The Principal Component Analysis is a popular unsupervised learning algorithm that is widely known for dimensionality reduction. It increases the interpretability and also reduces the loss of information while reducing the dimensionality.
First Order Motion Model is an Open source library that allows you to create 3D animated videos using facial capture videos and still images. The image animation consists of generating a video sequence so that an object in a source image is animated according to the motion of the driving video.
Adding Attention layer in any LSTM or Bi-LSTM can improve the performance of the model and also helps in making prediction in a accurate sequence. very helpful in NLP modeling with big data
Before 2015 when the first attention model was proposed, machine translation was based on the simple encoder-decoder model, a stack of RNN and LSTM layers. The encoder is used to process the entire sequence of input data into a context vector. This is expected to be a good summary of input data. The final stage of the encoder is the initial stage of the decoder.
image transformation are some basic techniques to deal with images where we performs various procedure to make a image data to deal well with image modeling processes.
Images consist of a lot of pixels that help retain their clarity. Still, as the number of images to process increases its size, it can significantly slow down the system’s performance. We can use the Image Reconstruction technique to overcome this situation, which comes under Unsupervised Machine Learning.
GANsNRoses is an open-source library that creates new Images by building a mapping that takes face images and produces them to anime drawings of faces. With the contents of the image being preserved, the same face could be represented in many different ways in the anime. GANsNRoses consists of a function that takes a content code recovered from the face image and a style code, a latent variable that produces an anime face. GNR uses the same image with different augmentations to form a batch, constraining the spatial invariance in style codes, all style codes being the same across the batch. The Diversity Discriminator present looks at batch-wise statistics by explicitly computing the minibatch standard deviation across the batch. This ensures diversity within each batch of images, in turn producing unique animated images.
Monte Carlo simulation, also known as the Monte Carlo method or multiple probability simulations, is a mathematical tool used to estimate the possible outcomes of uncertainty in an event.
OpenBox is a standalone python package as an open source for efficient generalized Block-Box optimization. It is designed to satisfy all aspect of generalized BBO.
DORO is a robust outlier refinement of DRO that takes inspiration from its robust statistics. The refined risk function, which prevents DRO from overfitting to potential outliers, intuitively, the new risk function adaptively filters out a small fraction of data with high risk during training, which is potentially caused by outliers.
Innovation of deep neural networks has given rise to many AI-based applications and overcome the difficulties faced by computer vision-based applications such image classification, object detections etc. and frameworks like Tensorflow, PyTorch, Theano, Keras, MxNet has made these task simpler than ever before.
The augmented dickey- fuller test is an extension of the dickey-fuller test, which removes autocorrelation from the series and then tests similar to the procedure of the dickey-fuller.
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