What happened? I honestly have no clue what is going on here. submitted by /u/Honest___Opinions [link] [Kommentare]

Hi everyone, I'm a Computer Science + Applied Mathematics major at a T15 CS university, focusing on machine learning, and I'll be an incoming junior this fall. My long-term goal is to land ML-focused internships and eventually work in machine learning or AI-related roles. I recently completed an introductory AI/ML course that covered the fundamentals of: * Data preprocessing (handling missing data, feature scaling, train/test splits, categorical encoding) * Regression (linear, polynomial, SVR, decision trees, random forests) * Classification (logistic regression, KNN, SVMs, Naive Bayes, decision trees, random forests) * Clustering (K-Means, hierarchical clustering) * Association rule learning (Apriori, Eclat) * Reinforcement learning (UCB, Thompson Sampling) * NLP (tokenization, bag-of-words, sentiment analysis) * Deep learning (ANNs, CNNs) * Dimensionality reduction (PCA, LDA, Kernel PCA) * Model selection and boosting (cross-validation, grid search, XGBoost) I've also completed two research/internship experiences: * Built a Human Activity Recognition model using KNN. * Developed a Louvain clustering pipeline for beauty product datasets. From a coursework perspective, I've completed Linear Algebra, Calculus III, and will soon be taking Applied Linear Algebra and Probability & Statistics. Given my current background, what projects, activities, courses, competitions, or skills would you recommend I focus on over the next year to become a stronger candidate for ML internships? Are there any gaps in my knowledge that stand out? submitted by /u/Reasonable_File663 [link] [Kommentare]

(Disclaimer: This post was drafted with the help of AI to keep it concise, but the research and work are entirely mine.) I’ve been building a spiking neural network (SNN) engine from scratch on my laptop as a solo project. To see if it was actually tracking anything useful outside of my own custom puzzles, I finally tested it on a standard benchmark: NARMA-10. It was a pretty humbling reality check. It flunked completely. The Failure NARMA-10 requires continuous time-series tracking. When I measured the spiking reservoir, its memory was barely two steps deep when the benchmark needed ten. Tweaking standard dials like input volume or cell lifespans did absolutely nothing to fix it. The Small Fix To get it out of the gutter, I tried a basic concept from neuroscience: heterogeneous wire lengths (adding discrete time delays to the inputs). This spread the past out across the network. It worked well enough to triple the memory depth and finally match a basic line-fitting baseline. It's nothing to brag about yet, but it at least made the network usable on the task. The 15x Efficiency Trade-off I want to be completely transparent—smooth, continuous units still beat this spiking net on absolute accuracy almost everywhere. Spikes are definitely not a magic shortcut to out-compute modern architectures. The only real win is pure efficiency. On a small 512-cell recognition task where the spiking net managed to tie a continuous net in accuracy, I counted the exact internal operations (multiply-and-adds): Standard continuous nets grind through every single cell on every tick, busy or not. My spiking net only does work when a cell actually fires. The rest stays silent. The tally: The spiking net used 15 times less internal arithmetic to get the exact same answer on my standard laptop hardware. Moving Forward This benchmark taught me that spikes don't think better; they just think cheaper when the problem space allows it. Instead of just grinding away trying to force prettier engineering benchmarks or out-accuracy standard models, I'm taking a step back to explore some new, creative avenues for how this engine can actually be utilized. submitted by /u/Gutbole [link] [Kommentare]