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Category: Developers Corner

Developers Corner
Victor Dey

Exploring Panda Gym: A Multi-Goal Reinforcement Learning Environment

The gym is an open-source toolkit for developing and comparing reinforcement learning algorithms. What makes it easier to work with is that it makes it easier to structure your environment using only a few lines of code and compatible with any numerical computation library, such as TensorFlow or Theano.

Developers Corner
Vijaysinh Lendave

Hands-On Guide to Generating Artificial Faces Using Progressive GAN

When it comes to large datasets with higher pixel values, GAN generates the images with sharp pixels that look crispy though make the training unstable. Generating high-resolution images is a challenging task because the generator must know the details and structures involved in images. The high-resolution images can cause any issues that the discriminator can easily spot; therefore, the whole training process fails.

Developers Corner
Victor Dey

Python 3.9 vs Python 3.10: A Feature Comparison

In this article, we will compare the features of two of the most recent versions of the Python programming language, Python 3.9 and Python 3.10, with their respective examples and try to explore what is different and new.

Developers Corner
Vijaysinh Lendave

Hands-On Tutorial on Performance Measure of Stratified K-Fold Cross-Validation

The most used validation technique is K-Fold Cross-validation which involves splitting the training dataset into k folds. The first k-1 folds are used for training, and the remaining fold is held for testing, which is repeated for K-folds. A total of K folds are fit and evaluated, and the mean accuracy for all these folds is returned.

Developers Corner
Victor Dey

Hands-On Guide to PaDELPy for ML Model Building

PaDELPy is an open-source library that provides a Python wrapper for the PaDEL-Descriptor and a molecular descriptor calculation software. The PaDEL-Descriptor can be used to work on scientific data to help calculate the molecular fingerprint of specific molecules used to build scientific machine learning models.

Developers Corner
Vijaysinh Lendave

Complete Guide To Descriptive Statistics in Python for Beginners

We are learning statistics because we can; observe the information properly, draw the conclusion from the large volume of the dataset, make reliable forecasts about business activity and improve the business process. To do all kinds of these analyses, statistics are used. Further, it is classified into two types: Descriptive and Inferential statistics.

Developers Corner
Vijaysinh Lendave

Hands-On Guide To Custom Training With Tensorflow Strategy

Distributed training in TensorFlow is built around data parallelism, where we can replicate the same model architecture on multiple devices and run different slices of input data on them. Here the device is nothing but a unit of CPU + GPU or separate units of GPUs and TPUs. This method follows like; our entire data is divided into equal numbers of slices. These slices are decided based on available devices to train; following each slice, there is a model to train on that slice.

Developers Corner
Yugesh Verma

Hands-On Guide To Triton By OpenAI

gramming language that provides features like writing GPU codes without having so much experience. We can also write efficient programs of GPU programming in just a few lines of code. It takes a lot of effort to write a single program. These features also boost the kernels’ efficiency up to 2x than the torch implementations.

Developers Corner
Yugesh Verma

Hands-On Guide To PyKale: A Python Tool for Multimodal and Transfer Learning

The library has a pipeline-based API that unifies the workflow in several steps that helps to increase the flexibility of the models. These APIs are designed to accomplish the following steps of any machine learning workflow
The pykale supports graph, images, text and videos data that can be loaded by PyTorch Dataloaders and supports CNN, GCN, transformers modules for machine learning.