Developers Corner

A Beginner’s Guide to Time Series Modelling Using PyCaret

When it comes to determining whether a business will succeed or fail, time is the most important factor.

How TensorFlow Probability is used in Neural Networks?

There are many cases where we get the requirements of probabilistic models and techniques in neural networks. These requirements can be filled up by adding probability layers to the network that are provided by TensorFlow.

A Hands-On Guide to SwinIR: A Transformer for Image Restoration

Image restoration techniques such as image super-resolution (SR), image denoising, and JPEG compression artefact reduction strive to recreate a high-quality clean image from a low-quality degraded image.

A Beginner’s Guide to Using Attention Layer in Neural Networks

In many of the cases, we see that the traditional neural networks are not capable of holding and working on long and large information. attention layer can help a neural network in memorizing the large sequences of data.

SceneFormer: A Transformer for 3-D Indoor Scene Generation

For 3D content creation, creating realistic 3D indoor scenes has a wide range of real-world applications.

This ML Model Can Help Robots Learn About The Relationships Between Objects

This ML Model Can Help Robots Learn About The Relationships Between Objects

MIT researchers have developed a new machine-learning model. As a result, the robots may understand…

Dataset

Popular Datasets Released By Tech Firms In 2021

This article lists some of the datasets open-sourced by big tech companies in 2021

What is Activity Regularization in Neural Networks?

Deep neural networks are sophisticated learning models that are prone to overfitting because of their ability to memorize individual training set patterns rather than applying a generalized approach to unrecognizable data.

A Hands-On Guide to IceVision Framework for Object Detection

IceVision is a framework for object detection which allows us to perform object detection in…

Neural Network Hyperparameter Tuning using Bayesian Optimization

The Bayesian statistics can be used for parameter tuning and also it can make the process faster especially in the case of neural networks. we can say performing Bayesian statistics is a process of optimization using which we can perform hyperparameter tuning.

How to Use Learning Rate Annealing with Neural Networks?

Learning rate is an important parameter in neural networks for that we often spend much time tuning it and we even don’t get the optimum result even trying for some different rates.

How to Use Lambda Layer in a Neural Network?

the lambda layer has its own function to perform editing in the input data. Using the lambda layer in a neural network we can transform the input data where expressions and functions of the lambda layer are transformed.