A Technology Scientist, Pioneer & Entrepreneur. An Executive Leader with worldwide record of success. A Visionary Innovator in STEM (Science, Technology, Engineering, and Mathematics). A Startup & Investments aficionado.
A new field promises to usher in a new era of using machine learning and computer vision to... | Click below to read the full article at The Digital Insider.
A new field promises to usher in a new era of using machine learning and computer vision to... | Click below to read the full article at The Digital Insider.
The role of machine learning and computer vision in Imageomics
🔬✨ Incorporating symmetry in machine learning processes offers a revolutionary approach to reducing complexity and improving accuracy. In a groundbreaking study, an MIT PhD student and her advisor explore the concept of using symmetry to enhance machine learning. Learn more about their findings and the transformative potential of symmetry in machine learning at Wolf Consultings: [https://lnkd.in/ghKmnAiz) #MachineLearning #SymmetryRevolution Read the full article here: [https://lnkd.in/gzjRfBWw) Original Source: [MIT News - How Symmetry Can Aid Machine Learning](https://lnkd.in/gzPCaZ7C) #GeometricDeepLearning #InnovativeResearch 📚🔍
I've just published my note and summary on gradient descent! It's a fundamental optimization algorithm used in machine learning. Check it out and hope you have fun with my illustration! #gradientdescent #machinelearning
Unlocking the Power of Hopfield Networks: A Journey in Machine Learning Did you know that Hopfield Networks, a type of recurrent artificial neural network, were a game-changer in the early 1980s? These networks are like the city roads that always lead to Rome—eventually, all inputs converge to a stable output. One of the most fascinating aspects of Hopfield Networks is their associative memory. Imagine being able to retrieve a complete image from mere fragments, much like solving a jigsaw puzzle with missing pieces. This capability has immense applications in fields like image recognition and data compression, letting us organize and identify information with astonishing accuracy. In my own experience, introducing Hopfield Networks to my team dramatically enhanced our data processing capabilities. One memorable project involved recognizing distorted patterns in medical images. The network's ability to fill in gaps made a critical difference in diagnosing conditions accurately. However, the journey is not without its bumps. Hopfield Networks can only store a limited number of patterns, about 20% of their neurons. This means they sometimes struggle with similar patterns, leading to possible errors. It's a humbling reminder of the imperfections in machine learning and the ongoing need for innovation. Are you leveraging the power of neural networks in your organization? What challenges have you faced, and how did you overcome them? Share your thoughts and experiences below! #MachineLearning #NeuralNetworks #AI #DataScience #Innovation
Futurist Keynote Speaker on Tech, Story, and Ethics. I distill complex trends into compelling stories that leverage change and reinvent uncertainty into opportunity.
In retrospect, this interview with Geordie Rose I recorded in 2013, has turned out to be prophetic: Geordie Rose: Machine Learning is Progressing Faster Than You Think https://lnkd.in/dkTVTwB
The machine learning landscape is a swiftly evolving field at the intersection of computer science, statistics, and domain-specific knowledge. It encompasses various algorithms and techniques for autonomous data learning. Key components include diverse algorithms, data quality, rigorous evaluation, feature engineering, deep learning, deployment considerations, and ethical concerns. Collaboration and innovation fuel its progress, with far-reaching implications for technology and society. #machinelearning #machinelearniglandscape My Blog: https://lnkd.in/grSmwK3Y
I recently spoke to a rocket scientist. He told me about a difficult image processing problem. I won't go into the details, but the basic idea involved identifying objects. His solutions were well-thought. Using image processing algorithms, they identify objects to take mission-critical decisions. I asked if he had considered using machine learning. He laughed: "Where would you get all that data from?" "I don't know." "Right...even if we could, we wouldn't. Exact algorithms are more reliable, and fast, as compared to 'trained' algorithms". The conversation convinced me that, to solve complex problems, I should start with exact methods. Too often, we throw ML algorithms at a problem. In the hope that computers will detect patterns that we couldn't. Let's change that. Onward with exact algorithms! (Followed by ML 😛) #machinelearning #algorithms
Building AI products @ E.ON Germany | Azure AI Engineer, MLOPS, LLM, Distributed Computing
Gaurav Sen Thank you for your post and the youtube architecture videos which have inspired me for many years. However, I'm trying to understand this problem, how many edge cases have rocket scientist covered in the object identification algorithm? Based on my experience. the problem with the custom algorithm is False Negatives. They are built to be too strict and do not work for exceptional cases or outliers. (I might be wrong in this case, but I would love to learn otherwise.)
I recently spoke to a rocket scientist. He told me about a difficult image processing problem. I won't go into the details, but the basic idea involved identifying objects. His solutions were well-thought. Using image processing algorithms, they identify objects to take mission-critical decisions. I asked if he had considered using machine learning. He laughed: "Where would you get all that data from?" "I don't know." "Right...even if we could, we wouldn't. Exact algorithms are more reliable, and fast, as compared to 'trained' algorithms". The conversation convinced me that, to solve complex problems, I should start with exact methods. Too often, we throw ML algorithms at a problem. In the hope that computers will detect patterns that we couldn't. Let's change that. Onward with exact algorithms! (Followed by ML 😛) #machinelearning #algorithms
Sophomore CSE Student | Driven by Machine Learning & AI, Pioneering the Future of Innovation
I'm thrilled to share that I've completed the basics of machine learning and delved into supervised, unsupervised, and hybrid learning algorithms, along with an introduction to linear regression with the experienced professors of Great Learning. Excited to dive deeper into this fascinating field! #machinelearning #greatlearning
