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Another Note: This series assumes you know some high school maths to get the most from the notes takeaways sections.
Bruh, Why are you starting from scratch? There’s PyTorch, Tensorflow, etc…
Around Thanksgiving 2019, I was able to visit my grandparents and talk with my grandfather. That conversation was so life-giving in so many ways and will stay in my memory. One of the golden nuggets of wisdom he told me was:
“If you wish to get good at anything, learn the theory behind what you seek to learn/master”.
This wisdom has stayed with me to this day, including in starting this Deep Learning (DL) journey. I am more much more interested in learning the fundamentals and sharing what I’ve learned with others (in my language of course), than diving headfirst into the most popular frameworks. While I am not against frameworks (and will use them later), this is the route I have chosen for myself.
In this scenario, I’d rather learn what makes the car drive, then to just get in the car and drive 🤷️. To continue with the metaphor, you can’t learn about the car without the manual, so below is my chosen manual .
Resource used during this journey
Out of the multitude of books and online learning resources, I’ve picked this as my beginning guide into the DL world. This pick was based on reading the trial version and being impressed with Andrew Trask’s articulation of complex topics in very easy-to-understand language. For someone starting on the theory side of DL, I view this as very important.
Therefore, the posts in this series will be more-so a companion alongside the book. It’s recommended that you purchase the book as posts and Jupyter notebooks (more…
You can read the article in its entirety, on the official website of https://hackernoon.com/deep-learning-from-scratch-series-a-simple-neural-network-part-1-j8bo3vx9
Thanks a lot … I hope your thoughts, your geniuses can have as much resonance as possible.