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
Machine Learning Beginner's Guide to Boltzmann Machines in PyTorch As research progressed and researchers could bring in more evidence about the architecture of the human brain, connectionist machine learning models came into the spotlight. Connectionist models, which are also
Deep Learning Fine-Tuning Shallow Networks with Keras for Efficient Image Classification Neural networks with extensively deep architectures typically contain millions of parameters, making them both computationally expensive and time-consuming to train. In this tutorial, we'll achieve state-of-the-art image classification performance using
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
Computer Vision Faster R-CNN Explained for Object Detection Tasks This article gives a review of the Faster R-CNN model developed by a group of researchers at Microsoft. Faster R-CNN is a deep convolutional network used for object detection, that
Computer Vision Paper Review: Funnel Activation for Visual Recognition (ECCV 2020) Component-based research in computer vision has accelerated significantly over the recent years, especially with the advent of adversarial attacks and attention mechanisms. One of the primary components which has been
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.
Neural Network Neural Architecture Search Part 4: REINFORCE Gradient and Evaluation So far we've built two major components of our neural architecture search (NAS) pipeline. In the second part of this series we created a model generator which takes encoded sequences
Neural Network Capsule Networks: A Quick Primer Whether you're new to deep learning or a serious researcher, you've surely encountered the term Convolutional Neural Networks. They are one of the most researched and top-performing architectures in the
Deep Learning A Review of Popular Deep Learning Architectures: DenseNet, ResNeXt, MnasNet, and ShuffleNet v2 The aim of this three-part series has been to shed light on the landscape and development of deep learning models that have defined the field and improved our ability to
Deep Learning A Review of Popular Deep Learning Architectures: ResNet, InceptionV3, and SqueezeNet Previously we looked at the field-defining deep learning models from 2012-2014, namely AlexNet, VGG16, and GoogleNet. This period was characterized by large models, long training times, and difficulties carrying over
Deep Learning A Review of Popular Deep Learning Architectures: AlexNet, VGG16, and GoogleNet Problems ranging from image recognition to image generation and tagging have benefited greatly from various deep learning (DL) architectural advancements. Understanding the intricacies of different DL models will help you
Neural Network Neural Architecture Search Part 3: Controllers and Accuracy Predictors In the first part of this series we saw an overview of neural architecture search, including a state of the art review of the literature. In Part 2 we then
Neural Network Neural Architecture Search Part 2: Search Space, Architecture Design and One-Shot Training This tutorial covers a step-by-step walkthrough of coding neural architecture search for multilayer perceptrons in Python with Keras.
Research Neural Architecture Search Part 1: An Overview With countless options to design neural networks, an effective architecture search algorithm would be game-changing. Here we look at the state of the art.
Keras Working With The Lambda Layer in Keras In this tutorial we'll cover how to use the Lambda layer in Keras to build, save, and load models which perform custom operations on your data.
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 Generating an interactive Pix2Pix model with Gradient° and ml5.js Learn to train a generative image model using Gradient° and then porting the model to ml5.js, so you can interact with it in the browser.
Series: Optimization Intro to Optimization in Deep Learning: Vanishing Gradients and Choosing the Right Activation Function An look into how various activation functions like ReLU, PReLU, RReLU and ELU are used to address the vanishing gradient problem, and how to chose one amongst them for your network.
Computer Vision Style Transfer Part 2: Face Swap! Learn how to use neural style transfer to swap faces in images.
Generative Models Recurrent Language Models with RNNs In this article, we will first try to understand the basics of language models, what Recurrent Neural Networks are and how can we use them to solve the problem of language modeling.
