Day 185 of 365: Activation Functions & Loss Functions ????????
Ajinkya Deokate
Data Scientist | Researcher | Author | Public Speaking Expert @PlanetSpark | Freelancer
Hey, Activator!
Welcome to Day 185 of our #365DaysOfDataScience journey! ??
By experimenting with these, we’ll discover what works best in different scenarios. As always, I’ll be learning alongside you!
?? What We’ll Be Exploring Today:
- We'll dive into two key pieces of neural networks today:??
??- Activation functions like Sigmoid, ReLU, and Tanh that help your network learn complex patterns.??
??- Loss functions like Cross-Entropy and Mean Squared Error (MSE) that tell us how far off our predictions are.
?? Learning Resources:
- Let’s start by reading some articles to get a solid grasp of how different activation and loss functions work.
???? Today’s Task:
- Time to get hands-on! We’ll implement a few activation functions in our neural network and see how they affect its learning.
- Then, we’ll experiment with different loss functions to understand how they change the network’s performance.
Happy Learning & See You Soon!
***