Best of Neural Networks — July 2025

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   Article

   C/C++ Community·46w

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

   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.

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   Video

   ThePrimeTime·47w

   Software Is Changing (Again) - Andrej Karpathy

   Andrej Karpathy presents his framework for understanding the evolution of software through three paradigms: Software 1.0 (traditional code), Software 2.0 (neural networks), and Software 3.0 (LLM prompts). He argues that LLMs function like new operating systems with unique properties including encyclopedic knowledge, cognitive deficits like hallucination, and jagged intelligence. The talk explores how LLMs differ from traditional technology adoption patterns by reaching consumers first, and discusses opportunities in partial autonomy applications while acknowledging current limitations around context windows, security risks, and the challenges of programming these stochastic systems.

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   Video

   Community Picks·47w

   Train a Convolutional Neural Network from Scratch: PyTorch, Next.js, React, Tailwind, Python (2025)

   A comprehensive tutorial covering the complete process of building a convolutional neural network from scratch using PyTorch to classify audio files. The guide starts with neural network fundamentals including neurons, activation functions, and training concepts like forward pass, backpropagation, and loss optimization. It then dives deep into CNN theory, explaining kernels, feature maps, spatial information preservation, and how CNNs extract hierarchical features from images. The practical implementation includes converting audio to spectrograms, training on serverless GPUs with Modal, achieving 83% accuracy, and building a Next.js frontend to visualize the model's convolutional layer outputs and feature extraction process.

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