Keras The Absolute Guide to Keras In this article, we will dive deeper into the world of TensorFlow and Keras. If you haven't already, I would highly recommend first checking out my previous article, The Absolute
TensorFlow The Absolute Guide to TensorFlow TensorFlow is one of the most popular deep learning libraries of the modern era. Developed by Google and released in 2015, TensorFlow is considered to be one of the best
RNN Advanced Recurrent Neural Networks: Bidirectional RNNs This series gives an advanced guide to different recurrent neural networks (RNNs). You will gain an understanding of the networks themselves, their architectures, their applications, and how to bring the models to life using Keras.
RNN Advanced Recurrent Neural Networks: Deep RNNs This series gives an advanced guide to different recurrent neural networks (RNNs). You will gain an understanding of the networks themselves, their architectures, applications, and how to bring the models
TensorFlow Paperspace Joins TensorFlow AI Service Partners We're excited to announce that we've partnered with the TensorFlow team as an official AI service Partner. As one of the world's most popular open source machine learning frameworks, with
Keras How To Train Keras Models Using the Genetic Algorithm with PyGAD PyGAD is an open-source Python library for building the genetic algorithm and training machine learning algorithms. It offers a wide range of parameters to customize the genetic algorithm to work
Computer Vision Object Detection Using Mask R-CNN with TensorFlow 2.0 and Keras In a previous tutorial, we saw how to use the open-source GitHub project Mask_RCNN with Keras and TensorFlow 1.14. In this tutorial, the project is inspected to replace
Object Detection Object Detection Using Mask R-CNN with TensorFlow 1.14 and Keras Mask R-CNN is an object detection model based on deep convolutional neural networks (CNN) developed by a group of Facebook AI researchers in 2017. The model can return both the
Tutorial How to Run TensorFlow Lite Models on Raspberry Pi The deep learning models created using TensorFlow require high processing capabilities to perform inference. Fortunately, there is a lite version of TensorFlow called TensorFlow Lite (TFLite for short) which allows
Computer Vision Efficient Channel Attention for Deep Convolutional Neural Networks (ECA-Net) In this article we'll dive into an in-depth discussion of a recently proposed attention mechanism, namely ECA-Net, published at CVPR 2020.
Computer Vision GhostNet (CVPR 2020) in PyTorch and TensorFlow In this post we'll take an in-depth look at feature maps in convolutional neural networks, do a thorough review of GhostNet, and break down the code in PyTorch and TensorFlow.
Keras A Guide to TensorFlow Callbacks TensorFlow callbacks are an essential part of training deep learning models, providing a high degree of control over many aspects of your model training.
Deep Learning Image Compression Using Autoencoders in Keras In this tutorial we cover a thorough introduction to autoencoders and how to use them for image compression in Keras.
Tutorial 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.
GAN Building a simple Generative Adversarial Network (GAN) using TensorFlow Generative Adversarial Networks or GANs are one of the most active areas in deep learning research and development due to their incredible ability to generate synthetic results. In this blog, we will build out the basic intuition of GANs through a concrete example.
