks with multiple layers to model complex patterns in data. It's widely used in tasks like image recognition, natural language processing, and more.
deep learning overview
Core Concepts 🔍
- Neurons & Layers: The building blocks of neural networks. Each neuron processes input and passes it to the next layer.
- Activation Functions: Non-linear functions like ReLU or Sigmoid that determine the output of a neuron.
- Backpropagation: The process of adjusting weights in a network using gradient descent to minimize error.
- Loss Functions: Metrics like Mean Squared Error (MSE) or Cross-Entropy that guide the learning process.
neural network structure
Popular Frameworks 🛠
- PyTorch – Dynamic computation with Python-first syntax.
- TensorFlow – Flexible ecosystem for research and production.
- Keras – High-level API for building and training models.
Practice Tips 🚀
- Start with simple projects like MNIST digit classification.
- Use this guide for hands-on exercises.
- Experiment with different architectures and hyperparameters.
deep learning workflow