One Pager Cheat Sheet

• The article provides a guide to tackle interview questions for AI engineering roles, covering a range of technical and behavioral questions and providing sample answers to aid preparation, in response to growing demand for professionals in the field.
• The concept of overfitting occurs when a model fits the training data too exactly, not being able to generalize to unfamiliar data, and can be mitigated through regularization. Gradient descent is an optimization algorithm that reduces loss by iteratively tweaking model parameters. The three core classes of machine learning are supervised, which uses labeled data, unsupervised, which learns from unlabeled data, and reinforcement, which learns from engaging with an environment. The bias-variance tradeoff explains the balance between a model's simplicity and complexity, where high bias leads to underfitting and high variance leads to overfitting. Regularization is a useful technique for discouraging overfitting and improving a model's ability to handle new data.
• The text covers the implementation of the k-nearest neighbors algorithm and backpropagation algorithm for a neural network, provides a code example for deep neural networks in Python using Keras API, discusses how to parse a large CSV dataset in Python using Pandas read_csv(), and shares tips on how to debug CUDA code for GPU processing.
• The text discusses the strengths and weaknesses of TensorFlow and PyTorch, with TensorFlow being better for production and PyTorch for research, as well as the use of Pandas and NumPy for data pre-processing, and extensive experience with the scikit-learn library for various machine learning tasks.
• Linear algebra and its concepts such as vectors, matrices, and eigenvalues are crucial for machine learning algorithms, while PCA (Principal Component Analysis) is used for dimensionality reduction by transforming correlated variables into uncorrelated principal components, and derivatives of a multivariate function can be calculated using the partial derivative for each input variable and the chain rule for nested variables, forming a gradient as the multivariate derivative vector.
• The document explains p-values and statistical significance, stating that a p-value below 0.05 indicates significant results not due to chance, recommends recall and F1-score as evaluation metrics for imbalanced binary classification problems, describes Gaussian distributions as distributions characterized by mean and standard deviation parameters, and defines precision, recall, and F1-score as measures of positive predictive value, sensitivity, and model performance respectively.
• In the field of Natural Language Processing (NLP), techniques like sentence tokenization and Named Entity Recognition (NER) can be implemented using libraries like NLTK or spaCy, while topic modeling techniques like Latent Dirichlet Allocation (LDA) can be used to identify topics in text. Other important concepts include stemming and lemmatization which reduce words to their root forms, word embeddings which are vector representations of words, and the measurement of text similarity using methods such as Cosine Similarity or Word2Vec models. It also discusses approaches to handle imbalanced text data in classification tasks, the use of Attention Mechanisms to focus on specific text parts, and the concept and utility of Transformer models in NLP.