On May 8, 2018, Google I/O was held at Shoreline Amphitheatre in Mountain View, California. If you are wondering what Google I/O is, don't worry, I've got your back. In the Keynote, Sundar Pichai, the CEO of Alphabet Inc. (Google's parent company), shared the then-latest developments that Google had been working on. One of the projects that he spoke about was something that maybe no one saw coming; an application of Artificial Intelligence (AI), soon to be on our own smartphones, that left the world in awe. The project was called'Google Duplex'. This initiative enables AI to place a phone call to a hair salon, converse just like us humans, and book a haircut appointment - and the part where your jaws drop is that all of this takes place in the background on your phone, without any intervention of yours!
Digital marketing in the modern era is first and foremost about data. With the huge amount of data available, it is increasingly common to see marketing become the top priority for many businesses because it is directly linked to increasing revenue. Businesses these days need to understand consumer behavior to optimize marketing campaigns. In this article, we'll look at how machine learning can help businesses improve and strengthen their marketing efforts. Also called statistical learning, machine learning is part of the race for useful information, which leads to rationalized decision-making.
A wind of innovation is blowing in the artificial intelligence sector. As artificial intelligence develops, its use cases diversify. Many companies are emerging and exploiting this technology in a relevant and innovative way. Artificial intelligence and machine learning are increasingly popular among companies in all industries. However, AI algorithms tend to overwork processors and GPUs.
Colaboratory, or Colab for short, is a Google Research product, which allows developers to write and execute Python code through their browser. Google Colab is an excellent tool for deep learning tasks. It is a hosted Jupyter notebook that requires no setup and has an excellent free version, which gives free access to Google computing resources such as GPUs and TPUs. Since Google Colab is built on top of vanilla Jupyter Notebook, which is built on top of Python kernel, let's look at these technologies before diving into why we should and how we can use Google Colab. There are several tools used in Python interactive programming environments.
"Neural networks represent the beginning of a fundamental shift in how we write software. The current coding paradigms nudge developers to write code using restrictive machine learning libraries that can learn, or explicitly programmed to do a specific job. But, we are witnessing a tectonic shift towards automation even in the coding department. So far, code was used to automate jobs now there is a requirement for code that can write itself adapting to various jobs. This is software 2.0 where software writes on its own and thanks to machine learning; this is now a reality. Differentiable programming especially, believes the AI team at Facebook, is key to building tools that can help build ML tools. To enable this, the team has picked Kotlin language. Kotlin was developed by JetBrains and is popular with the Android developers. Its rise in popularity is a close second to Swift. Kotlin has many similarities with Python syntax, and it was designed as a substitute for Java.
Google Cloud Platform provides us with a wealth of resources to support data science, deep learning, and AI projects. Now all we need to care about is how to design and train models, and the platform manages the rest tasks. In current pandemic environment, the entire process of an AI project from design, coding to deployment, can be done remotely on the Cloud Platform. IMPORTANT: If you get the following notification when you create a VM that contains GPUs. You need to increase your GPU quota.
What is the state of the art in #ArtificialIntelligence? The state of the art in Artificial Intelligence (SOTA AI) follows the reduction rule, SOTA AI #DataScience #MachineLearning #DeepLearning Narrow/Weak AI The SOTA AI, as specific ML/DL models, #algorithms, techniques and technologies, it is what makes today's commercially prevalent weak AI. The SOTA AI is still after building machines and software agents somehow mimicking human-like cognition and #intelligence (sense (perceiving), analysis, reasoning, understanding and response) by means of statistic learning techniques. Most present AI companies, are about some advanced data analytics, predictive modeling, or computational neural networks based on mathematics and algorithms, as some specific ML/DL techniques, algorithms, models or applications. They are outperforming humans in some very narrowly defined task, focusing on imitating, simulating some single cognitive ability, skill or competence.
It is the analysis of the dataset that has a sequence of time stamps. It has become more and more important with the increasing emphasis on machine learning. So many different types of industries use time-series data now for time series forecasting, seasonality analysis, finding trends, and making important business and research decisions. So it is very important as a data scientist or data analyst to understand the time series data clearly. I will start with some general functions and show some more topics using the Facebook Stock price dataset. Time series data can come in with so many different formats. But not all of those formats are friendly to python's pandas' library.
The graph represents a network of 1,033 Twitter users whose tweets in the requested range contained "iiot machinelearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 20 November 2020 at 12:20 UTC. The requested start date was Friday, 20 November 2020 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 17-hour, 40-minute period from Tuesday, 17 November 2020 at 06:56 UTC to Friday, 20 November 2020 at 00:37 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.
To cover the complete interview process, I have divided this post into separate events based on the timeline. This will help you to evaluate the process and preparation time required for each stage of the interview better. If you are looking to interview for a similar position, it is important to evaluate the profiles which get picked for the interview process. Now, I don't know exactly how my profile stood in the pool of candidates, but I am sharing my resume as a sample profile that got picked for such interviews. As you can see, I had completed 4 years of my Ph.D. by this time, publishing majorly in the areas of Machine Learning, Data Visualization, and Computer Vision.