I've been learning C and have practiced the basics, especially loops and recursion. I originally wanted to build my own AI model to understand how neurons and weights work, but I'm realizing that a full neural network or language model is probably too ambitious for a first project. I'd like recommendations for learning machine learning, understanding the math behind neural networks, and choosing smaller projects that will help me build toward that goal. I'm also wondering which parts of C—such as arrays, pointers, functions, structs, and memory management—I should focus on first, and whether I should eventually use Python for machine-learning experiments.
3 Answers
The important part isn’t just writing loops around neurons—you also need some math and machine-learning concepts. Focus on vectors, matrices, functions, derivatives, probability, loss functions, gradient descent, and backpropagation. You can implement the small exercises in C to learn how they work, but Python is commonly used for practical machine-learning work because its libraries make experimentation much easier.
A good first exercise is a model that learns a simple relationship, such as predicting y from x. Start with a few training examples, initialize a weight, calculate a prediction, measure the error, and repeatedly adjust the weight. Once that works, try a perceptron that classifies two categories, then add more inputs or layers. This gives you the basic training loop without pretending you’re building a chatbot.
Start smaller and make sure your C fundamentals are solid first. Arrays, pointers, functions, structs, file handling, and basic debugging will all be useful. After that, try a simple project such as a text adventure, notes app, or program that reads and processes a dataset. Those projects will teach you more right now than jumping straight into a large AI system.
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