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
PyTorch A Comprehensive Guide to the DataLoader Class and Abstractions in PyTorch In this post, we'll deal with one of the most challenging problems in the fields of Machine Learning and Deep Learning: the struggle of loading and handling different types of data.
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
Deep Learning How JavaScript Libraries Are Training Neural Networks on Web Browsers For years, JavaScript has been one of the most-loved programming languages by developers. It’s primarily used for creating web browser UI's and backend business logic (with Node.js). Looking
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 How To Speed Up Object Detection Using NumPy Reshape and Transpose This is Part 4 of our ongoing series on NumPy optimization. In Parts 1 and 2 we covered the concepts of vectorization and broadcasting, and how they can be applied
Object Detection Object Detection with PyTorch and Detectron2 In this post, we will show you how to train Detectron2 on Gradient to detect custom objects ie Flowers on Gradient. We will show you how to label custom dataset
Deep Learning Evaluating Object Detection Models Using Mean Average Precision (mAP) To evaluate object detection models like R-CNN and YOLO, the mean average precision (mAP) is used. The mAP compares the ground-truth bounding box to the detected box and returns a
Tutorial How To Implement Text Recommendation on Android Using the Levenshtein Distance Typing text on your phone might not be as comfortable as writing on your PC. That's why most applications that take text as an input offer users suggested corrections and
Computer Vision Transpose Convolution Explained for Up-Sampling Images Convolutional neural networks need no introduction when it comes to image processing using Deep Neural Networks (DNNs). CNNs provide a more realistic way to extract and learn features from an
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.
Machine Learning Working with Different Genetic Algorithm Representations in Python Depending on the nature of the problem being optimized, the genetic algorithm (GA) supports two different gene representations: binary, and decimal. The binary GA has only two values for its
Reinforcement Learning Play Super Mario Bros with a Double Deep Q-Network As cool as neural networks are, the first time that I felt like I was building true AI was not when working on image classification or regression problems, but when
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.
Keras How to Build a Variational Autoencoder in Keras This tutorial gives an introduction to the variational autoencoder (VAE) neural network, how it differs from typical autoencoders, and its benefits. We'll then build a VAE in Keras that can
NumPy Nuts and Bolts of NumPy Optimization Part 3: Understanding NumPy Internals, Strides, Reshape and Transpose We cover basic mistakes that can lead to unnecessary copying of data and memory allocation in NumPy. We further cover NumPy internals, strides, reshaping, and transpose in detail.
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
NLP Generating Text Summaries Using GPT-2 on PyTorch with Minimal Training In this article I will discuss an efficient abstractive text summarization approach using GPT-2 on PyTorch with the CNN/Daily Mail dataset.
NumPy Nuts and Bolts of NumPy Optimization Part 1: Understanding Vectorization and Broadcasting In Part 1 of our series on writing efficient code with NumPy we cover why loops are slow in Python, and how to replace them with vectorized code. We also dig deep into how broadcasting works, along with a few practical examples.
Machine Learning Building a Game-Playing Agent for CoinTex Using the Genetic Algorithm Games can have complex environments, where many different factors contribute to making a decision. An entire area of artificial intelligence is devoted to building game-playing agents that can make the
Python Getting Started with PyPy The Python programming language is an interface that can be implemented in many ways. Some examples include CPython which uses the C language, Jython that is implemented using Java, and
Machine Learning 5 Genetic Algorithm Applications Using PyGAD This tutorial introduces PyGAD, an open-source Python library for implementing the genetic algorithm and training machine learning algorithms. PyGAD supports 19 parameters for customizing the genetic algorithm for various applications.
Tutorial A Guide to Paperspace's Gradient Community Notebooks Gradient Community Notebooks allow users to create, run, and share Jupyter notebooks on free GPUs. In this post Gradient Community Notebooks will be introduced and the steps to get started
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
