Ensembling Neural Network Models With Tensorflow
Boosting the performance and generalization of models by ensembling multiple neural network models.
3 months ago
TensorFlow
Movies Recommendation Systems with TensorFlow
In this blog post, we cover the three types of recommender systems, and demo their use with the MovieLens dataset.
8 months ago
Music Generation with LSTMs
In this article, we explore the progress that deep learning has made in the field of music in numerous tasks related to audio and signal processing. We then proceed to model and generate our own music files using pretty_midi.
By
Bharath K
Featured
public
Tutorial
Audio Classification with Deep Learning
This is a tutorial for conducting auditory classification within a Gradient Notebook using TensorFlow. Readers can expect to learn about the essential basic concepts of signal processing and some of the best techniques for audio classification to achieve the best desired outcomes.
By
Bharath K
Featured
public
Tutorial
BERT Transformers for Natural Language Processing
In this tutorial, we examine how the BERT language model works in detail before jumping into a coding demo. We then showed how to fine-tune the model for a particular text classification task.
By
Bharath K
Featured
public
Tutorial
pix2pix Generative Adversarial Networks
In this tutorial, we show how to construct the pix2pix generative adversarial from scratch in TensorFlow, and use it to apply image-to-image translation of satellite images to maps.
By
Bharath K
Featured
public
Tutorial
Constructing Neural Networks From Scratch: Part 1
Learn how to construct neural networks from scratch with NumPy, and simultaneously see how the internal mechanisms behind popular libraries like PyTorch and Keras are implemented.
By
Bharath K
Featured
public
Tutorial
Implementing Conditional Generative Adversarial Networks
This tutorial examines how to construct and make use of conditional generative adversarial networks using TensorFlow on a Gradient Notebook.
By
Bharath K
Featured
public
Tutorial
Interpreting Computer Vision Models
In this tutorial, we look at various methodologies that facilitate and aid the interpretation of several computer vision models, including LIME, SHAP, Grad-CAM, Guided Grad-CAM, and Expected Gradients.
a year ago
