Implementing CycleGAN for Age Conversion
In this tutorial we'll train CycleGAN with Keras to generate images which age a subject's face, either forwards or backwards.
6 years ago
Tutorial
Implementing Gradient Descent in Python, Part 4: Applying to Any Number of Neurons
In this tutorial, we extend our implementation of gradient descent to work with a single hidden layer with any number of neurons.
6 years ago
Neural Machine Translation with TensorFlow
Learn how to build build a recurrent neural network to do French to English translation using Google's open-source machine learning library, TensorFlow.
6 years ago
Implementing Gradient Descent in Python, Part 3: Adding a Hidden Layer
In the third part of this series, the implementation of Part 2 will be extended for allowing the GD algorithm to work with a single hidden layer with 2 neurons.
6 years ago
Unpaired Image to Image Translation with CycleGAN
This article gives insights into the working mechanism of a Generative Adversarial Network and one of its popular variants, the Cycle Consistent Adversarial Network.
6 years ago
Implementing Gradient Descent in Python, Part 2: Extending for Any Number of Inputs
This is the second tutorial in the series which discusses extending the implementation for allowing the GD algorithm to work with any number of inputs in the input layer.
6 years ago
TensorFlow 2.0 in Action
To demonstrate what we can do with TensorFlow 2.0, we will be implementing a GAN mode using the Keras API and generative models.
By
Alvin Koontz
Implementing Gradient Descent in Python, Part 1: The Forward and Backward Pass
In this tutorial, which is the Part 1 of the series, we are going to make a worm start by implementing the GD for just a specific ANN architecture in which there is an input layer with 1 input and an output layer with 1 output.
6 years ago
Building a State of the Art Bacterial Classifier with Paperspace Gradient and Fast.ai
In this post, we’re going to be demonstrating how to to build a state of the art Bacterial Classification model on Gradient using the Fast.ai machine learning library.
By
Harsh Sikka
