Deeplearning Stories

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Machine learning consists of three parts: supervised learning, unsupervised learning, and reinforcement learning. The three learning algorithms focus on teaching a machine to learn from its mistakes, to improve, and to improve. Is knowledge of data structures and algorithms absolutely necessary? Not exactly! No one expects you to know how to use linked lists, tree search, or recursion to build predictive models. However, you need to understand arrays and vectors. Predictive modeling in R and Python is mostly based on packages and libraries. These packages and libraries are hardcoded so that a user does not need to do any serious coding other than calling these libraries and performing calculations on them. Read More : https://www.yourquorum.com/question/do-machine-learning-engineers-need-to-know-data-structures-and-algorithms?utm_source=medium_sh&utm_medium=medium_sh&utm_campaign=medium_sh

The rising availability of data is propelling the surge of generative AI in the fintech industry. Explore how data-driven insights are revolutionising the Fintech industry. www.spiralmantra.com

I am presenting the way to write your own functions for algorithms. In this series, I am starting with Gradient descent algorithm. I briefly explain, what is gradient descent. After that, I apply gradient descent algorithm for a linear regression to identify parameters. For illustration, I simulate data for simple linear regression. To read the full blog visit: https://www.rangtech.com/blog/ai-machine-learning/r-and-python-gradient-descent

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The future of this world is unclear At odds with what that AI in here May bring upon us too soon Lurking often quite unseen. With powers greater than nukes, It brings risks more than just mooks; AI can spell the end for our race, Sure death for strong and meek alike, no trace.