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Reinforcement Learning: Deep Q-networks
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Online Q-learning can experience instabilities during training. This is because by using experience sampled sequentially from the environment leads to highly correlated gradient steps. Deep Q-networks (DQN) made deep reinforcement learning a viable approach to complex sequential control problems. In this section, we introduce the vanilla DQN algorithm. Next sections will discuss various improvements that have been proposed in the literature.
PyTorch with C++: Develop a neural network
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Create a simple neural network using PyTorch C++ frontend.
C++ Programming: Using the auto keyword
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The auto keyword changed its semantics starting from the C++11 standard. In this notebook, we will review its new semantics and examine its new flavors.
Machine Learning Notes: Introduction to gradient boosting
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This post introduces gradient boosting. A nice introduction can be found at A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning.