Overview
I successfully completed the "Neural Networks and Deep Learning" course, the first part of the Deep Learning Specialization on Coursera. This foundational course provided a comprehensive introduction to the core concepts of neural networks, including:
- What is a Neural Network? Understanding the basic building blocks and architecture.
- Neural Network Basics: Logistic Regression as a simple neural network, cost functions, and gradient descent.
- Shallow Neural Networks: Implementing a neural network with one hidden layer.
- Deep Neural Networks: Building and training neural networks with multiple hidden layers.
This course laid the groundwork for understanding more advanced deep learning techniques covered in the subsequent courses of the specialization.
Key Concepts Covered
- Forward and backward propagation
- Activation functions (sigmoid, ReLU, tanh)
- Weight initialization
- Vectorization for efficient computation
- Hyperparameters