Deep Learning Basics
Explore the core principles of deep learning by understanding and implementing neural network activation functions such as the Swish (SiLU) function and dropout regularization. This lesson guides you through coding these techniques in Python using NumPy, demonstrating how to enhance model performance and combat overfitting through practical forward and backward passes in neural layers.
Let’s take a look at a few interview questions that cover fundamental deep learning concepts. To answer these questions, you should have a basic understanding of neural network activation functions and regularization. You will be challenged to implement activation functions and regularization techniques in Python, and may consider looking at the solution snippet to guide your implementation.
Implementing a custom neural network activation function
Implement a custom activation function called the Swish activation function in NumPy. Write a function that computes the Swish activation and its derivative, and demonstrate how it can be used in a simple neural network layer.
The Swish activation is defined as:
where σ(x) is the logistic sigmoid. The logistic sigmoid function is a mathematical function widely used in machine learning, ...