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Guest Blog – Machine Learning In Talent Management - AI Summary

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AI & Machine Learning Applications in the Real World According to the latest trends of AI-based solutions, there is hardly any decisive sector or industry that does not rely on smart algorithms and automation to perform highly advanced tasks that would be impossible for most humans. Many companies use Machine Learning and Artificial Intelligence to identify and sort through the best possible candidates for a position. With a few Machine Learning courses that are specially designed for regular people, without advanced technical knowledge, it's easy to understand why there are so many applications of advanced technologies in the real world. Luckily, this situation can now be avoided by training machine learning algorithms to take over the task. According to a case study performed at Canada's largest bookstore chain (Indigo), the use of AI and machine learning algorithms to screen job candidates and decide who to hire has led to an increase in overall productivity.


ML for Algorithmic Trading, with Stefan Jansen

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Listen to this episode on Anchor FM. Stefan has been a partner in an investment firm where he assisted in building data infrastructure and predictive analytics practice. He accomplished this when data science was only beginning to be taken seriously in the investment industry. You won't want to miss this opportunity to learn from Stefan's experiences. Machines learning from data will continually improve in achieving performance measures.



How deep learning took so much time to take off

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Maybe the worse thing that can happen to an idea is being born on the wrong moment, or/and even wrong place. Take the case of YouTube, was is the first video streaming platform? But it was born on the right moment! "In 1999–2000 it was too hard to watch online content you had to put codecs in your browser and do all this stuff [about company that failed two years before YouTube]" Bill Gross It was somehow similar with deep learning, since the act adding more hidden-layers is not new, and it is even straightfoward: anyone with a outside thinking could try that out, and have succeeded if we had the proper tools. What made deep learning just now? Indeed, it is amazing how fast hardware evolved, in special for personal usage.


GATO: Google's Generalized AI

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Note: The entire model is trained in a purely supervised fashion as opposed to any form of reinforcement learning. The first question you may ask is how the model takes different types of inputs like tabular data, images, sound, audio, video, etc. The answer to this is that everything is first converted to the same format, i.e. After converting data into tokens, they use the following canonical sequence ordering. The goal here is to put everything in the same format with a particular ordering depending upon the task.


Best Examples Of Python Programming Jobs

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A dedicated Python Developer will be expected to understand the language at a higher level and be capable of using Python to accomplish any number of tasks, including but not limited to data collection and analytics, database creation, web development, design scripting, and automation. A Python Developer frequently collaborates with data collection and analytics to provide valuable answers and insight. Python is used in web development, machine learning, artificial intelligence, scientific computing, and academic research. Its growing popularity can be attributed to the data science community's embrace of artificial intelligence and machine learning. Machine-learning applications are being used to innovate organizations in education, healthcare, and finance.


iiot machinelearning_2022-05-20_04-17-50.xlsx

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The graph represents a network of 1,175 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 May 2022 at 11:21 UTC. The requested start date was Friday, 20 May 2022 at 00:01 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, 16-hour, 1-minute period from Tuesday, 17 May 2022 at 07:58 UTC to Friday, 20 May 2022 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


Emergent bartering behaviour in multi-agent reinforcement learning

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Advances in artificial intelligence often stem from the development of new environments that abstract real-world situations into a form where research can be done conveniently. This paper contributes such an environment based on ideas inspired by elementary Microeconomics. Agents learn to produce resources in a spatially complex world, trade them with one another, and consume those that they prefer. We show that the emergent production, consumption, and pricing behaviours respond to environmental conditions in the directions predicted by supply and demand shifts in Microeconomics. We also demonstrate settings where the agents' emergent prices for goods vary over space, reflecting the local abundance of goods.


Data Science Student Success

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Berkeley Coding Academy teaches Python Programming, Data Analytics, and Machine Learning to teenagers. Our Medium publication includes articles related to data science for parents of teens, teens, and a general audience.


K-Medoid Clustering (PAM)Algorithm in Python

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Clustering of large-scale data is key to implementing segmentation-based algorithms. Segmentation can include identifying customer groups to facilitate targeted marketing, identifying prescriber groups to allow health care players to reach out to them with the right messaging, and identifying patterns or abnormal values in the data. K-Means is the most popular clustering algorithm adopted across different problem areas, mostly owing to its computational efficiency and ease of understanding the algorithm. K-Means relies on identifying cluster centers from the data. It alternates between assigning points to these cluster centers using the Euclidean distance metric and recomputes the cluster centers till a convergence criterion is achieved.