Gradient provides a production-ready platform as a service to accelerate the development of their AI applications. Platform capabilities include the ability for ML teams to run Jupyter Notebooks, distributed training, hyperparameter tuning, deploy models as RESTful APIs.
Learn how to build build a recurrent neural network to do French to English translation using Google's open-source machine learning library, TensorFlow.
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.
This article gives insights into the working mechanism of a Generative Adversarial Network and one of its popular variants, the Cycle Consistent Adversarial Network.
Build out complex end-to-end machine learning pipelines with the new Gradient Python SDK.
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.
To demonstrate what we can do with TensorFlow 2.0, we will be implementing a GAN mode using the Keras API and generative models.
In this post, we'll be giving a broad overview of the tools and practices the Advanced Technologies Group, or ATG, uses to explore various research, in the form of a high level research workflow.
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.
