PyTorch with C++: Linear regression
Published:
Linear regression using PyTorch C++ frontend.
Posts by Tags
API
PyTorch with C++: Develop a neural network
Published:
Create a simple neural network using PyTorch C++ frontend.
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.
algorithms
Matrix approximation with SVD
Published:
Matrix approximation with SVD
apache-spark
Apache Spark: RDD operations with Scala
Published:
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.
autonomous agents
Reinforcement Learning Notes: Introduction to reinforcement learning
Published:
Brief introduction to reinforcement learning.
big-data
Apache Spark: RDD operations with Scala
Published:
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.
c++
PyTorch with C++: Linear regression
Published:
Linear regression using PyTorch C++ frontend.
PyTorch with C++: Develop a neural network
Published:
Create a simple neural network using PyTorch C++ frontend.
C++ Programming: Using the auto keyword
Published:
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.
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.
decision making
Reinforcement Learning Notes: Introduction to reinforcement learning
Published:
Brief introduction to reinforcement learning.
deep-neural-networks
PyTorch with C++: Develop a neural network
Published:
Create a simple neural network using PyTorch C++ frontend.
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.
deep-reinforcement-learning
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.
dqn
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.
gradient-boosting
Machine Learning Notes: Introduction to gradient boosting
Published:
This post introduces gradient boosting. A nice introduction can be found at A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning.
hidden-markov-model
Machine Learning Notes: Introduction to hidden Markov models
Published:
Brief introduction to hidden Markov models.
linear-algebra
Matrix approximation with SVD
Published:
Matrix approximation with SVD
linear-regression
PyTorch with C++: Linear regression
Published:
Linear regression using PyTorch C++ frontend.
machine-learning
Machine Learning Notes: Introduction to gradient boosting
Published:
This post introduces gradient boosting. A nice introduction can be found at A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning.
Machine Learning Notes: Introduction to hidden Markov models
Published:
Brief introduction to hidden Markov models.
matrix-approximation
Matrix approximation with SVD
Published:
Matrix approximation with SVD
numerics
PyTorch with C++: Linear regression
Published:
Linear regression using PyTorch C++ frontend.
PyTorch with C++: Develop a neural network
Published:
Create a simple neural network using PyTorch C++ frontend.
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.
Machine Learning Notes: Introduction to hidden Markov models
Published:
Brief introduction to hidden Markov models.
Matrix approximation with SVD
Published:
Matrix approximation with SVD
programming
C++ Programming: Using the auto keyword
Published:
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.
pytorch
PyTorch with C++: Linear regression
Published:
Linear regression using PyTorch C++ frontend.
PyTorch with C++: Develop a neural network
Published:
Create a simple neural network using PyTorch C++ frontend.
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
Reinforcement Learning Notes: Introduction to reinforcement learning
Published:
Brief introduction to reinforcement learning.
reinforcement-learning
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.
resilient-distributed-dataset
Apache Spark: RDD operations with Scala
Published:
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.
scala
Apache Spark: RDD operations with Scala
Published:
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.
singular-value-decomposition
Matrix approximation with SVD
Published:
Matrix approximation with SVD