Deep-Dive into Tensorflow Activation Functions
Key Takeaways
Explores Tensorflow activation functions for neural network design and implementation
Original Description
You've learned how to use Tensorflow. You've learned the important functions, how to design and implement sequential and functional models, and have completed several test projects. What's next? It's time to take a deep dive into activation functions, the essential function of every node and layer of a neural network, deciding whether to fire or not to fire, and adding an element of non-linearity (in most cases).
In this 2 hour course-based project, you will join me in a deep-dive into an exhaustive list of activation functions usable in Tensorflow and other frameworks. I will explain the working details of each activation function, describe the differences between each and their pros and cons, and I will demonstrate each function being used, both from scratch and within Tensorflow. Join me and boost your AI & machine learning knowledge, while also receiving a certificate to boost your resume in the process!
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
AI explanation not available for this lesson yet
This lesson is still being prepared for the AI tutor. In the meantime, explore lessons that are ready.
Browse explainer-ready lessons →
Related Reads
📰
📰
📰
📰
I Went Back to 2012 to Build AlexNet From Scratch
Medium · Deep Learning
Detecting Rice Leaf Diseases with Deep Learning
Medium · Deep Learning
Trained a neural net to reconstruct Bad Apple in real-time.
Reddit r/deeplearning
AI/ML Under the Hood — Part 29: CNN Breaking News: Proximity Matters
Medium · Deep Learning
🎓
Tutor Explanation
DeepCamp AI