H2O.ai (H2O)Scalable machine learning and deep learning platform for big data analysis.ELI5 (Explain Like I’m 5)Helps in explaining predictions from machine learning models.POTAutomated machine learning (AutoML) tool that optimizes pipelines using genetic algorithms.PyOD (Python Outlier Detection)Specialized in detecting anomalies in large datasets.CatBoostGradient boosting library that handles categorical data efficiently.LudwigDeclarative machine learning framework from Uber that requires no coding.Dask-MLDistributed machine learning library compatible with Scikit-learn APIs.VaexOut-of-core DataFrame for machine learning on large datasets without memory constraints.LightwoodLibrary from MindsDB to build neural networks with minimal configuration.SHAP (SHapley Additive exPlanations)For explainable AI, focusing on the impact of features in machine learning models.GensimPopular for topic modeling and document similarity using unsupervised algorithms.SkaterInterpretability library for machine learning models.PyroDeep probabilistic programming library for Bayesian modeling built on PyTorch.Flux (for Probabilistic Computing)Offers tools for defining probabilistic programming with machine learning models.

I have install tensorflow and pytorch on separate environment.

I've installed both tensorflow and pytorch in separate environments and they are working fine on my machine.

installed tensorflow and pytorch in a separate environment

installed tensorflow and pytorch in a separate environment

I have installed TensorFlow in tf_env and Pytorch in pytorch_env.

Chainer MXnet Pytorch Keras(for experiment) Tensorflow(for development)

I have done this assignment.

I have done this assignment.

Assigements of Deep learningHow many libraries can be used in deep learning and machine learning tasks?1-TensorFlow: an open-source library for deep learning and numerical computations, developed by Google.2-PyTorch: a dynamic computational framework for building deep learning models, known for its research-friendly interface.3-Keras: A high-level deep learning library built on top of TensorFlow, providing an easy-to-use API for neural network construction.4-Scikit-learn: is a popular machine learning library in Python, offering a wide range of algorithms and tools for various tasks.5-Theano: Python library for efficient mathematical operations and optimization, commonly used as a backend for deep learning frameworks.6-Caffe: A deep learning framework known for its speed and efficiency, particularly for computer vision applications.7-MXNet: A flexible and efficient deep learning library supporting both imperative and symbolic programming.8-Microsoft Cognitive Toolkit (CNTK): Deep learning library by Microsoft, offering efficient computation and distributed training capabilities.9-H2O: Open-source machine learning platform with a high-level API for building and deploying models, capable of scaling to big data environments.10-Spark MLlib: A machine learning library provided by Apache Spark, offering distributed implementations of various algorithms.