Bragadeesh Sundararajan

Vibe-coding without supervision is a disaster. Here is my vibe-coding step-by-step guide: 1. Write down a high-level plan 2. Break it down into small phases 3. Focus on one phase at a time For each phase: 1. Add as much context as possible to the plan 2. Include the things you want to build 3. Include the things you don't want to build 4. Commit your code 5. Give the plan to the agent to start building For each iteration of the agent: 1. Review everything the agent built 2. Provide clear feedback for refinements 3. Update the plan with your feedback 4. Discard code and restart anytime the agent messes up As I build critical functionality: 1. Write unit tests for any critical piece of code 2. Ensure these tests run after each agent iteration I'd love to improve this process. What are you doing that you find helpful?

For all those criticizing @theliverdoc , fr his contributions to fighting pseudoscience/ recent skirmish with the great Chess Player Vidit, let me tell u that his life is not easy. Besides his busy medical practice, the great Dr fights pseudoscience, not fr his benefit , but to save precious Indian Lives. That is a noble cause. There is no Dr more abused in Indian Twitter than @theliverdoc . Yet, he continues to do his good work. Sitting in Tamilnadu, I hv heard testimonials of several ppl saved from alcoholism/ potential painful death, just by following him. He has contributed to improving the general health of Indians by his rational, intelligent tweets replete with immense, life-saving wisdom. Bringing his family into ur quarrel with him, tarnishing their image is revolting. Nobody else among the current generation of Indian Drs has done more to improve the health of 1.4 billion Indians, as has Dr.Cyriac Abby Philips, the great hepatologist frm Kerala. He hs a certain style of putting things across, which is his wish. Whenever/wherever Pseudoscience had the potential to destroy human life/good health, this Dr hs appeared on the scene, to explain in simple terms, what is medically good or bad fr the avg Indian. Fr that alone, he shd be awarded the B.C.Roy award fr the best Indian Dr. & don't bring in religion. Not in a single tweet, hs he remotely been critical of ANY religion. Raise a toast to Dr. Cyriac Abby Philips, lovingly called The Liver Doctor. A one-man-army that has saved thousands of lives.

This hit me hard.

Google Maps was struggling. It was slow, clunky, and frustrating to use. Then, one engineer stepped in and rewrote the entire base code in a single weekend. Here’s how Bret Taylor pulled it off.

Outrageously cool paper. A 65% reduction in the incidence (new cases) of liver cancer (hepatocellular carcinoma) was observed in patients diagnosed with cirrhosis consuming ≥ 240 g/day of vegetables. Study: sciencedirect.com/science/articl… Such a simple intervention, which our grandmothers and mothers always used to say. Eat your vegetables! Image courtesy: tinyurl.com/4mfu9xpu

Impact of AI Multimodality in Retail Healthcare: Diagnostics, Personalised Treatment and Experience healthmanagement.org/c/healthmanage…

For categorical (discrete) supervised learning, the most fitting loss function would be cross-entropy loss, which measures the difference between two probability distributions for a given random variable or set of events. However, the term "Kullback-Leibler (KL) loss" mentioned here is another name for KL divergence, which is a measure of how one probability distribution diverges from a second, expected probability distribution. Cross-entropy can be seen as KL divergence with an additional constant. So if the context is about measuring the divergence, then KL loss would be appropriate. Between the options provided: - Kullback-Leibler (KL) loss is used to measure the divergence between two probability distributions, which is relevant in cases like variational autoencoders or when the true distribution is known. - Binary Crossentropy is a special case of cross-entropy loss used in binary classification tasks. - Mean Squared Error (MSE) and Any L2 loss are typically used for regression problems. So for a general categorical supervised learning task, especially if it involves more than two classes, a form of cross-entropy loss (not specifically binary) would be most appropriate, which is not explicitly listed here. If we are to choose from the given options and the task is a binary classification, then Binary Crossentropy would be the best fit. Otherwise, for tasks like distribution matching, KL divergence would be used.

Machine Learning Practice Question 🙋‍♂️ Loss functions are important for grading the performance of your models.

@DanKornas C. power In statistics, "power" refers to the ability of a hypothesis test to correctly reject a false null hypothesis. It is the probability that the test will detect an effect when there is an actual effect present in the population.

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C. Random Forest is an ensemble learning method that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random Forests fall into the category of bagging techniques, where multiple learners (trees) are trained with different subsets of the training data.

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@AnkitSingh_0805 @DanKornas You have a point. I agree.

@Bragy @DanKornas Sorry sir,but I am not satisfied with your ans,when we put the value of k to 1 ,it essentially memorizes the training examples leads to high variance and has low bias because it is capable of fitting complex patterns in training data

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📈 Data Deep Dive: "Exploring Variability: Understanding Measures of Dispersion, Range, and Interquartile Range" 🌟 Key Concepts: Range & IQR: Quick insights into data spread; IQR minimizes outlier effects. Variance & Standard Deviation: Detailed analysis of spread around the mean. 🔍 Why It's Important: The right dispersion measure can illuminate your data's true story. Essential for robust data analysis in research, business, or policy-making. 💡 Remember: Pair dispersion measures with central tendency for a complete data picture. @bragadeeshs/exploring-variability-understanding-measures-of-dispersion-range-and-interquartile-range-4ef82aa409ba" target="_blank" rel="nofollow noopener">medium.com/@bragadeeshs/e… #Statistics #DataAnalysis #Dispersion #Range #IQR #Variance #StandardDeviation

📊 Delving Into Data: "The Core of Data: Central Tendency and the Three Ms — Mean, Median, Mode" 🔑 Understanding the 3Ms: Mean: Best for normal distributions, but watch for outliers! Median: Ideal for skewed data, revealing the true middle. Mode: Crucial for categorical data, showing what's most common. 🧐 Why It Matters: Choosing the right measure can lead to more accurate data interpretations. Essential for informed decision-making across various industries. 💡 Pro Tip: Tailor your approach based on data nature and analytical goals. @bragadeeshs/the-core-of-data-central-tendency-and-the-three-ms-mean-median-mode-5532867dccd8" target="_blank" rel="nofollow noopener">medium.com/@bragadeeshs/t… #DataAnalysis #Statistics #CentralTendency #MeanMedianMode #DataScience

🚀 Exploring the Evolution of ML: "The Fine Art of Hyperparameter Tuning: Elevating Machine Learning to Excellence" 🧠 Key Shifts: Hyperparameter tuning is evolving with advanced, adaptive algorithms. AutoML is transforming AI, making it more accessible & efficient. 🔮 Future Outlook: AI development becomes faster, more intuitive, and inclusive. Ethical AI and model interpretability become focal points. 💡 Takeaway: We're entering an era where AI is not just advanced, but also more accessible and responsible. @bragadeeshs/the-fine-art-of-hyperparameter-tuning-elevating-machine-learning-to-excellence-063773eab50a" target="_blank" rel="nofollow noopener">medium.com/@bragadeeshs/t… #MachineLearning #AutoML #AI #HyperparameterTuning #TechInnovation

🤖💡 "Optimizing AI’s Choices: The Power of Information Gain in Machine Learning" 🔍 Key Insights: Information Gain: A crucial metric for reducing uncertainty in AI models. Challenges: Biases towards certain features, handling continuous data, sensitivity to noise. Balanced Approach: Combine with other metrics for enhanced performance. 🌐 Future Outlook: Ongoing R&D is refining Information Gain for complex datasets. Integrating with advanced ML algorithms for more nuanced AI models. ✨ The Takeaway: A vital tool in AI, guiding informed decision-making. Recognize its potential, yet be mindful of its limitations. @bragadeeshs/optimizing-ais-choices-the-power-of-information-gain-in-machine-learning-c815cfdd2df0" target="_blank" rel="nofollow noopener">medium.com/@bragadeeshs/o… #AI #MachineLearning #DataScience #InformationGain #ArtificialIntelligence

🔍 Dive into "Unraveling the Mystique of Entropy in Machine Learning: A Journey from Theory to Practice" 🚀 🧠 Why Entropy Matters: It's the compass for understanding data complexity. A key to enhancing model accuracy & decision-making. Vital in quantifying uncertainty in datasets. 🌐 Entropy's Role in ML: From decision trees to deep learning & beyond. Not just for today's data challenges, but a pillar for future innovations. 💡 Insight: Mastering entropy = Mastering the language of information. Essential for any data science enthusiast. @bragadeeshs/unraveling-the-mystique-of-entropy-in-machine-learning-a-journey-from-theory-to-practice-4b21743f4222" target="_blank" rel="nofollow noopener">medium.com/@bragadeeshs/u… #MachineLearning #DataScience #Entropy #AI #Innovation

📚 New Resource Alert: "Mastering Regular Expressions in Python: A Practical Guide to Text Processing" 🐍🔍 🌟 Key Insights: Regex: An essential tool for text manipulation & data validation. From basic patterns to advanced features, uncover the power of regex in Python. Learn best practices for efficient & readable expressions. 🧩 Why Regex Matters: It's a game-changer for parsing large datasets, user input validation, and automating text tasks. Complex, yes, but its versatility in text processing is unmatched. 💡 Pro Tip: Mastering regex is a journey of practice and refinement. Embrace its intricacies and unlock new possibilities in data handling! @bragadeeshs/mastering-regular-expressions-in-python-a-practical-guide-to-text-processing-7ce0e653d7de" target="_blank" rel="nofollow noopener">medium.com/@bragadeeshs/m… #PythonProgramming #Regex #TextProcessing #DataValidation #CodingExcellence

🚀 Just out: "Mastering Python & MySQL Integration: A Comprehensive Guide for Developers" 🐍💾 🔑 What You'll Learn: 🌉 Establishing secure Python-MySQL connections. ✍️ Mastering CRUD operations for data management. 🛠️ Advanced DB techniques: Indexing, complex queries & more. 🚅 Script optimization for faster, scalable apps. 🛠️ Practical Applications: Build robust apps for web, analytics, fintech... Innovate with Python & MySQL in any data-driven domain. 🌟 Why It Matters: Database integration skills are essential in the evolving tech landscape. Keep learning to stay ahead in software development! @bragadeeshs/mastering-python-mysql-integration-a-comprehensive-guide-for-developers-6037d435e053" target="_blank" rel="nofollow noopener">medium.com/@bragadeeshs/m… #Python #MySQL #SoftwareDevelopment #DataManagement #TechInnovation

🚀 Just published: "Harnessing the Power of NoSQL in the Big Data Era: A Symbiotic Evolution" 🌐 ✨ Key Insights: 🌟 NoSQL's future? Continuous innovation with AI and real-time analytics. 🔐 Tackling data security, privacy & scalability head-on. 📈 Expect wider adoption across sectors, thanks to its flexibility & scalability. 🎛️ Balancing advanced features with ease of use - crucial for NoSQL's growth. 🔮 Looking Ahead: The NoSQL & Big Data synergy is a landscape of endless possibilities! Staying agile & adaptable is key in this dynamic tech space. @bragadeeshs/harnessing-the-power-of-nosql-in-the-big-data-era-a-symbiotic-evolution-54f7427a294b" target="_blank" rel="nofollow noopener">medium.com/@bragadeeshs/h… #NoSQL #BigData #DataAnalytics #CloudComputing #TechInnovation

🚀 #TechTrend: "Scaling Heights with NoSQL: Mastering Growth and Overcoming Challenges" 📈 Key Takeaways: - Strategic scaling with NoSQL: Cassandra's distribution, Redis's speed, Neo4j's relationships, MongoDB's flexibility. - A balancing act: Juggle performance, resource management, and cost. - Learn from Twitter, Netflix, LinkedIn on aligning database choices with business needs. 🧗 Navigating Challenges: - Avoid common pitfalls: Data modeling errors, operational complexity. 🔮 The Future: - As data grows, mastering NoSQL scaling is essential. - Success lies in understanding NoSQL, strategic scaling, and adaptability. @bragadeeshs/harnessing-the-power-of-nosql-in-the-big-data-era-a-symbiotic-evolution-54f7427a294b" target="_blank" rel="nofollow noopener">medium.com/@bragadeeshs/h… #NoSQL #DataScaling #BigData #Cassandra #Redis #Neo4j #MongoDB