Learn how to easily build, train and validate a Recurrent Neural Network. I’ll assume you have a basic understanding of what I’m going to talk in the next lines. I’ll be stacking layers of concepts as I move forward, keeping a very low-level language — don’t worry if you fell a little lost between lines, later I will probably clarify your doubts.
Get comfortable, it’s going to take you several minutes to read but hopefully, you’ll stick with me along the whole article. I’m gonna walk you through a foundational task that you as data scientist/machine learning engineer must know how to perform because at some point of your career you’ll be required to do so. In the context of this article, I’ll assume you have a basic understanding of what I’m going to talk in the next lines. I’ll be stacking layers of concepts as I move forward, keeping a very low-level language — don’t worry if you fell a little lost between lines, later I will probably clarify your doubts. The main idea is for you to understand what I’ll be explaining. That being said, let’s get hands-on (Btw, don’t miss any detail and download the whole project from my repo.)
I’ll start by defining the first unusual term in the title: *Sentiment Analysis *is a very frequent term within text classification and is essentially to use natural language processing (quite often referred simply as NLP)+ machine learning to interpret and classify emotions in text information. Imagine the task of determining whether a product’s review is positive or negative; you could do it yourself just by reading it, right? But what happens when the company you work for sells 2k products every single day? Are you pretending to read all the reviews and manually classify them? Let’s be honest, your job would be the worst ever. There’s where Sentiment Analysis comes in and makes your life and job easier.
There are several ways to implement Sentiment Analysis and each data scientist has his/her own preferred method, I’ll guide you through a very simple one so you can understand what it involves, but also suggest you some others that way you can research about them. Let’s place first things first: If you are not familiar with Machine Learning, you must know all algorithms are only able to understand and process numeric data (particularly floating point data), thus you cannot feed them with text and wait for them to solve your problems; instead, you’ll have to make several transformations to your data until it reaches a representative numeric shape. The common and most basic steps are:
Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data
The past few decades have witnessed a massive boom in the penetration as well as the power of computation, and amidst this information.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Artificial Neural Networks — Recurrent Neural Networks. Remembering the history and predicting the future with neural networks. A intuition behind Recurrent neural networks.
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.