Hey everyone,
I’m excited to share that I’ve completed the Introduction to Machine Learning course on Coursera! This is a big step in my journey from VFX to machine learning.
As I continue learning, I plan to create a blog where I simplify and share my thoughts on various concepts.
Since I don’t have a background in math, my goal is to convert the mathematical concepts I learn into Python code. I’ll break these down into bite-sized snippets to aid both my understanding and help others learn.
Why Machine Learning?
AI and ML are transforming my industry, whether I’m involved or not. I’m learning ML because it has the potential to enhance VFX and revolutionize the field. At some point I believe we will get useful data out of this feild.
Whether it’s developing a consistent image-to-depth process for depth of field, fog, and projection, or enhancing image-to-vector passes for better tracking and motion blur, understanding the intuition behind AI and ML is essential.
Cost Function
What’s Next?
- Algorithms: Explored various predictive models.
- Gradient Descent: Mastered this optimization algorithm.
- Feature Scaling: Improved model efficiency with normalized input features.
- Advanced Courses: Diving into more specialized topics.
- Python Projects: Applying ML in Nuke program tools. Creating small python programs to aid in my learning.
- Portfolio Development: Showcasing my progress on my website.
Final Thoughts
The journey has just begun. I’m excited to bridge VFX and ML and share my progress. Thanks for your support!
Stay tuned for more!
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