How Deep Learning Mimics the Brain in Processing Data

Muneeb Chawla is an Aurora, Colorado-based IT professional with experience in technical areas such as natural language processing and machine learning. Among the areas of the latter domain in which Muneeb Chawla has in-depth knowledge is deep learning. This focuses on algorithms that reflect artificial neural networks, which are based on the function and structure of the human brain.

The advantage of a brain simulation approach is that it improves and simplifies the use of learning algorithms, and also represents a viable pathway toward real artificial intelligence (AI). Another important aspect of such systems is scale, with larger neural networks able to be trained with huge data loads in ways that boost performance. This avoids the “performance plateau” common in other types of machine learning.

Beyond scalability, deep learning models offer automatic feature extraction (feature learning), which uses a “hierarchy of concepts approach.” This enables the computer program to drill down and build on simple concepts when learning complex concepts. With computing power steadily increasing and ever larger datasets available, neural networks have the potential to achieve real-time object and sound recognition, which in turn is used to increase the performance of many everyday tasks.

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Basic Curling Strategies with the Hammer

Based in Colorado, Muneeb Chawla is a senior data scientist who works with technologies such as machine learning in developing networks. A sports enthusiast, Muneeb Chawla enjoys karate to sky diving in his free time. He has also tried ice sport curling.

Curling is a tactical game that requires precision and involves sets of team throws to gain points by knocking out opponents’ stones and leaving your stones within the house (the concentric circles on ice).

The offense focuses on the hammer or situations where your team is on the offensive and attempting to score two or more points. The major advantage of the hammer is that your team is in control of the outcome, as it throws the last stone, and the opponent cannot place the last shot.

The best strategy for scoring at least two points is to begin by throwing a corner guard away from the centerline. This stone is positioned such that another stone can be thrown behind it while still leaving a scoring opportunity open (hitting the stone into the house).

At the same time, maintain an open middle of the sheet, as this offers defensive benefits. For example, the stone can be knocked out if the opponent tries for a center guard (guarding the house). Should the opponent throw into the house, your team can attempt a takeout or lighter hit. Conversely, when the team does not have a hammer, it tries to limit the opponent to a single point.

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How Neural Networks Perform Complex Tasks

Based in Aurora, Colorado, Muneeb Chawla is a tech entrepreneur with a background in areas such as machine learning, natural language processing, and statistics. In his work as a data scientist, Muneeb Chawla has also developed neural networks, which lie at the center of deep learning algorithms.

Also known as artificial neural networks (ANNs), these constructs have a structure that mimics the human brain in the way that neurons rapidly signal to each other within a complex environment.

Unlike the brain, ANNs are made up of node layers that have an input layer and a number of hidden layers, as well as an output layer. Every node connects to another node, with each possessing a specified weight and activation threshold. When a node surpasses the threshold value, it is activated and sends data to the network’s next layer.

Over an extended period, training data is used by neural networks to boost accuracy. Once the networks have been fine-tuned, they can be employed as AI tools that enable high velocity data to be clustered and classified. Practical uses of this include image and speech recognition. The search algorithm of Google is another example of a neural network that rapidly filters mass data and delivers relevant results.

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The Fastest Racket Sport in the World

Muneeb Chawla has served OPTUM in Eden Prairie, Minnesota, as a senior data scientist since 2021. When he is not developing machine learning solutions for the OPTUM information technology department, Muneeb Chawla enjoys leading an active lifestyle. His personal interests include hiking and playing badminton.

According to the Association of Tennis Professionals, the fastest serve in the history of the sport was recorded by American John Isner, who delivered a 157.2-mile-per-hour serve during a 2016 Davis Cup rubber match against Australian Bernard Tomic. While impressive, Isner’s serve pales in comparison to the world’s fastest racket sport – badminton.

Badminton is a racket sport with somewhat similar rules and equipment to tennis. However, while tennis players use a tennis ball, badminton players exchange rallies using a birdie, or shuttlecock. Birdies can be made of various materials, but they typically consist of a rounded cork tip with goose feathers attached.

Badminton rules dictate that serves must be delivered using an underhand motion, meaning serves typically travel at only a few miles per hour. However, overhead smashes can send the birdie traveling at speeds in excess of 200 miles per hour. In fact, Mads Pieler Kolding holds the record for the world’s fastest badminton smash at 264.7 miles per hour.

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