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This article introduces classic optimization algorithms, distinguishing direct methods that require convexity and closed‑form solutions from iterative first‑ and second‑order methods, and explains their applicability, underlying theory, and key references for solving smooth unconstrained problems.
This article explains how to construct a multi‑layer perceptron that implements XOR and arbitrary n‑bit Boolean functions, detailing the hidden‑node count for single‑ and multi‑layer designs and the minimal number of network layers needed.
This article explains why Gaussian (normal) distribution sampling is essential, describes the mathematical transformation from a standard normal, and compares several practical algorithms—including inverse transform, Box‑Muller, Marsaglia polar, rejection sampling, and Ziggurat—highlighting their implementation steps and efficiency considerations.
This article introduces ensemble learning as a meta‑algorithm that combines multiple base classifiers, explains the two main strategies—Boosting and Bagging—covers their bias‑variance trade‑offs, outlines essential steps, and provides concrete examples such as AdaBoost, Random Forest, and GBDT applied to user age prediction.
This article explains why attention mechanisms were introduced into Seq2Seq models, how they address the limitations of fixed‑length encoding, the role of bidirectional RNNs, and showcases their impact on machine translation and image captioning with illustrative diagrams.
This article explains the Seq2Seq encoder‑decoder framework, its benefits for various sequence modeling tasks, and compares common decoding strategies such as greedy search and beam search, while also introducing attention and other enhancements for improved performance.
This article explains how LSTM networks overcome RNN limitations by using input, forget, and output gates with sigmoid and tanh activations, describes the core update equations, discusses alternative activation functions and hard‑gate variants, and provides references for deeper study.
This article explains what Recurrent Neural Networks are, how they create text representations, why they suffer from gradient vanishing or explosion, and what architectural improvements such as LSTM, GRU, residual connections and gradient clipping can do to overcome these issues.
This article explains how deep reinforcement learning, built upon traditional Q‑learning and enhanced with techniques like experience replay, enables agents to play Atari video games directly from raw pixel inputs, illustrating the key differences, processing steps, and the significance of this breakthrough in AI.
This article introduces the fundamental concepts of reinforcement learning, describing its origins, key components such as agents, environments, states, actions, and rewards, explaining the Markov decision process framework, and highlighting common algorithms like Q‑learning, policy gradients, and actor‑critic methods.
