For language-agnostic do you mean just tutorials on the mathematics? Or do you mean pseudocode? (which would be hard since a lot of the implementations have to make assumptions about things in the language, like how arrays work, or mutability, etc.)
Or do you mean just a resource that had implementations in multiple languages from the start that spanned a wide range of practically used tools? (this would be great, but seems like it would require a huge additional amount of effort, both to create and also to maintain if a given example needs to change)
Machine learning is still a field that employers are very confused about and the hiring practices vary from misguided to fundamentally misunderstanding the entire premise of the technology.
If you really think about it, there are so many wide ranging tools in machine learning that you simply cannot spread yourself thin enough to stay sharp and "interview-ready" on all of it.
Just off the top of my head, I can think of:
svm, decision tree, random forest, gradient boosted trees, simple ensembling, Gaussian processes, classical regression, complex treatment of standard errors, Bayesian regression, hierarchical regression, mixed effects models, structured prediction, outlier detection techniques like RANSAC or one-class SVM, agglomerative clustering, k-means, approximate nearest neighbor methods, missing data techniques, EM algorithm, non-parametric regression, locally weighted regression, regularized regression, model evaluation concepts like goodness-of-fit tests, likelihood tests, interpretation of R-squared, how these metrics change with different data collection changes or problems, nuanced understanding of the pitfalls of p-value reasoning, Bayesian p-values, classical confidence intervals, Bayesian credible intervals, logistic regression, softmax regression, ANOVA, linear discriminate analysis, graph algorithms, graph centrality measures, Page rank, min-cut and norm-cut, segmentation models, perceptron, and multi-layer perceptron, pros and cons of different activation functions, pros and cons of different momentum-based optimizers in deep learning, general backpropagation algorithm, nuances of how complex recurrent neural networks like LSTM or GRU work, exploding or vanishing gradient problems, nuanced understanding of weight initialization techniques in deep learning, batch normalization, stochastic gradient descent, nuanced understanding of learning rates, training diagnostics to detect overfitting or sample biases or convergence, how to use deep learning libraries to make customized network topologies, domain-specific topics like word or character embedding representations in natural language models, bag-of-words, language distributional assumptions, shallow neural models like fasttext and word2vec, multi-task learning, computer vision models, face detection, segmentation, inpainting, natural scene statistics, adversarial networks, adversarial noise, techniques for exploring interpretation of high-dimensional models like weights in a deep neural network, pivotal papers like AlexNet, ResNet, Inception, glorot, cyclegan, style transfer, learned data structures, dropout, etc. etc. Comvolutional neural networks and how the shapes of output layers derive from inputs and filter sizes, the usefulness of 1x1 convolutions, regular max pooling as well as global pooling layers, understanding of why max pooling is used to learn larger-scale spatial relationships and why capsule networks are meant to address that in a more rigorous but harder to train way. Reinforcement learning (both Q learning and deep reinforcement), AlphaGo, monte carlo tree search, general MCMC methods like metropolis and gibbs, and advanced ones like variational bayes and NUTS, and how to use them with STAN and pymc, and diagnostics to monitor convergence of sampled Markov chains, sampling methods like importance sampling (and in what situations it reduces error), rejection sampling, inverse CDF, basic pseudorandom number generation, sensitivity of all these things to IEEE floating point specs and precision, digital signal process basics like FFT, autocorrelation, time series modeling with ARMA, ARIMA, differencing, trade-offs vs just using indicator variables or lagged regressors in more standard models, hidden Markov Models, Kalman filters, particle filters, A/B testing, as well as working understanding of many SQL RDBMSs, Hadoop, Spark, appropriatness of GPUs for different methods and how to understand associated cost trade-offs, and working knowledge of data visualization tools to communicate about results.
In practice this is not true at all. For example, I’ve been turned down for senior-level roles in the past, roles requiring deep experience and rigour, after doing weeks of difficult interviews and having a great rapport with the hiring manager and getting lots of positive feedback. The reason for not choosing me was, “we are looking for someone with less experience.”
It’s just a crap shoot in any given company. Behind the scenes you don’t know what the power stuggle is and how it shapes which people have the power to hire who they want vs. which people are commanded to minimize the budget at all costs. Other companies want senior level experience at junior level prices and are willing to leave headcount unfilled for years rather than pay conpetitively.
And the broader point is that jobs like what I described (where you gain meaningful experience from your actual work projects, rather than needing to pass some outrageous barrier of expertise to get hired only to do menial work that causes your skills to atrophy) doesn’t exist. It’s not a question of out-competing other candidates.
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[ 3.8 ms ] story [ 31.7 ms ] threadOr do you mean just a resource that had implementations in multiple languages from the start that spanned a wide range of practically used tools? (this would be great, but seems like it would require a huge additional amount of effort, both to create and also to maintain if a given example needs to change)
If you really think about it, there are so many wide ranging tools in machine learning that you simply cannot spread yourself thin enough to stay sharp and "interview-ready" on all of it.
Just off the top of my head, I can think of:
svm, decision tree, random forest, gradient boosted trees, simple ensembling, Gaussian processes, classical regression, complex treatment of standard errors, Bayesian regression, hierarchical regression, mixed effects models, structured prediction, outlier detection techniques like RANSAC or one-class SVM, agglomerative clustering, k-means, approximate nearest neighbor methods, missing data techniques, EM algorithm, non-parametric regression, locally weighted regression, regularized regression, model evaluation concepts like goodness-of-fit tests, likelihood tests, interpretation of R-squared, how these metrics change with different data collection changes or problems, nuanced understanding of the pitfalls of p-value reasoning, Bayesian p-values, classical confidence intervals, Bayesian credible intervals, logistic regression, softmax regression, ANOVA, linear discriminate analysis, graph algorithms, graph centrality measures, Page rank, min-cut and norm-cut, segmentation models, perceptron, and multi-layer perceptron, pros and cons of different activation functions, pros and cons of different momentum-based optimizers in deep learning, general backpropagation algorithm, nuances of how complex recurrent neural networks like LSTM or GRU work, exploding or vanishing gradient problems, nuanced understanding of weight initialization techniques in deep learning, batch normalization, stochastic gradient descent, nuanced understanding of learning rates, training diagnostics to detect overfitting or sample biases or convergence, how to use deep learning libraries to make customized network topologies, domain-specific topics like word or character embedding representations in natural language models, bag-of-words, language distributional assumptions, shallow neural models like fasttext and word2vec, multi-task learning, computer vision models, face detection, segmentation, inpainting, natural scene statistics, adversarial networks, adversarial noise, techniques for exploring interpretation of high-dimensional models like weights in a deep neural network, pivotal papers like AlexNet, ResNet, Inception, glorot, cyclegan, style transfer, learned data structures, dropout, etc. etc. Comvolutional neural networks and how the shapes of output layers derive from inputs and filter sizes, the usefulness of 1x1 convolutions, regular max pooling as well as global pooling layers, understanding of why max pooling is used to learn larger-scale spatial relationships and why capsule networks are meant to address that in a more rigorous but harder to train way. Reinforcement learning (both Q learning and deep reinforcement), AlphaGo, monte carlo tree search, general MCMC methods like metropolis and gibbs, and advanced ones like variational bayes and NUTS, and how to use them with STAN and pymc, and diagnostics to monitor convergence of sampled Markov chains, sampling methods like importance sampling (and in what situations it reduces error), rejection sampling, inverse CDF, basic pseudorandom number generation, sensitivity of all these things to IEEE floating point specs and precision, digital signal process basics like FFT, autocorrelation, time series modeling with ARMA, ARIMA, differencing, trade-offs vs just using indicator variables or lagged regressors in more standard models, hidden Markov Models, Kalman filters, particle filters, A/B testing, as well as working understanding of many SQL RDBMSs, Hadoop, Spark, appropriatness of GPUs for different methods and how to understand associated cost trade-offs, and working knowledge of data visualization tools to communicate about results.
... and this is w...
It’s just a crap shoot in any given company. Behind the scenes you don’t know what the power stuggle is and how it shapes which people have the power to hire who they want vs. which people are commanded to minimize the budget at all costs. Other companies want senior level experience at junior level prices and are willing to leave headcount unfilled for years rather than pay conpetitively.
And the broader point is that jobs like what I described (where you gain meaningful experience from your actual work projects, rather than needing to pass some outrageous barrier of expertise to get hired only to do menial work that causes your skills to atrophy) doesn’t exist. It’s not a question of out-competing other candidates.