Originally published on Towards AI. Introduction Casting refers to the manufacturing process in which a molten material like metal is poured into a hole or mold with the...

Are you looking for a step-by-step Data Analysis with Python Roadmap? If yes, then this article is for you. In this article, you will find a step-by-step roadmap to learn data analysis with python. Along with that, at each step, you will find resources to learn. So without any further ado, let's get started- So, you have chosen Python programming for Data Analysis.

Since the 1940s, when the digital computer was developed, it's been clear that computers could be programmed to complete extremely complex tasks. For example, they could discover proofs for mathematical theorems or play chess. In fact, computers or computer-controlled robots can perform tasks typical of humans. That's where artificial intelligence comes into play. Are you interested in how to build an AI? This article provides a basic understanding of artificial intelligence, its application, and the steps necessary for making an AI.

In 2019, Venturebeat reported that almost 87% of data science projects do not get into production. Redapt, an end-to-end technology solution provider, also reported a similar number of 90% ML models not making it to production. However, there has been an improvement. In 2020, enterprises realized the need for AI in their business. Due to COVID-19, most companies have scaled up their AI adoption and increased their AI investment.

In the previous article, we discussed in brief about encoder decoders and our approach towards solving the task of captioning. We fine-tuned a language model, this allowed the decoder to learn new words, generate brief captions and save training time. This can be referred as priming our decoder before actual training on the captioning task. Before we dirty our hands with the code, let us understand how the Vision Encoder Decoder module connects the two models (Image Encoder and Text Sequence generator) and how it deciphers what is present in the image. To understand this, you need a basic understanding of how transformer attention works and terminologies like KEY, QUERY & VALUE.

According to Taweh Beysolow, "Natural Language Processing (NLP) is a subfield of computer science that is focused on allowing computers to understand language in a'natural' way, as humans do." NLP has evolved so rapidly gaining traction in its applications inn artificial intelligence (AI). In this project, we will explore one of the most exciting NLP applications i.e. We will build a machine learning model that can categorize tweets as positive (pro-vaccine), negative (anti-vaccine) or neutral. Stay tuned and let's jump into the project.

Nowadays a lot of applications are building with the help of a flutter platform. It is an open-source software development kit. Flutter was developed by Google and released at the end of 2018. Flutter is a flexible and cross-platform software development kit used by application developers to develop applications for modern platforms like Android, Mac, Linux, iOS, Windows, and Google Fuchsia. Most machine learning development company is offering rich enchanting features that make most business ventures work more profound and smoother.

WhatsApp bot using Python: We are living in a digital era. Have you ever wondered how every company can boost its sales and marketing tactics with efficiency without spending much on the assets sounds impossible right? And that's how chatbots come into use. A chatbot is computer software that mimics human talks using artificial intelligence. They're also known as human-aware digital assistants.

In 2019, Venturebeat reported that almost 87% of data science projects do not get into production. Redapt, an end-to-end technology solution provider, also reported a similar number of 90% ML models not making it to production. However, there has been an improvement. In 2020, enterprises realized the need for AI in their business. Due to COVID-19, most companies have scaled up their AI adoption and increased their AI investment.

Part 1- The first part is about setting up the docker container for detectron2. The architecture of the detection model is a Faster region proposal convolutional neural network (FRCNN) with a Feature pyramid network(FPN) and the backbone is resnet101. We will learn the steps to train a multiclass model. Detectron2 is created by the Facebook research team. This is the official GitHub repository of Detectron2.