TensorFlow is one of the most prominent libraries for Python applications launched by Google in 2020. It is a free, open

TensorFlow is available for Python and C APIs and also for C , Java, JavaScript, Go, Swift, etc. Also, Third-party packages are also available for MATLAB, C#, Julia, Scala, R, Rust, etc.

Theano is a computational framework machine learning library in Python and it is a popular choice for performing neural network models. Theano works similarly to TensorFlow, but it is not as effective as TensorFlow.

NumPy is known as one of the most popular machine learning library in Python. NumPy is generally used for scientific computation. TensorFlow and other libraries use NumPy within for performing multiple operations on Tensors. Array interface is the best and the most prominent feature of NumPy.

Developers who work with Keras are fascinated with its user-friendly and modular structure. It provides an easier mechanism to signify neural networks. Keras also provides some of the best services for compiling models, processing data-sets, visualization of graphs, and much more.

It is based upon data flow graphs that are used in GPUs and CPUs with a single API. However, it is not restricted to machine learning only; you can also utilize it for dataflow and programs that are differentiable.

SciPy is a machine learning library that is used for both application developers and engineers. SciPy is one of the core packages that offer up the SciPy stack. It provides many user-friendly and effective numerical methods such as methods for numerical integration and optimization.

This user-friendly, extensible tool allows easier development of deep learning models. It is a great, effective tool for beginners, too, and can run seamlessly on CPUs and GPUs.

Keras has the ability and resources to run on top of popular deep learning libraries like TensorFlow, Theano, or CNTK. It also presents a comparably simple API that manages to also offers a lot of flexibility. This makes Keras easy to learn and simple to use.

Almost every machine learning technician or data scientist applies this module for complex mathematical computations. While NumPy is a helpful Python package for a sort of general-purpose programming tasks, it’s especially great if you want to do machine learning since it provides part of the foundation for libraries like TensorFlow.

As Pandas is a data analysis module, we can refine the data most efficiently using the pandas’ library. It allows different kinds of data structures that are able to work.

It is a must to learn for data-science and dedicatedly formulated for Python language. It is a fast, definite, and flexible platform that allows intuitive data-structures.

It enables us to operate multidimensional arrays. Arrays implementation is not there in Python so mainly the developers use NumPy in their machine learning projects. It’s easy to learn and is an open-source library that provides advanced math functionalities and a fundamental package for scientific computing with Python.

Pandas is a machine learning library in Python that gives data structures of high-level and a broad variety of tools for analysis. One of the great features of this library is the capability to translate complex operations with data using one or two commands.

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oid confusion, let’s work on the notation first. This is a frequentist method by nature. You will often find a semicolon instead of a vertical bar | to denote that we are conditioning on the parameters. In the classical perspective, our μ and σ are unknown parameters and not random variables. If we were using our Bayesian hats (which we are), we would use the vertical bar because our parameters become true random variables. This gives us consistency and also makes it simpler to understand.

SciPy supplies for various scientific computing tasks that manage data optimization, data integration, and data modification. Just like NumPy, the multidimensional models are the main purposes in SciPy, which are given by the NumPy module itself.