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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
Reinforcement Learning: Deep Q-networks
Published:
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++: Linear regression
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Linear regression using PyTorch C++ frontend.
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.
Apache Spark: RDD operations with Scala
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In this notebook, we go over Spark’s resilient distributed dataset or RDD. The official programming guide can be found here. RDDs form the backbone of Spark’s data structures. The DataSet and DataFrame are based on RDD.
PyTorch with C++ 1
Published:
PyTorch is one of the well established libraries for modeling deep neural networks. The exposed Python API is the most commonly used one. However, the library also exposes bindings for C++. In this series of notebooks, I will try to demonstrate how to use the latter. I will be following to a large extent the documentation for the C++ frontend.
Reinforcement Learning Notes: Introduction to reinforcement learning
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Brief introduction to reinforcement learning.
Machine Learning Notes: Introduction to hidden Markov models
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Brief introduction to hidden Markov models.
Matrix approximation with SVD
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Matrix approximation with SVD
big-data
portfolio
Portfolio item number 1
Published:
Short description of portfolio item number 1
Portfolio item number 2
Published:
Short description of portfolio item number 2
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.
Apache Spark. Create a DataFrame with Scala
, , 2021
In this post, we will review how to create a DataFrame in Spark using the Scala API.