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.
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.
forecasting analysis for one single future value using LSTM in Univariate time series. LSTM is a RNN architecture of deep learning van be used for time series analysis.
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.
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.
Mito is an open-source python library. For creating interactive dashboards and graphs with the. Mito makes EDA faster.
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.
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.
AugLy, a new open source Python library that helps AI researchers to make use and create data augmentations to improve the robustness of their created machine learning models.
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.
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.
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.
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