corpus.blog
41554 blogs · [ { "id": "01a087c5-0835-73ba-b9ec-f08b0709de0c", "title": "Known Known, Known Unknown and Unknown Unknown for LLM Users", "url": "https://pkghosh.wordpress.com/2025/07/19/known-known-known-unknown-and-unknown-unknown-for-llm-users/", "published_at": "2025-07-20T01:11:56+00:00" }, { "id": "01a087c5-0835-73ba-b9ec-f08b079b9c26", "title": "Unreasonable Claim of Reasoning Ability of LLM : Part 2", "url": "https://pkghosh.wordpress.com/2025/06/28/unreasonable-claim-of-reasoning-ability-of-llm-part-2/", "published_at": "2025-06-28T17:49:59+00:00" }, { "id": "01a087c5-0835-73ba-b9ec-f08b07b559eb", "title": "Does LLM Have Emergent Behavior?", "url": "https://pkghosh.wordpress.com/2025/05/31/does-llm-have-emergent-behavior/", "published_at": "2025-05-31T17:47:18+00:00" }, { "id": "01a087c5-0835-73ba-b9ec-f08b07d22a58", "title": "Neuro Symbolic Planning Solutions with LLM", "url": "https://pkghosh.wordpress.com/2025/03/28/neuro-symbolic-planning-solutions-with-llm/", "published_at": "2025-03-29T05:11:15+00:00" }, { "id": "01a087c5-0835-73ba-b9ec-f08b0841d8dd", "title": "Inference Time Scaling Demystified : Part 2", "url": "https://pkghosh.wordpress.com/2025/02/17/inference-time-scaling-demystified-part-2/", "published_at": "2025-02-18T04:05:31+00:00" }, { "id": "01a087c5-0835-73ba-b9ec-f08b09327286", "title": "LLM Based AI Agents : Myth and Reality", "url": "https://pkghosh.wordpress.com/2025/01/23/llm-based-ai-agents-myth-and-reality/", "published_at": "2025-01-24T06:22:12+00:00" }, { "id": "01a087c5-0835-73ba-b9ec-f08b0984bf5b", "title": "Inference Time Scaling Demystified : Part 1", "url": "https://pkghosh.wordpress.com/2024/12/31/inference-time-scaling-demystified/", "published_at": "2024-12-31T18:07:32+00:00" }, { "id": "01a087c5-0836-71bc-b16a-b4c3cf6595a6", "title": "Intuitive Explanation of LLM Hallucination with Semantic Entropy", "url": "https://pkghosh.wordpress.com/2024/11/29/intuitive-explanation-of-llm-hallucination-with-semantic-entropy/", "published_at": "2024-11-30T05:34:38+00:00" }, { "id": "01a087c5-0836-71bc-b16a-b4c3cff3aa58", "title": "Historical Review of General Problem Solver in AI Including LLM", "url": "https://pkghosh.wordpress.com/2024/10/10/historical-review-of-general-problem-solver-in-ai-including-llm/", "published_at": "2024-10-11T04:55:54+00:00" }, { "id": "01a087c5-0836-71bc-b16a-b4c3d0031c1d", "title": "Demystifying OpenAI Q* Reasoning Engine", "url": "https://pkghosh.wordpress.com/2024/09/16/demystifying-openai-q-reasoning-engine/", "published_at": "2024-09-16T17:12:44+00:00" }, { "id": "01a0bdcf-340e-713b-98ec-d471337c18e7", "title": "Robot Navigation with Empowerment Based Implicit Reward", "url": "https://pkghosh.wordpress.com/2024/08/21/robot-navigation-with-empowerment-based-implicit-reward/", "published_at": "2024-08-21T17:24:33+00:00" }, { "id": "01a0bdcf-340e-713b-98ec-d4713473e95b", "title": "LLM Failure on Out of Distribution Reasoning and Problem Solving Tasks", "url": "https://pkghosh.wordpress.com/2024/07/29/llm-failure-on-out-of-distribution-reasoning-and-problem-solving-tasks/", "published_at": "2024-07-29T16:16:39+00:00" }, { "id": "01a0bdcf-340e-713b-98ec-d47135556113", "title": "Pattern, Knowledge, Intelligence and LLM", "url": "https://pkghosh.wordpress.com/2024/06/29/pattern-knowledge-intelligence-and-llm/", "published_at": "2024-06-29T18:24:57+00:00" }, { "id": "01a0bdcf-340e-713b-98ec-d471355e7fdf", "title": "Demonstrating LLM Model Collapse with a Simple Analogical Example.", "url": "https://pkghosh.wordpress.com/2024/05/28/demonstrating-llm-model-collapse-with-a-simple-analogical-example/", "published_at": "2024-05-28T17:22:18+00:00" }, { "id": "01a0bdcf-340e-713b-98ec-d4713585d70e", "title": "Route Planning with DynaQ Reinforcement Learning", "url": "https://pkghosh.wordpress.com/2024/04/23/route-planning-with-dynaq-reinforcement-learning/", "published_at": "2024-04-24T05:10:18+00:00" }, { "id": "01a0bdcf-340e-713b-98ec-d47135e2dd69", "title": "Time Series Feature Engineering with Histogram", "url": "https://pkghosh.wordpress.com/2024/03/30/time-series-feature-engineering-with-histogram/", "published_at": "2024-03-31T04:48:15+00:00" }, { "id": "01a0bdcf-340e-713b-98ec-d47135e46b94", "title": "AI Chatbots with Human in the Loop", "url": "https://pkghosh.wordpress.com/2024/02/23/ai-chatbots-with-human-in-the-loop/", "published_at": "2024-02-23T16:07:47+00:00" }, { "id": "01a0bdcf-340e-713b-98ec-d47136c9178a", "title": "Time Series Classification with Neural Network using Random Sub Sequence Statistics as Features", "url": "https://pkghosh.wordpress.com/2024/01/26/time-series-classification-with-neural-network-using-random-sub-sequence-statistics-as-features/", "published_at": "2024-01-27T04:38:31+00:00" }, { "id": "01a0bdcf-340e-713b-98ec-d47136d70914", "title": "Time Series Forecasting with Decomposition and Two Linear Networks", "url": "https://pkghosh.wordpress.com/2023/12/28/time-series-forecasting-with-decomposition-and-two-linear-networks/", "published_at": "2023-12-28T16:52:23+00:00" }, { "id": "01a0bdcf-340e-713b-98ec-d471378ea12f", "title": "Unreasonable Claim of Reasoning Ability of LLM: Part 1", "url": "https://pkghosh.wordpress.com/2023/11/24/unreasonable-claim-of-reasoning-ability-of-llm/", "published_at": "2023-11-24T17:28:34+00:00" }, { "id": "01a0bdd3-63ff-7118-94c5-8282e5ee5d5a", "title": "Customization of LLM ChatBots with Retrieval Augmented Generation", "url": "https://pkghosh.wordpress.com/2023/10/26/customization-of-llm-chatbots-with-retrieval-augmented-generation/", "published_at": "2023-10-26T20:30:54+00:00" }, { "id": "01a0bdd3-63ff-7118-94c5-8282e65a070a", "title": "Time Series Data Exploration with Wavelet Transform", "url": "https://pkghosh.wordpress.com/2023/09/29/time-series-data-exploration-with-wavelet-transform/", "published_at": "2023-09-29T17:55:29+00:00" }, { "id": "01a0bdd3-63ff-7118-94c5-8282e6d72186", "title": "Store Inventory Replenishment with Q Value Reinforcement Learning", "url": "https://pkghosh.wordpress.com/2023/08/25/store-inventory-replenishment-with-q-value-reinforcement-learning/", "published_at": "2023-08-25T15:59:26+00:00" }, { "id": "01a0bdd3-63ff-7118-94c5-8282e716f94d", "title": "Online Advertisement Placement with Contextual Bandit.", "url": "https://pkghosh.wordpress.com/2023/07/27/online-advertisement-placement-with-contextual-bandit/", "published_at": "2023-07-27T16:21:04+00:00" }, { "id": "01a0bdd3-63ff-7118-94c5-8282e7affacf", "title": "Time Series Sequence Anomaly Detection with Markov Chain", "url": "https://pkghosh.wordpress.com/2023/06/28/time-series-sequence-anomaly-detection-with-markov-chain/", "published_at": "2023-06-28T16:43:28+00:00" }, { "id": "01a0bdd3-63ff-7118-94c5-8282e7b8e98e", "title": "Realtime Concept Drift Detection for Machine Learning Classification Models", "url": "https://pkghosh.wordpress.com/2023/05/29/realtime-concept-drift-detection-for-machine-learning-classification-models/", "published_at": "2023-05-29T17:37:00+00:00" }, { "id": "01a0bdd3-63ff-7118-94c5-8282e7ba62de", "title": "Delivery Vehicle Route Planning with Ant Colony Optimization", "url": "https://pkghosh.wordpress.com/2023/04/28/delivery-vehicle-route-planning-with-ant-colony-optimization/", "published_at": "2023-04-28T17:18:30+00:00" }, { "id": "01a0bdd3-63ff-7118-94c5-8282e8047de6", "title": "Synthetic Time Series Data Generation", "url": "https://pkghosh.wordpress.com/2023/03/29/synthetic-time-series-data-generation/", "published_at": "2023-03-29T17:20:44+00:00" }, { "id": "01a0bdd3-63ff-7118-94c5-8282e8bfacbf", "title": "Simulating A/B Test with Counterfactual and Machine Learning Regression Model", "url": "https://pkghosh.wordpress.com/2023/02/28/simulating-a-b-test-with-counterfactual-and-machine-learning-regression-model/", "published_at": "2023-02-28T14:02:27+00:00" }, { "id": "01a0bdd3-63ff-7118-94c5-8282e92081be", "title": "Synthetic Regression Data Generation in Python", "url": "https://pkghosh.wordpress.com/2023/01/22/synthetic-regression-data-generation-in-python/", "published_at": "2023-01-23T04:57:09+00:00" }, { "id": "01a0bdd6-d4f3-7317-a031-00a8593977ce", "title": "AI Past, Present and Future", "url": "https://pkghosh.wordpress.com/2022/12/28/ai-past-present-and-future/", "published_at": "2022-12-28T16:46:28+00:00" }, { "id": "01a0bdd6-d4f3-7317-a031-00a8594ef309", "title": "Information Gain based Feature Selection in Python for Machine Learning Models", "url": "https://pkghosh.wordpress.com/2022/11/30/information-gain-based-feature-selection-in-python-for-machine-learning-models/", "published_at": "2022-11-30T13:06:56+00:00" }, { "id": "01a0bdd6-d4f3-7317-a031-00a85a290d5e", "title": "Machinery Fault Detection with Vibration Data Anomaly Detection using Variational Auto Encoder.", "url": 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"published_at": "2021-10-16T08:20:59+00:00" }, { "id": "01a0bddb-128a-72d3-81e1-96b65ca4c995", "title": "Deep Learning based Anomaly Detection for Data with Temporal and Spatial Correlation", "url": "https://pkghosh.wordpress.com/2021/09/27/deep-learning-based-anomaly-detection-for-data-with-temporal-and-spatial-correlation/", "published_at": "2021-09-28T04:12:14+00:00" }, { "id": "01a0bddb-128a-72d3-81e1-96b65d1cac89", "title": "Find Out How Well Your Machine Learning Model is Calibrated", "url": "https://pkghosh.wordpress.com/2021/08/25/find-out-how-well-your-machine-learning-model-is-calibrated/", "published_at": "2021-08-25T15:45:50+00:00" }, { "id": "01a0bddb-128a-72d3-81e1-96b65da33550", "title": "Duplicate Data Detection with Neural Network and Contrastive Learning", "url": "https://pkghosh.wordpress.com/2021/07/21/duplicate-data-detection-with-neural-network-and-contrastive-learning/", "published_at": "2021-07-22T05:24:48+00:00" }, { "id": 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}, { "id": "01a0bddd-efbc-71fb-8dd3-72e776120472", "title": "Robustness Measurement of Machine Learning Models with Examples in Python", "url": "https://pkghosh.wordpress.com/2021/03/25/robustness-measurement-of-machine-learning-models-with-examples-in-python/", "published_at": "2021-03-25T17:47:39+00:00" }, { "id": "01a0bddd-efbc-71fb-8dd3-72e776caccc8", "title": "Detecting and Measuring Human Bias in Machine Learning Models", "url": "https://pkghosh.wordpress.com/2021/02/26/detecting-and-measuring-human-bias-in-machine-learning-models/", "published_at": "2021-02-26T16:51:47+00:00" }, { "id": "01a0bddd-efbc-71fb-8dd3-72e776f7423f", "title": "Customer Service Quality Monitoring with AutoEncoder based Anomalous Case Detection", "url": "https://pkghosh.wordpress.com/2021/01/20/customer-service-quality-monitoring-with-autoencoder-based-anomalous-case-detection/", "published_at": "2021-01-20T16:10:37+00:00" }, { "id": "01a0bddd-efbc-71fb-8dd3-72e7775d4deb", "title": "Concept Drift Detection Techniques with Python Implementation for Supervised Machine Learning Models", "url": "https://pkghosh.wordpress.com/2020/12/24/concept-drift-detection-techniques-with-python-implementation-for-supervised-machine-learning-models/", "published_at": "2020-12-24T18:44:38+00:00" }, { "id": "01a0bddd-efbc-71fb-8dd3-72e777ac2bbf", "title": "Meeting Schedule Optimization with Genetic Algorithm in Python", "url": "https://pkghosh.wordpress.com/2020/11/26/meeting-schedule-optimization-with-genetic-algorithm-in-python/", "published_at": "2020-11-26T13:51:55+00:00" }, { "id": "01a0bddd-efbc-71fb-8dd3-72e778948348", "title": "Causal Inference with Deep Learning using Manufacturing Supply Chain Optimization as an Example", "url": "https://pkghosh.wordpress.com/2020/10/28/causal-inference-with-deep-learning-using-manufacturing-supply-chain-optimization-as-an-example/", "published_at": "2020-10-28T14:21:40+00:00" }, { "id": "01a0bddd-efbc-71fb-8dd3-72e7791f823f", "title": "Time Series Change Point Detection with Two Sample Statistic on Spark with Application for Retail Sales Data", "url": "https://pkghosh.wordpress.com/2020/09/27/time-series-change-point-detection-with-two-sample-statistic-on-spark-with-application-for-retail-sales-data/", "published_at": "2020-09-28T04:44:59+00:00" }, { "id": "01a0bddd-efbc-71fb-8dd3-72e779da93d2", "title": "Predicting Individual Viral Infection using Contact Data with LSTM Neural Network", "url": "https://pkghosh.wordpress.com/2020/08/18/predicting-individual-viral-infection-using-contact-data-with-lstm-neural-network/", "published_at": "2020-08-18T15:52:26+00:00" }, { "id": "01a0bddd-efbc-71fb-8dd3-72e77a4ad116", "title": "Semantic Search with Pre Trained Neural Transformer Model using Document, Sentence and Token Level Embedding", "url": "https://pkghosh.wordpress.com/2020/07/28/semantic-search-with-pre-trained-neural-transformer-model-using-document-sentence-and-token-level-embedding/", "published_at": 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"2020-05-11T15:02:04+00:00" }, { "id": "01a0bde0-92ba-72a6-ba21-8652bd086f31", "title": "Detecting Quarantine Violation from Mobile Phone Location Anomaly on Spark", "url": "https://pkghosh.wordpress.com/2020/04/20/detecting-quarantine-violation-from-mobile-phone-location-anomaly-on-spark/", "published_at": "2020-04-20T14:40:57+00:00" }, { "id": "01a0bde0-92ba-72a6-ba21-8652bd830a75", "title": "Building SciKitLearn Random Forest Model and Tuning Parameters without writing Python Code", "url": "https://pkghosh.wordpress.com/2020/03/26/building-scikitlearn-random-forest-model-and-tuning-parameters-without-writing-python-code/", "published_at": "2020-03-27T05:00:43+00:00" }, { "id": "01a0bde0-92ba-72a6-ba21-8652be70957e", "title": "Model Drift Detection with Kolmogorov Smirnov Statistic on Spark", "url": "https://pkghosh.wordpress.com/2020/02/24/model-drift-detection-with-kolmogorov-smirnov-statistic-on-spark/", "published_at": "2020-02-24T13:11:33+00:00" }, { "id": "01a0bde0-92ba-72a6-ba21-8652bf112887", "title": "Evaluation of Time Series Predictability with Kaboudan Metric using Prophet", "url": "https://pkghosh.wordpress.com/2020/01/21/evaluation-of-time-series-predictability-with-kaboudan-metric-using-prophet/", "published_at": "2020-01-21T17:47:51+00:00" }, { "id": "01a0bde0-92ba-72a6-ba21-8652bf9d4a91", "title": "Contextual Data Completeness Metric Computation on Spark", "url": "https://pkghosh.wordpress.com/2019/12/18/contextual-data-completeness-metric-computation-on-spark/", "published_at": "2019-12-18T17:03:27+00:00" }, { "id": "01a0bde0-92ba-72a6-ba21-8652c0862c23", "title": "Machine Learning Model Interpretation and Prescriptive Analytic with Lime", "url": "https://pkghosh.wordpress.com/2019/11/22/machine-learning-model-interpretation-and-prescriptive-analytic-with-lime/", "published_at": "2019-11-22T16:27:33+00:00" }, { "id": "01a0bde0-92ba-72a6-ba21-8652c13ecf31", "title": "Automated Machine Learning with Hyperopt and Scikitlearn without Writing Python Code", "url": "https://pkghosh.wordpress.com/2019/10/23/automated-machine-learning-with-hyperopt-and-scikitlearn-without-writing-python-code/", "published_at": "2019-10-23T17:40:01+00:00" }, { "id": "01a0bde3-04a7-7351-b0d5-09de813098c5", "title": "Time Series Trend and Seasonality Component Decomposition with STL on Spark", "url": "https://pkghosh.wordpress.com/2019/09/24/time-series-trend-and-seasonality-component-decomposition-with-stl-on-spark/", "published_at": "2019-09-24T20:03:49+00:00" }, { "id": "01a0bde3-04a7-7351-b0d5-09de8146888a", "title": "Missing Value Imputation with Restricted Boltzmann Machine Neural Network", "url": "https://pkghosh.wordpress.com/2019/08/26/missing-value-imputation-with-restricted-boltzmann-machine-neural-network/", "published_at": "2019-08-26T16:58:13+00:00" }, { "id": "01a0bde3-04a7-7351-b0d5-09de8242fa0e", "title": "Encoding High Cardinality Categorical Variables with Feature Hashing on Spark", "url": "https://pkghosh.wordpress.com/2019/08/07/encoding-high-cardinality-categorical-variables-with-feature-hashing-on-spark/", "published_at": "2019-08-07T17:28:27+00:00" }, { "id": "01a0bde3-04a7-7351-b0d5-09de82b0c03f", "title": "Time Series Sequence Anomaly Detection with Markov Chain on Spark", "url": "https://pkghosh.wordpress.com/2019/07/25/time-series-sequence-anomaly-detection-with-markov-chain-on-spark/", "published_at": "2019-07-25T17:32:47+00:00" }, { "id": "01a0bde3-04a7-7351-b0d5-09de8342a66e", "title": "Six Unsupervised Extractive Text Summarization Techniques Side by Side", "url": "https://pkghosh.wordpress.com/2019/06/27/six-unsupervised-extractive-text-summarization-techniques-side-by-side/", "published_at": "2019-06-28T04:03:10+00:00" }, { "id": "01a0bde3-04a7-7351-b0d5-09de83f04670", "title": "Synthetic Training Data Generation for Machine Learning Classification Problems using Ancestral Sampling", "url": "https://pkghosh.wordpress.com/2019/05/07/synthetic-training-data-generation-for-machine-learning-classification-problems-using-ancestral-sampling/", "published_at": "2019-05-07T18:34:53+00:00" }, { "id": "01a0bde3-04a7-7351-b0d5-09de84355265", "title": "Elastic Search or Solr Search Result Quality Evaluation with NCDG Metric on Spark", "url": "https://pkghosh.wordpress.com/2019/04/24/elastic-search-or-solr-search-result-quality-evaluation-with-ncdg-on-spark/", "published_at": "2019-04-24T18:34:02+00:00" }, { "id": "01a0bde3-04a7-7351-b0d5-09de84539f7a", "title": "Plugin Framework Based Data Transformation on Spark", "url": "https://pkghosh.wordpress.com/2019/03/21/plugin-framework-based-data-transformation-on-spark/", "published_at": "2019-03-21T15:37:25+00:00" }, { "id": "01a0bde3-04a7-7351-b0d5-09de8472333f", "title": "Supervised Machine Learning Parameter Search and Tuning with Simulated Annealing", "url": "https://pkghosh.wordpress.com/2019/02/10/supervised-machine-learning-parameter-search-and-tuning-with-simulated-annealing/", "published_at": "2019-02-11T02:58:15+00:00" }, { "id": "01a0bde3-04a7-7351-b0d5-09de84b8b275", "title": "Normal Distribution Fitness Test with Chi Square on Spark", "url": "https://pkghosh.wordpress.com/2019/01/30/normal-distribution-fitness-test-with-chi-square-on-spark/", "published_at": "2019-01-30T17:01:23+00:00" }, { "id": "01a0bde5-6185-7387-8246-a31d12cd455e", "title": "Time Series Seasonal Cycle Detection with Auto Correlation on Spark", "url": "https://pkghosh.wordpress.com/2018/12/23/time-series-seasonal-cycle-detection-with-auto-correlation-on-spark/", "published_at": "2018-12-23T13:55:52+00:00" }, { "id": "01a0bde5-6185-7387-8246-a31d12f93194", "title": "Bulk Mutation in an Integration Data Lake with Spark", "url": "https://pkghosh.wordpress.com/2018/11/19/bulk-mutation-in-an-integration-data-lake-with-spark/", "published_at": "2018-11-19T12:54:49+00:00" }, { "id": "01a0bde5-6185-7387-8246-a31d13226483", "title": "Learning Alarm Threshold from User Feedback using Decision Tree on Spark", "url": "https://pkghosh.wordpress.com/2018/10/15/learning-alarm-threshold-from-user-feedback-using-decision-tree-on-spark/", "published_at": "2018-10-15T15:33:17+00:00" }, { "id": "01a0bde5-6185-7387-8246-a31d140a06da", "title": "Contextual Outlier Detection with Statistical Modeling on Spark", "url": "https://pkghosh.wordpress.com/2018/09/18/contextual-outlier-detection-with-statistical-modeling-on-spark/", "published_at": "2018-09-18T16:50:39+00:00" }, { "id": "01a0bde5-6185-7387-8246-a31d14fab5ac", "title": "Pluggable Rule Driven Data Validation with Spark", "url": "https://pkghosh.wordpress.com/2018/08/20/pluggable-rule-driven-data-validation-with-spark/", "published_at": "2018-08-20T15:44:36+00:00" }, { "id": "01a0bde5-6185-7387-8246-a31d1587de7a", "title": "Improving Elastic Search Query Result with Query Expansion using Topic Modeling", "url": "https://pkghosh.wordpress.com/2018/07/18/improving-elastic-search-query-result-with-query-expansion-using-topic-modeling/", "published_at": "2018-07-19T00:47:23+00:00" }, { "id": "01a0bde5-6185-7387-8246-a31d15efe436", "title": "Leave One Out Encoding for Categorical Feature Variables on Spark", "url": "https://pkghosh.wordpress.com/2018/06/18/leave-one-out-encoding-for-categorical-feature-variables-on-spark/", "published_at": "2018-06-18T18:56:46+00:00" }, { "id": "01a0bde5-6185-7387-8246-a31d16095d8e", "title": "Auto Training and Parameter Tuning for a ScikitLearn based Model for Leads Conversion Prediction", "url": "https://pkghosh.wordpress.com/2018/05/14/auto-training-and-parameter-tuning-for-a-scikitlearn-based-model-for-leads-conversion-prediction/", "published_at": "2018-05-14T16:18:14+00:00" }, { "id": "01a0bde5-6185-7387-8246-a31d1689000f", "title": "Predicting CRM Lead Conversion with Gradient Boosting using ScikitLearn", "url": "https://pkghosh.wordpress.com/2018/04/18/predicting-crm-lead-conversion-with-gradient-boosting-using-scikitlearn/", "published_at": "2018-04-18T17:13:10+00:00" }, { "id": "01a0bde5-6185-7387-8246-a31d17072b08", "title": "Handling Categorical Feature Variables in Machine Learning using Spark", "url": "https://pkghosh.wordpress.com/2018/03/19/handling-categorical-feature-variables-in-machine-learning-using-spark/", "published_at": "2018-03-19T23:26:32+00:00" }, { "id": "01a0bde7-e680-71ec-843d-9be7f34ef0a4", "title": "Optimizing Discount Price for Perishable Products with Thompson Sampling using Spark", "url": "https://pkghosh.wordpress.com/2018/02/21/optimizing-discount-price-for-perishable-products-with-thompson-sampling-using-spark/", "published_at": "2018-02-22T03:33:24+00:00" }, { "id": "01a0bde7-e680-71ec-843d-9be7f3c76f45", "title": "Data Type Auto Discovery with Spark", "url": "https://pkghosh.wordpress.com/2018/01/10/data-type-auto-discovery-with-spark/", "published_at": "2018-01-10T16:57:13+00:00" }, { "id": "01a0bde7-e680-71ec-843d-9be7f4121304", "title": "Data Normalization with Spark", "url": "https://pkghosh.wordpress.com/2017/12/05/data-normalization-with-spark/", "published_at": "2017-12-06T03:41:22+00:00" }, { "id": "01a0bde7-e680-71ec-843d-9be7f4a4ee49", "title": "Removing Duplicates from Order Data Using Spark", "url": "https://pkghosh.wordpress.com/2017/11/07/removing-duplicates-from-order-data-using-spark/", "published_at": "2017-11-08T03:02:02+00:00" }, { "id": "01a0bde7-e680-71ec-843d-9be7f54efb66", "title": "Combating High Cardinality Features in Supervised Machine Learning", "url": "https://pkghosh.wordpress.com/2017/10/09/combating-high-cardinality-features-in-supervised-machine-learning/", "published_at": "2017-10-09T16:43:21+00:00" }, { "id": "01a0bde7-e680-71ec-843d-9be7f5ff2f94", "title": "Handling Rare Events and Class Imbalance in Predictive Modeling for Machine Failure", "url": "https://pkghosh.wordpress.com/2017/09/18/handling-rare-events-and-class-imbalance-in-predictive-modeling-for-machine-failure/", "published_at": "2017-09-19T03:41:54+00:00" }, { "id": "01a0bde7-e680-71ec-843d-9be7f6e31edc", "title": "Measuring Campaign Effectiveness for an Online Service on Spark", "url": "https://pkghosh.wordpress.com/2017/08/15/measuring-campaign-effectiveness-for-an-online-service-on-spark/", "published_at": "2017-08-16T04:05:26+00:00" }, { "id": "01a0bde7-e680-71ec-843d-9be7f7a57afc", "title": "Processing Missing Values with Hadoop", "url": "https://pkghosh.wordpress.com/2017/07/26/processing-missing-values-with-hadoop/", "published_at": "2017-07-27T02:28:00+00:00" }, { "id": "01a0bde7-e680-71ec-843d-9be7f7d73676", "title": "Project Assignment Optimization with Simulated Annealing on Spark", "url": "https://pkghosh.wordpress.com/2017/06/26/project-assignment-optimization-with-simulated-annealing-on-spark/", "published_at": "2017-06-26T19:33:55+00:00" }, { "id": "01a0bde7-e680-71ec-843d-9be7f8961969", "title": "Mining Seasonal Products from Sales Data", "url": "https://pkghosh.wordpress.com/2017/05/30/mining-seasonal-products-from-sales-data/", "published_at": "2017-05-30T18:47:59+00:00" } ] posts Claim your blog
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2025

Known Known, Known Unknown and Unknown Unknown for LLM Usersoriginal ↗
20 Jul 2025
Unreasonable Claim of Reasoning Ability of LLM : Part 2original ↗
28 Jun 2025
Does LLM Have Emergent Behavior?original ↗
31 May 2025
Neuro Symbolic Planning Solutions with LLMoriginal ↗
29 Mar 2025
Inference Time Scaling Demystified : Part 2original ↗
18 Feb 2025
LLM Based AI Agents : Myth and Realityoriginal ↗
24 Jan 2025

2024

Inference Time Scaling Demystified : Part 1original ↗
31 Dec 2024
Intuitive Explanation of LLM Hallucination with Semantic Entropyoriginal ↗
30 Nov 2024
Historical Review of General Problem Solver in AI Including LLMoriginal ↗
11 Oct 2024
Demystifying OpenAI Q* Reasoning Engineoriginal ↗
16 Sept 2024
Robot Navigation with Empowerment Based Implicit Rewardoriginal ↗
21 Aug 2024
LLM Failure on Out of Distribution Reasoning and Problem Solving Tasksoriginal ↗
29 Jul 2024
Pattern, Knowledge, Intelligence and LLMoriginal ↗
29 Jun 2024
Demonstrating LLM Model Collapse with a Simple Analogical Example.original ↗
28 May 2024
Route Planning with DynaQ Reinforcement Learningoriginal ↗
24 Apr 2024
Time Series Feature Engineering with Histogramoriginal ↗
31 Mar 2024
AI Chatbots with Human in the Looporiginal ↗
23 Feb 2024
Time Series Classification with Neural Network using Random Sub Sequence Statistics as Featuresoriginal ↗
27 Jan 2024

2023

Time Series Forecasting with Decomposition and Two Linear Networksoriginal ↗
28 Dec 2023
Unreasonable Claim of Reasoning Ability of LLM: Part 1original ↗
24 Nov 2023
Customization of LLM ChatBots with Retrieval Augmented Generationoriginal ↗
26 Oct 2023
Time Series Data Exploration with Wavelet Transformoriginal ↗
29 Sept 2023
Store Inventory Replenishment with Q Value Reinforcement Learningoriginal ↗
25 Aug 2023
Online Advertisement Placement with Contextual Bandit.original ↗
27 Jul 2023
Time Series Sequence Anomaly Detection with Markov Chainoriginal ↗
28 Jun 2023
Realtime Concept Drift Detection for Machine Learning Classification Modelsoriginal ↗
29 May 2023
Delivery Vehicle Route Planning with Ant Colony Optimizationoriginal ↗
28 Apr 2023
Synthetic Time Series Data Generationoriginal ↗
29 Mar 2023
Simulating A/B Test with Counterfactual and Machine Learning Regression Modeloriginal ↗
28 Feb 2023
Synthetic Regression Data Generation in Pythonoriginal ↗
23 Jan 2023

2022

AI Past, Present and Futureoriginal ↗
28 Dec 2022
Information Gain based Feature Selection in Python for Machine Learning Modelsoriginal ↗
30 Nov 2022
Machinery Fault Detection with Vibration Data Anomaly Detection using Variational Auto Encoder.original ↗
25 Oct 2022
Patient Appointment Management Optimization with Simulated Annealingoriginal ↗
29 Sept 2022
Stock Portfolio Balancing with Monte Carlo Simulationoriginal ↗
24 Aug 2022
Pricing Policy Evaluation and Comparison with Temporal Difference Learningoriginal ↗
31 Jul 2022
Tabular Data Column Semantic Type Identification with Contrastive Deep Learningoriginal ↗
30 Jun 2022
Feature Selection with Information Theory Based Techniques in Python.original ↗
30 May 2022
Discovering Subject Matter Experts from Email Communication Data using Graph Convolution Network.original ↗
28 Apr 2022
Gig Economy Workforce Scheduling with Reinforcement Learningoriginal ↗
28 Mar 2022
Out of Distribution Data Detection in Deployed Machine Learning Modelsoriginal ↗
25 Feb 2022
Remedial Action Recommendation with Machine Learning and Genetic Algorithmoriginal ↗
26 Jan 2022

2021

Conformal Prediction for a Neural Regression Modeloriginal ↗
31 Dec 2021
Machine Learning Model Performance Robustness Based on Local Neighborhood Performanceoriginal ↗
1 Dec 2021
Class Separation based Machine Learning Model Performance Metricoriginal ↗
16 Oct 2021
Deep Learning based Anomaly Detection for Data with Temporal and Spatial Correlationoriginal ↗
28 Sept 2021
Find Out How Well Your Machine Learning Model is Calibratedoriginal ↗
25 Aug 2021
Duplicate Data Detection with Neural Network and Contrastive Learningoriginal ↗
22 Jul 2021
eCommerce Order Processing System Monitoring with Isolation Forest Based Anomaly Detection on Sparkoriginal ↗
28 Jun 2021
Data Driven Causal Relationship Discovery with Python Example Codeoriginal ↗
25 May 2021
Unsupervised Concept Drift Detection Techniques for Machine Learning Models with Examples in Pythonoriginal ↗
21 Apr 2021
Robustness Measurement of Machine Learning Models with Examples in Pythonoriginal ↗
25 Mar 2021
Detecting and Measuring Human Bias in Machine Learning Modelsoriginal ↗
26 Feb 2021
Customer Service Quality Monitoring with AutoEncoder based Anomalous Case Detectionoriginal ↗
20 Jan 2021

2020

Concept Drift Detection Techniques with Python Implementation for Supervised Machine Learning Modelsoriginal ↗
24 Dec 2020
Meeting Schedule Optimization with Genetic Algorithm in Pythonoriginal ↗
26 Nov 2020
Causal Inference with Deep Learning using Manufacturing Supply Chain Optimization as an Exampleoriginal ↗
28 Oct 2020
Time Series Change Point Detection with Two Sample Statistic on Spark with Application for Retail Sales Dataoriginal ↗
28 Sept 2020
Predicting Individual Viral Infection using Contact Data with LSTM Neural Networkoriginal ↗
18 Aug 2020
Semantic Search with Pre Trained Neural Transformer Model using Document, Sentence and Token Level Embeddingoriginal ↗
28 Jul 2020
Learn about Your Data with about Hundred Data Exploration Functions All in One Python Classoriginal ↗
14 Jul 2020
Deep Reinforcement Learning with RLlib and TensorFlow for Price Optimizationoriginal ↗
8 Jun 2020
Monte Carlo Simulation Library in Python with Project Cost Estimation as an Exampleoriginal ↗
11 May 2020
Detecting Quarantine Violation from Mobile Phone Location Anomaly on Sparkoriginal ↗
20 Apr 2020
Building SciKitLearn Random Forest Model and Tuning Parameters without writing Python Codeoriginal ↗
27 Mar 2020
Model Drift Detection with Kolmogorov Smirnov Statistic on Sparkoriginal ↗
24 Feb 2020
Evaluation of Time Series Predictability with Kaboudan Metric using Prophetoriginal ↗
21 Jan 2020

2019

Contextual Data Completeness Metric Computation on Sparkoriginal ↗
18 Dec 2019
Machine Learning Model Interpretation and Prescriptive Analytic with Limeoriginal ↗
22 Nov 2019
Automated Machine Learning with Hyperopt and Scikitlearn without Writing Python Codeoriginal ↗
23 Oct 2019
Time Series Trend and Seasonality Component Decomposition with STL on Sparkoriginal ↗
24 Sept 2019
Missing Value Imputation with Restricted Boltzmann Machine Neural Networkoriginal ↗
26 Aug 2019
Encoding High Cardinality Categorical Variables with Feature Hashing on Sparkoriginal ↗
7 Aug 2019
Time Series Sequence Anomaly Detection with Markov Chain on Sparkoriginal ↗
25 Jul 2019
Six Unsupervised Extractive Text Summarization Techniques Side by Sideoriginal ↗
28 Jun 2019
Synthetic Training Data Generation for Machine Learning Classification Problems using Ancestral Samplingoriginal ↗
7 May 2019
Elastic Search or Solr Search Result Quality Evaluation with NCDG Metric on Sparkoriginal ↗
24 Apr 2019
Plugin Framework Based Data Transformation on Sparkoriginal ↗
21 Mar 2019
Supervised Machine Learning Parameter Search and Tuning with Simulated Annealingoriginal ↗
11 Feb 2019
Normal Distribution Fitness Test with Chi Square on Sparkoriginal ↗
30 Jan 2019

2018

Time Series Seasonal Cycle Detection with Auto Correlation on Sparkoriginal ↗
23 Dec 2018
Bulk Mutation in an Integration Data Lake with Sparkoriginal ↗
19 Nov 2018
Learning Alarm Threshold from User Feedback using Decision Tree on Sparkoriginal ↗
15 Oct 2018
Contextual Outlier Detection with Statistical Modeling on Sparkoriginal ↗
18 Sept 2018
Pluggable Rule Driven Data Validation with Sparkoriginal ↗
20 Aug 2018
Improving Elastic Search Query Result with Query Expansion using Topic Modelingoriginal ↗
19 Jul 2018
Leave One Out Encoding for Categorical Feature Variables on Sparkoriginal ↗
18 Jun 2018
Auto Training and Parameter Tuning for a ScikitLearn based Model for Leads Conversion Predictionoriginal ↗
14 May 2018
Predicting CRM Lead Conversion with Gradient Boosting using ScikitLearnoriginal ↗
18 Apr 2018
Handling Categorical Feature Variables in Machine Learning using Sparkoriginal ↗
19 Mar 2018
Optimizing Discount Price for Perishable Products with Thompson Sampling using Sparkoriginal ↗
22 Feb 2018
Data Type Auto Discovery with Sparkoriginal ↗
10 Jan 2018

2017

Data Normalization with Sparkoriginal ↗
6 Dec 2017
Removing Duplicates from Order Data Using Sparkoriginal ↗
8 Nov 2017
Combating High Cardinality Features in Supervised Machine Learningoriginal ↗
9 Oct 2017
Handling Rare Events and Class Imbalance in Predictive Modeling for Machine Failureoriginal ↗
19 Sept 2017
Measuring Campaign Effectiveness for an Online Service on Sparkoriginal ↗
16 Aug 2017
Processing Missing Values with Hadooporiginal ↗
27 Jul 2017
Project Assignment Optimization with Simulated Annealing on Sparkoriginal ↗
26 Jun 2017
Mining Seasonal Products from Sales Dataoriginal ↗
30 May 2017
Next