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Intro to Generative Adversarial Networks | GANs 001

   GANs consist of three terms Generative Adversarial Network. Let's understand these three terms first. Generative : A Generative Model takes input training sample from some distribution and learns to represents that distribution. Adversarial : It basically means Conflicting or Opposing. Networks : These are basically neural networks. So,Generative Adversarial Networks are deep neural network architecture comprising of two neural networks compete with each other to make a generative model. A GAN consist of two class models : Discriminative Model :- It is the one that discriminate between two different classes of data.It tries to identify real data from fakes created by the generator Generative Model :- The Generator turns noise into an imitation of the data to try to trick the discriminator Mathematically, A Generative Model 'G' to be trained on training data 'X' sampled from some true distribution 'D' is the one which, given some standard random distrib...
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Top 5 Python Libraries | Must Learn 2020


A Python library is a reusable piece of code that you may want to use in your programs/projects. Compared to languages such as C++ or C, Python libraries do not have a clear background in Python.Today, more than 137,000 Python libraries are present. Python libraries play a crucial role in creating applications for machine learning, data science, visualization techniques, image and data manipulation, and more.

 1. Pandas

Pandas is a software library written for the Python programming language for data manipulation and analysis. Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. In particular, it offers data structures and operations for manipulating numerical tables and time series.Pandas has been one of the most popular and favorite data science tools used in Python programming language for data wrangling and analysis. Data is unavoidably messy in real world. And Pandas is seriously a game changer when it comes to cleaning, transforming, manipulating and analyzing data.

2.Numpy

NumPy is a Python numerical open-source library.  A multi-dimensional array and matrix data structures are included in NumPy. A variety of mathematical operations on arrays, such as trigonometric, statistical, and algebraic routines, can be used. NumPy is a Python programming language library that provides support for large multi-dimensional arrays and matrices and a large set of high-level mathematical functions to work on these arrays.

3.TensorFlow

TensorFlow is a library of free and open-source machine learning applications. It can be used across a variety of activities, but has a special emphasis on deep neural network training and inference. Tensorflow is a symbolic math library focused on data flow and programming differentiation.TensorFlow makes it simple for beginners and experts to build desktop, mobile, web, and cloud machine learning models.


 

4. Scikit-Learn

 Scikit-learn is a free software machine learning library for the Python programming language.Through a clear Python interface, Scikit-learn offers a variety of supervised and unsupervised learning algorithms. It is licenced under a permissive simplified BSD licence and is distributed, supporting academic and commercial use, on many Linux distributions.


 

5. Keras

Keras is an open-source library which provides artificial neural networks with a Python interface. Keras serves as a TensorFlow library gui. Multiple backends, including TensorFlow, Microsoft Cognitive Toolkit, R, Theano, and PlaidMLL, were provided by Keras until version 2.3.On smartphones (iOS and Android), on the web or on the Java Virtual Machine, Keras allows users to build deep models. It also enables the use of distributed deep learning model training on graphics processing unit (GPU) clusters and tensor processing unit (TPU) clusters (TPU).


 

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