TensorFlow

How to Generate Images with Variational Autoencoders(VAE) (Create VAE from scratch using Keras and TensorFlow) Members Public

An autoencoder takes an input image and creates a low-dimensional representation, i.e., a latent vector. This vector is then used to reconstruct the original image. Regular autoencoders get an image as input and output the same image. However, Variational AutoEncoders (VAE) generate new images with the same distribution as

Derrick Mwiti
Derrick Mwiti
TensorFlow

Distributed training with TensorFlow: How to train Keras models on multiple GPUs Members Public

Training computer vision models requires a lot of time because of the size of the models and image data. Therefore, training these models can take prolonged periods of time, especially when training on a single GPU. You can reduce the training time by distributing the training across several GPUs. This

Derrick Mwiti
Derrick Mwiti
TensorFlow

Create U-Net from scratch (Image segmentation with U-Net with Keras and TensorFlow) Members Public

In the Implementing Fully Convolutional Networks (FCNs) from scratch in Keras and TensorFlow article, you saw how to build an image segmentation model with FCNs. However, due to the model's limitations, it did not perform very well in the segmenting task. In this post, you will see how to improve

Derrick Mwiti
Derrick Mwiti
TensorFlow

Transfer learning guide(With examples for text and images in Keras and PyTorch) Members Public

Training computer vision (CV) or natural language processing (NLP) models can be expensive and requires large datasets. If labeling is done manually, the process will take a longer training time and requires expensive hardware. For instance, the Generative Pre-trained Transformer 2 (GPT-2), a benchmark-setting language model created by Open AI

Kamanda Wycliffe
Kamanda Wycliffe
TensorFlow

TensorBoard tutorial (Deep dive with examples and notebook) Members Public

TensorBoard is a visualization library that enables data science practitioners to visualize various aspects of their machine learning modeling. For instance, you can use TensorBoard to: * Visualize the performance of the model. * Tuning model parameters. * Profile the executions of the program. For example, check the utilization of GPUs. *  Debug machine

Kamanda Wycliffe
Kamanda Wycliffe
TensorFlow

TensorFlow Recurrent Neural Networks (Complete guide with examples and code) Members Public

Recurrent Neural Networks (RNNs) are a class of neural networks that form associations between sequential data points. For example, the average sales made per month over a certain period. The data has a natural progression from month to month, meaning that the sales for the first month are the only

Thomas Tsuma
Thomas Tsuma
TensorFlow

Flax vs. TensorFlow Members Public

Flax is the neural network library for JAX. TensorFlow is a deep learning library with a large ecosystem of tools and resources. Flax and TensorFlow are similar but different in some ways. For instance, both Flax and TensorFlow can run on XLA. Let's look at the differences between Flax and

Derrick Mwiti
Derrick Mwiti
Flax

How to build TensorFlow models with the Keras Functional API (Examples, code, and notebook) Members Public

The Keras Functional API provides a way to build flexible and complex neural networks in TensorFlow. The Functional API is used to design networks that are not linear. In this article, you will discover that the Keras Functional API is used to create networks that: * Are non-linear. * Share layers. * Have

Derrick Mwiti
Derrick Mwiti
TensorFlow