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Serving machine learning models using TensorFlow Serving
Serving machine learning models using TensorFlow Serving
This post is a continuation of the previous post where we deployed a machine learning application using Docker, Flask, ngnix and gunicorn…
Guru Prasad Natarajan
Jun 5
Convolutional Generative Adversarial Network: “EyeGaze” Image Generator
A generative adversarial network (GAN) is a system composed of two neural networks: a generator and a discriminator. The discriminator…
Eric Muccino
Jun 5
Active Learning for Fast Data Set Labeling
Active Learning for Fast Data Set Labeling
Active learning is a special case of machine learning where a model can query a user for input. In this post, we will see how we can use…
Eric Muccino
Jun 5
Deploying machine learning models using Docker
Deploying machine learning models using Docker
This section explains how to productionize your Flask API and get it ready for deployment using Docker.
Guru Prasad Natarajan
May 23
An Investigation of RNN Memory Stability
An Investigation of RNN Memory Stability
A Recurrent Neural Networks (RNN) is a class of Artificial Neural Network that contains connections along a temporal axis, producing a…
Eric Muccino
Apr 2
Time series prediction using a simple RNN
Time series prediction using a simple RNN
For deeper networks, the obsession with image classification tasks seems to have also caused tutorials to appear on the more complex…
Guru Prasad Natarajan
Mar 19
Advantage function in Deep Reinforcement learning
Deep reinforcement learning involves building a deep learning model which enables function approximation between the input features and…
Gayatri Vadali
Mar 7
More Case Studies & Articles
LSTM vs GRU: Experimental Comparison
LSTM vs GRU: Experimental Comparison
A Recurrent Neural Network is a type of Artificial Neural Network that contains shared neuron layers between its inputs through time. This…
Eric Muccino
Mar 6
LSTMs for Time Series Prediction — Part I
Time Series Predictions
Guru Prasad Natarajan
Feb 26
Input Window Size for Deep Recurrent Reinforcement Learning
Input Window Size for Deep Recurrent Reinforcement Learning
Deep Recurrent Reinforcement Learning makes use of a Recurrent Neural Network (RNN), such as Long Short-Term Memory (LSTM) or Gated…
Eric Muccino
Feb 26
Scaling Reward Values for Improved Deep Reinforcement Learning
Scaling Reward Values for Improved Deep Reinforcement Learning
Deep Reinforcement Learning involves using a neural network as a universal function approximator to learn a value function that maps…
Eric Muccino
Feb 17
Crowd Density Estimation
Crowd Density Estimation
Introduction:
Guru Prasad Natarajan
Feb 12
Training Recurrent Neural Networks on Long Sequences
Training Recurrent Neural Networks on Long Sequences
Deep Recurrent Neural Networks (RNN) are a type of Artificial Neural Network that takes the networks previous hidden state as part of its…
Eric Muccino
Feb 11
Q Matrix Update to train Deep Recurrent Q Networks More Effectively
Q Matrix Update to train Deep Recurrent Q Networks More Effectively
Deep Reinforcement learning for Deep Recurrent Q Networks
Gayatri Vadali
Nov 27, 2018
Artificial Intelligence in Quantitative Finance and Trading
Artificial Intelligence’s primary goal is to achieve superhuman intelligence with zero human input in challenging real-world scenarios…
Gayatri Vadali
Nov 2, 2018
Development of a 5-year Information Technology Strategic Plan
Client: Town of Paradise Valley, AZ Town Government
Antoine RJ Wright
Sep 28, 2018
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