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
Data Science Introduction to Time Series Forecasting: Regression and LSTMs In the first part of this series, Introduction to Time Series Analysis, we covered the different properties of a time series, autocorrelation, partial autocorrelation, stationarity, tests for stationarity, and seasonality.
Reinforcement Learning Getting Started With OpenAI Gym: Creating Custom Gym Environments This post covers how to implement a custom environment in OpenAI Gym. As an example, we implement a custom environment that involves flying a Chopper (or a helicopter) while avoiding obstacles mid-air.
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
Reinforcement Learning Getting Started With OpenAI Gym: The Basic Building Blocks In this article, we'll cover the basic building blocks of Open AI Gym. This includes environments, spaces, wrappers, and vectorized environments.
Computer Vision Build A Flask Web App To Compress Images Using A Variational Autoencoder In this tutorial, we'll build a web application using Flask which will allow the user to upload images to be encoded (i.e., compressed) using a pre-trained variational autoencoder (VAE)
Tutorial How to Train A Question-Answering Machine Learning Model (BERT) In this article, I will give a brief overview of BERT based QA models and show you how to train Bio-BERT to answer COVID-19 related questions from research papers.
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
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.
