TensorFlow allows developers to create dataflow graphs, which are structures that describe how the data moves through a graph, or a series of processing nodes present.
Elastic Net is a regularized regression model that combines l1 and l2 penalties, i.e., lasso and ridge regression. regularization helps in overfitting problems of the models.
In any data science project life cycle, cleaning and preprocessing data is the most important performance aspect. Say if you are dealing with unstructured text data, which is complex among all the data, and you carried the same for modeling two things will happen. Either you come up with a big error, or your model will not perform as you expected.
AnyTrading is an Open Source collection of OpenAI Gym environments for reinforcement learning-based trading algorithms. AnyTrading aims to provide Gym environments to improve upon and facilitate the procedure of developing and testing Reinforcement Learning based algorithms in the area of Market Trading.
DataPrep is an open-source library available for python that lets you prepare your data using a single library with only a few lines of code. DataPrep can be used to address multiple data-related problems, and the library provides numerous features through which every problem can be solved and taken care of.
This is one of the top voted thesis papers from upGrad’s online working professional programs in partnership with one of the UK’s leading universities.
Many algorithms belong to the family of tree and ensemble, which are hard for computational problems and exposed to many hyperparameters that can be modified to improve the performance. However, manually exploring those parameters and setting those for optimized solutions is a rigorous task and often leads to unsatisfactory results.
VAR models are different from univariate autoregressive models because they allow analysis and make predictions on multivariate time series data.
RTDL (Revisiting Tabular Deep Learning) is an open-source Python package based on implementing the paper “Revisiting Deep Learning Models for Tabular Data”. The library leverages the ease of creating a Deep Learning Model and can be used by practitioners and programmers looking to implement Deep Learning models in tabular data.
Digital Signal Processors take waveforms in voice, audio, video, temperature, and then mathematically manipulate them. The key idea of a DSP is to create complex, realistic signals by precisely controlling and tuning their many parameters.
Gradient Boosting Decision Tree (GBDT) is a popular machine learning algorithm. It has quite effective implementations such as XGBoost as many optimization techniques are adopted from this algorithm. However, the efficiency and scalability are still unsatisfactory when there are more features in the data.
RecBole is a python package for various recommendation system algorithms. which covers four major categories of recommendation system with 72 algorithms
