1. TENSORFLOW
So TensorFlow is a library for high-performance numerical computations with around 35,000 GitHub commits and a vibrant community of around 1500 contributors and it’s used across various scientific domains. It’s a framework where we can Define and run computations that involve tensors and tensors. We can say are partially defined computational objects again, where they will eventually produce a value that is about TensorFlow.
Let’s talk about the features of TensorFlow. TensorFlow is majorly used in deep learning models and neural networks where we have other libraries like torch and piano also but Tensorflow has hands-down better computational graphical visualizations when compared to them. Also, tensorflow reduces the error largely by 50 to 60 percent in neural machine translations. It’s highly parallel in a way that it can train multiple neural networks and multiple GPUs for highly efficient and scalable models. This parallel computing feature of TensorFlow is also called pipelining. Also, TensorFlow has the advantage of seamless performance as it’s backed by Google. It has quicker updates and frequently releases with the latest features.
Now let’s look at some applications. TensorFlow is extensively used in speech and image recognition, text-based applications, time series analysis and forecasting, and various other applications involving video detection. So favorite thing about TensorFlow is that it’s already popular among the machine learning community and most are open to trying it and some of us are already using it.
2. NUMPY
Now let’s talk about a common yet very powerful Python library called NumPy. NumPy is a fundamental package for numerical computation in Python. It stands for numerical Python as the name suggests. It has around 18,000 commits on GitHub with an active community of 700 contributors. It’s a general-purpose array processing package in a way that provides high-performance multi-dimensional objects called arrays and tools for working with them. Also, NumPy addresses the slowness problem partly by providing these multi-dimensional arrays that we talked about and then functions and operators that operate efficiently on these arrays interesting, right?
Now, let’s talk about the features of numbers. It’s very easy to work with large arrays and mattresses using NumPy. NumPy fully supports an object-oriented approach. For example, coming back to Ndra once again, it’s a class possessing numerous methods and attributes Ndra provides for larger and repeated computations. NumPy offers vectorization. It’s more faster and compact than traditional methods. We always wanted to get rid of loops and vectorization of NumPy helps us with that.
Now, let’s talk about the applications of NumPy. NumPy along with pandas is extensively used in data analysis, which forms the basis of data science. It helps in creating a powerful n-dimensional array. Whenever we talk about numpy we mention the array, we cannot do it without the mention of the powerful n-dimensional array. Also numpy is extensively used in machine learning when we are creating machine learning models as in where it forms the base of other libraries like scipy scikit-learn Etc. When you start creating the machine learning models in data science, you will realize that all the models will have their basis numpy or pandas also when number is used with scipy and matplotlib. It can be used as a replacement for Matlab.
3. SCIPY
Now let’s discuss the next library which is SciPy. So this is another free and open-source Python library extensively used in data science for high-level competitions. So this library as the name suggests stands for scientific Python and it has around 19,000 commits on GitHub with an active community of 600 contributors. It is extensively used for scientific and technical competitions. Also as it extends NumPy, it provides many user-friendly and efficient routines for scientific calculations.
Now, let’s discuss some features of SciPy. It has a collection of algorithms and functions which is built on the NumPy extension of Python. Secondly, it has various high-level commands for data manipulation and visualization. Also, the ND image function of SciPy is very useful in multi-dimensional image processing and it includes built-in functions for solving differential equations, linear algebra, and many more. So that was about the features of scipy.
Now let’s discuss its applications. Scipy is used in multi-dimensional image operations. It has functions to read images from disk into numpy arrays, write arrays to disk, discuss images, resize images etc. Solve differential equations, Fourier transforms, then optimization algorithms, linear algebra, etc.