๐ก๐ฒ๐๐ฟ๐ฎ๐น ๐ก๐ฒ๐๐๐ผ๐ฟ๐ธ ๐ณ๐ฟ๐ผ๐บ ๐ฆ๐ฐ๐ฟ๐ฎ๐๐ฐ๐ต: ๐ก๐ผ ๐ฃ๐๐ง๐ผ๐ฟ๐ฐ๐ต & ๐ง๐ฒ๐ป๐๐ผ๐ฟ๐๐น๐ผ๐. ๐๐๐๐ ๐ฝ๐๐ฟ๐ฒ ๐บ๐ฎ๐๐ต
Beneath the surface of AI that feels like magic, lies elegant mathematics and careful coding. For those who love to learn deeper, there's something incredibly satisfying about building a neural network from scratchโnot using PyTorch or TensorFlow packages but with nothing but pure mathematics.
If you have ever wanted to fully understand deep neural networks, this is your opportunity. I have created a 1-hour video on Vizuaraโs YouTube channel, where we will understand and implement a neural network step-by-step: https://www.youtube.com/watch?v=A83BbHFoKb8&feature=youtu.be
๐๐ถ๐ฟ๐๐ ๐ฏ๐ฌ ๐ ๐ถ๐ป๐๐๐ฒ๐: ๐ง๐ต๐ฒ๐ผ๐ฟ๐ ๐ฎ๐ป๐ฑ ๐ ๐ฎ๐๐ต
This segment goes into the equations and logic behind neural networks.
1) We start with the fundamentalsโno shortcuts, no pre-built libraries.
2) Problem statement and dataset
3) Defining the neural network architecture by hand
3) Setting up forward propagation
4) What exactly is backpropagation, and why is it so central to deep learning?
5) Setting up the mathematical equations for gradient descent.
๐ก๐ฒ๐
๐ ๐ฏ๐ฌ ๐ ๐ถ๐ป๐๐๐ฒ๐: ๐ฃ๐๐๐ต๐ผ๐ป ๐ฐ๐ผ๐ฑ๐ถ๐ป๐ด
Here, you will see every line of code written from scratchโinitializing weights, performing forward and backward passes, and updating the parameters using gradient descent. We will use NumPy, allowing us to manipulate arrays and matrices while staying close to the mathematical essence of neural networks.
You will get a "high" if your from-scratch code works, and you see that you can make good predictions on the dataset.
In a world where high-level libraries handle the heavy lifting, you might wonder, โWhy bother with this hard way? I can do all of this in 10 lines of code which ChatGPT can give me.โ Hereโs why:
1) Deep understanding: Pre-built frameworks are powerful but abstract. Writing your own neural network forces you to understand how each component works together.
2) Debugging: When something goes wrong in a complex model, having a firm grasp of the fundamentals can save hours of frustration.
3) Foundational skills: Learning to code from scratch builds confidence and lays a solid foundation for more advanced topics like custom layers, optimizers, and model architectures.
This lecture is perfect for anyone curious about AI and machine learningโwhether you are just starting or looking to strengthen your foundational knowledge. You donโt need an extensive math background, just a willingness to learn and follow along.
If this sounds like something you would enjoy, check out the full video. By the end, you will have your very own neural network runningโnot because a library did it for you, but because you built it with your own hands. Watch the full video here:
Letโs make AI a little less magical and a lot more understandable.
Let me know your thoughts after watching.
Wrote the entire code from scratch, following your tutorial. Learned a lot. Thanks, as always, for the excellent videos, Dr. Sreedath! โค๏ธ