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Word2Vec's Secret Sauce: Bridging Traditional and Neural Methods

2025-02-17

This blog post delves into the factors contributing to Word2Vec's success and its relationship with traditional word embedding models. By comparing models like GloVe, SVD, Skip-gram with Negative Sampling (SGNS), and PPMI, the author reveals that hyperparameter tuning is often more crucial than algorithm choice. The research demonstrates that traditional distributional semantic models (DSMs), with proper pre- and post-processing, can achieve performance comparable to neural network models. The article highlights the benefits of combining traditional and neural approaches, offering a fresh perspective on word embedding learning.

An Overview of Gradient Descent Optimization Algorithms

2025-01-25

This comprehensive blog post dives deep into gradient descent optimization algorithms, the preferred method for optimizing neural networks and many machine learning algorithms. It begins by exploring the variants of gradient descent (batch, stochastic, mini-batch), then addresses training challenges like learning rate selection and saddle point problems. The post meticulously details popular gradient-based optimization algorithms including Momentum, Nesterov Accelerated Gradient, Adagrad, Adadelta, RMSprop, Adam, AdaMax, Nadam, and AMSGrad, explaining their mechanisms and update rules. Furthermore, it covers algorithms and architectures for optimizing gradient descent in parallel and distributed settings, along with additional strategies to enhance SGD performance, such as shuffling, curriculum learning, batch normalization, early stopping, and gradient noise.