Working On SMPL Models With Blender
Part 3 of series on 3d modeling with python script in Blender.
Part 3 of series on 3d modeling with python script in Blender.
part 4 of series on 3d modeling with python script in Blender. This part finishes the series off with a section on creating animations.
In this tutorial, we extend our implementation of gradient descent to work with a single hidden layer with any number of neurons.
You can create a custom handle for your personal account as well as one for any shared team you manage. This enables you to share your Public Notebooks easily with the world!
Gradient Community Notebooks provide free GPUs with pre-configured environments for machine learning and deep learning projects.
Deepfake technology is unlocking a new era of media production. As with all technologies, both positive and harmful use cases exist. As ethical technologists, we aspire to push the limits of what is possible, while minding the impact of the tools we create.
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.