Some moments occur when we notice that we have seen this term somewhere and we don’t remember what it exactly means. Entropy and information gain belong to the same categories of terminologies.
We will try to understand entropy and information gain in a very simple and uncomplicated way. A basic understanding of the decision tree is required to understand these two terminologies. If you haven’t yet seen what is a decision tree, that really cool you are at the right address, you’ll take away something memorable from here.
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Writing after a long time, Here’s the basic + in-depth explanation of Generative adversarial networks and Style-Based Generative Adversarial networks. This explanation will resolve all your doubts regarding GANs, as we know understanding GANs is a bit confusing.
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Introduction
GAN is simply a generative model that generates new data from the input data. They are used to perform unsupervised operations. They work majorly with image data and also audio data. The Generative adversarial networks consist of a generator and a discriminator. Both are kinds of neural networks that compete with each other. GANs are very computationally expensive with a requirement of extremely high-end GPUs and lots of time to get trained.
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Let’s understand the linear regression without any elaboration and in a short manner, that won’t waste your time rather than it will deliver you just the needed information. That will help you to grasp the linear regression and would also make your way easy to know logistic regression.
What you will learn
- An overview of the basics.
- Working
- Cost function
- Gradient Descent
- Assumptions
- Implementing in python from scratch
- Metrics in linear regression
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