Objective:
The primary objective of this lesson is to equip learners with the foundational skills and knowledge necessary to begin developing proficiency in deep learning. By the end of this lesson, learners will be able to understand and apply the core concepts and techniques that underpin deep learning, including neural networks, backpropagation, and loss functions. This practical knowledge will empower them to tackle real-world machine learning problems and set the stage for further study and innovation in the field.
Comprehensive Content Overview:
Deep learning is a subset of machine learning where artificial neural networks — algorithms inspired by the human brain — learn from large amounts of data. Deep learning drives many artificial intelligence (AI) applications and services that improve automation, performing analytical and physical tasks without human intervention.
Core Components:
- Artificial Neural Networks (ANNs): Composed of input, hidden, and output layers, ANNs are the backbone of deep learning..
- Activation Functions: Functions like ReLU or Sigmoid that help neural networks make complex decisions..
- Cost Functions: Measures how well the neural network performed to guide its training..
- Backpropagation: The process by which neural networks learn from errors and adjust their weights..
- Optimization Algorithms: Algorithms such as Gradient Descent that optimize neural networks to improve performance..
- Overfitting & Regularization: Techniques to ensure that models generalize well to new, unseen data..
