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Batch Normalization (BN) has become a cornerstone technique in deep learning since its introduction by Sergey Ioffe and Christian Szegedy in 2015. Designed to accelerate deep network training, it has evolved over the years, addressing both its inherent limitations and adapting to new challenges in the field. This blog post

Calibrated classifiers are a critical concept in the field of machine learning, especially in the context of probability estimation. The primary goal of a classifier in machine learning is to predict the category or class of given input data. However, in many real-world applications, understanding the confidence of these predictions

Principal Component Analysis (PCA) is a powerful statistical technique used for dimensionality reduction in data analysis and machine learning. Its primary objective is to transform a set of possibly correlated variables into a set of linearly uncorrelated variables called principal components. PCA is widely used in fields such as exploratory

Proximal Policy Optimization (PPO) is an advanced reinforcement learning algorithm that has become very popular in recent years. In this comprehensive guide, we will cover: * What is PPO and how it relate to reinforcement learning * The key components and techniques used in PPO * Actor-critic method * Clipping the objective function * Adaptive

Word2vec is a popular group of related models used to produce word embeddings. Word embeddings are vector representations of words that capture semantic meaning allowing words with similar meanings to have similar representations. Word2vec models are shallow, two-layer neural networks trained on large amounts of text data to reconstruct linguistic