Hey devs! 👋
TL;DR: Spent 2 weeks building a neural network library in C++ to understand what happens behind the scenes of TensorFlow/PyTorch. Turns out, debugging gradient calculations at 2 AM teaches you more than any tutorial!
Why build this when ML frameworks exist?
With the AI boom, I noticed many tutorials focus on high-level APIs. so I tried peeling back the layers (literally!) and understand:
<ul>
<li>How backpropagation actually works</li>
<li>Memory management in ML contexts</li>
<li>The math behind optimizers like SGD with momentum</li>
</ul>
What I built:
<ul>
<li>Dense layers + activations (ReLU, LeakyReLU, SELU, Sigmoid, tanh)</li>
<li>SGD optimizer with momentum &amp; learning rate scheduling</li>
<li>Batch training pipelines with proper gradient handling</li>
<li>CSV/binary dataset support</li>
<li>Cross-platform build system</li>
</ul>
Key lessons learned:
<ol>
<li>Gradient debugging is an art - Nothing humbles you like realizing you returned the loss with an extra negative sign 😅</li>
<li>Memory management matters - Deciding when to share things by references and when to make copy and share them really fried the brain</li>
<li>C++ + ML = Fun but challenging - Though the complexity of C++ scares a few but I love the language</li>
</ol>
What’s next:
<ul>
<li>A Tensor datatype or class</li>
<li>Convolutional layers</li>
<li>Adam/RMSprop optimizers</li>
</ul>
The real win? This taught me more about ML fundamentals than months of high-level tutorials. When you implement backprop yourself, you truly get it.
🔗 Check it out here: <a href="https://github.com/CuriosityKilledTheCache/Deep-in-scratch_Maths_the_catch" target="_blank" rel="noopener nofollow">Neural Network Library in C++</a>
Questions for the community:
<ul>
<li>What ML concept surprised you most when you implemented it?</li>
<li>Any suggestions for optimizing the program?</li>
<li>War stories from your “from scratch” projects?</li>
</ul>
P.S. Yes, I went down several rabbit holes. No regrets! 🐰
#cpp #machinelearning #neuralnetworks #fromscratch #learning

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ML & AI

A developer shares their experience building a neural network library in C++ from scratch over two weeks to understand the fundamentals behind ML frameworks like TensorFlow and PyTorch. The project includes dense layers, various activation functions, SGD optimizer with momentum, batch training pipelines, and dataset support. Key insights include the challenges of gradient debugging, importance of memory management in ML contexts, and how implementing algorithms from scratch provides deeper understanding than high-level tutorials. Future plans include adding tensor datatypes, convolutional layers, and additional optimizers.

🚀 Built a Neural Network Library in C++ from Scratch - Here's What I Learned About the Fundamentals Behind ML Frameworks

That’s awesome! I actually built something similar a Multilayer Perceptron in JavaScript myself. It’s always amazing to see how much you can learn by building things from scratch. I really respect your dedication to understanding the fundamentals behind ML frameworks. It’s not easy, especially with something like C++, but it really gives you a deep understanding of how things work under the hood. 
You may check it out here: <a href="https://github.com/MOHD-SHAMOON-04/NeuralNet" target="_blank" rel="noopener nofollow">https://github.com/MOHD-SHAMOON-04/NeuralNet</a>