Rajandran R Telecom Engineer turned Full-time Derivative Trader. Mostly Trading Nifty, Banknifty, USDINR and High Liquid Stock Derivatives. Trading the Markets Since 2006 onwards. Using Market Profile and Orderflow for more than a decade. Designed and published 100+ open source trading systems on various trading tools. Strongly believe that market understanding and robust trading frameworks are the key to the trading success. Writing about Markets, Trading System Design, Market Sentiment, Trading Softwares & Trading Nuances since 2007 onwards. Author of Marketcalls.in)

[Infographic] Evolution of Machine Learning

2 min read

Imagine a world where machines learn like humans, constantly evolving and improving. This isn’t a scene from a sci-fi movie—it’s the reality of machine learning. This technology has come a long way since its inception, and today, we’re taking you on a fascinating journey through the milestones of machine learning, from 1805 to the present.

The Humble Beginnings: Linear Regression (1805-1809) It all started with Linear Regression, developed independently by Adrien-Marie Legendre and Carl Friedrich Gauss. This technique, based on the method of least squares, was a stepping stone in predictive modeling, allowing us to forecast future trends from past data. It laid the groundwork for what was to become a revolution in data analysis.

The Neural Network Precursor: Perceptron (1957) Fast forward to 1957, and we witness the birth of the Perceptron by Frank Rosenblatt. This simple yet powerful model, simulating a neuron for binary classification tasks, was labeled as the precursor to neural networks. It marked the beginning of machines mimicking human brain functions.

The Art of Decision Making: Reinforcement Learning (1959) Richard Bellman’s invention of Reinforcement Learning in 1959 introduced a new era of decision-making algorithms. By teaching agents to make decisions based on rewards and penalties, this method laid the foundation for developing autonomous systems and robotics.

Redefining Classification: Support Vector Machines (1964) The introduction of Support Vector Machines by Vladimir Vapnik and Alexey Chervonenkis in 1964 was a game-changer in classification tasks. Excelling in handwriting recognition and face detection, these machines demonstrated the potential of ML in practical applications.

Training the Neural Networks: Backpropagation (1986) The popularization of backpropagation for neural networks in 1986, thanks to David Rumelhart, Geoffrey Hinton, and Ronald Williams, marked a significant advancement in training complex neural networks. This method optimized the learning process, making it possible to develop more sophisticated AI models.

Combining Weakness for Strength: Boosting Algorithms (1995) In 1995, Yoav Freund and Robert Schapire introduced AdaBoost, an algorithm that improved prediction accuracy by combining multiple weak learning models. This concept showed that strength could indeed be found in numbers, or in this case, algorithms.

The Ensemble Approach: Random Forests (1995) Tin Kam Ho’s introduction of Random Forests in 1995 brought a robust approach to classification and regression. By creating ensembles of decision tree-like models, these forests demonstrated improved accuracy and stability in predictions.

Sequencing Success: RNN and LSTM (1997) The development of RNN (Recurrent Neural Networks) and LSTM (Long Short-Term Memory) networks, particularly by Sepp Hochreiter and Jürgen Schmidhuber for LSTM, revolutionized sequential data processing. This was a milestone in natural language processing and speech recognition, enabling machines to understand and generate human-like language.

Visionary Machines: Deep Convolutional Neural Networks (2012) In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton introduced Deep Convolutional Neural Networks. These networks revolutionized image recognition, enabling machines to identify and classify images with incredible accuracy, mimicking the human visual system.

The Creative AI: Generative Adversarial Networks (2014) Ian Goodfellow’s invention of Generative Adversarial Networks in 2014 opened up new horizons in AI creativity. These networks became groundbreaking in generating realistic images and videos, blurring the line between AI-generated and real-life content.

Transforming Language Processing: Transformer Networks (2017) The introduction of Transformer Networks by Ashish Vaswani and his team in 2017 marked a new era in natural language processing. These networks, efficient in context-aware processing, became the cornerstone for modern NLP tasks, leading to advanced models like BERT and GPT series.

From linear regression to transformer networks, the evolution of machine learning has been nothing short of extraordinary. Each breakthrough has built upon the last, pushing the boundaries of what’s possible with artificial intelligence. As we look to the future, one thing is certain: machine learning will continue to evolve, transforming our world in ways we can only begin to imagine. Stay tuned for the next chapter in this incredible journey.

Stay connected for more insights into the fascinating world of machine learning and AI advancements!

Rajandran R Telecom Engineer turned Full-time Derivative Trader. Mostly Trading Nifty, Banknifty, USDINR and High Liquid Stock Derivatives. Trading the Markets Since 2006 onwards. Using Market Profile and Orderflow for more than a decade. Designed and published 100+ open source trading systems on various trading tools. Strongly believe that market understanding and robust trading frameworks are the key to the trading success. Writing about Markets, Trading System Design, Market Sentiment, Trading Softwares & Trading Nuances since 2007 onwards. Author of Marketcalls.in)

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