🧠 Introduction to Deep Learning
Deep learning is a subset of machine learning that uses neural networks with many layers to model complex patterns. Python, with its rich ecosystem of libraries like TensorFlow, PyTorch, and Keras, has become the go-to language for deep learning research and development.

Key Concepts

  • Neural Networks: The building blocks of deep learning, consisting of layers of interconnected nodes (neurons).
  • Activation Functions: Non-linear functions like ReLU, Sigmoid, and Tanh that determine the output of a neuron.
  • Backpropagation: The process of adjusting weights in a neural network by propagating errors backward through the layers.
  • Optimization Algorithms: Techniques such as Gradient Descent and Adam that minimize the loss function.

Resources

For deeper exploration:

  1. Tutorial: Keras for Deep Learning
  2. Books on Deep Learning
  3. Python Libraries Guide

Practice Projects

  • 🧩 Implement a simple neural network for image classification
  • 📈 Train a model on the MNIST dataset using TensorFlow
  • 🤖 Build a chatbot with deep learning techniques
Python
Neural Network
Deep Learning Books
Python Code Sample