Welcome to Module 3 — Deep Learning & Neural Networks.

In this lesson, you will explore how neural networks work and understand the technologies that power many modern AI applications. You will learn how artificial neurons process information, how neural networks learn from data, and how deep learning is applied to images, text, speech, and other complex data.

Topics Covered:

1. What is Deep Learning?
   Understand Deep Learning, its relationship with Machine Learning, and why deep neural networks are effective for complex problems.
2. Introduction to Neural Networks
   Learn the basic structure and purpose of artificial neural networks and how they are inspired by the concept of biological neurons.
3. Artificial Neurons
   Understand how individual neurons receive inputs, apply weights and biases, and produce outputs.
4. Perceptron
   Learn the basic concept of the perceptron and how it forms the foundation of simple neural network models.
5. Layers in Neural Networks
   Understand input layers, hidden layers, and output layers and how information flows through a network.
6. Weights and Biases
   Learn how weights and biases influence predictions and how they are adjusted during the learning process.
7. Activation Functions
   Understand why activation functions are required and explore commonly used functions such as ReLU, Sigmoid, and Softmax.
8. Forward Propagation
   Learn how input data moves through the neural network to generate an output or prediction.
9. Loss Functions
   Understand how a neural network measures prediction errors using loss functions.
10. Backpropagation
    Learn how errors are propagated backward through the network to determine how model parameters should be updated.
11. Gradient Descent
    Understand how optimization algorithms adjust weights and biases to reduce the model's error.
12. Training a Neural Network
    Explore epochs, batches, learning rate, optimization, and the overall neural network training process.
13. Convolutional Neural Networks — CNNs
    Understand how CNNs process visual information and why they are widely used for image recognition and computer vision.
14. Recurrent Neural Networks — RNNs
    Learn how RNNs process sequential information and understand their applications in text and time-series data.
15. LSTM Networks
    Understand Long Short-Term Memory networks and how they address some limitations of traditional RNNs when working with longer sequences.
16. Deep Learning Applications
    Explore applications of Deep Learning in computer vision, natural language processing, speech recognition, recommendation systems, healthcare, and automation.
17. Deep Learning Challenges
    Understand important challenges such as large data requirements, computational cost, training time, overfitting, and model interpretability.

Mini Project:
Build a simple image or text classification model using a neural network. Prepare the dataset, train the model, evaluate its performance, and test it on new inputs.

Learning Outcome:
By the end of this lesson, you will understand the fundamental architecture of neural networks, how models learn through forward propagation, loss calculation, backpropagation, and gradient descent, and how CNNs, RNNs, and LSTMs are used in real-world Deep Learning applications.

Lesson Resources:
Video Lesson + Presentation PDF + Quize