Author Archives: Pranab

About Pranab

I am Pranab Ghosh, a software professional in the San Francisco Bay area. I manipulate bits and bytes for the good of living beings and the planet. I have worked with myriad of technologies and platforms in various business domains for early stage startups, large corporations and anything in between. I am an active blogger and open source project owner. I am passionate about technology and green and sustainable living. My technical interest areas are Big Data, Distributed Processing, NOSQL databases, Machine Learning and Programming languages. I am fascinated by problems that don't have neat closed form solution.

Route Planning with DynaQ Reinforcement Learning

Posted on April 23, 2024 by Pranab

The purpose of a simple route planning is to find the optimum route from one location to another that minimizes travel distance or time. Generally various heuristic optimization techniques are used to find optimum route. In this post we will … Continue reading →

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Time Series Feature Engineering with Histogram

Posted on March 30, 2024 by Pranab

Typically time series data requires manual feature engineering unless you are using Deep Learning. Deep Learning alleviates you from this task but there is no guarantee. There are many techniques for feature engineering time series. In this post will use … Continue reading →

Posted in AI, Anomaly Detection, Data Science, feature engineering, Machine Learning, Time Series Analytic | Tagged , , , , , , | Leave a comment

AI Chatbots with Human in the Loop

Posted on February 23, 2024 by Pranab

LLM based AI Chatbots on auto pilot is not very wise and risky. Even when manually composing text or getting an answer to a question through AI chatbots, a human tries different prompts and evaluates responses. So there is human … Continue reading →

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Time Series Classification with Neural Network using Random Sub Sequence Statistics as Features

Posted on January 26, 2024 by Pranab

Deep Learning models generally extract features in the early layers of the network. It does away with the need for manual feature engineering making it attractive ML technique. However, this not always the case, time series clustering and classification being … Continue reading →

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Time Series Forecasting with Decomposition and Two Linear Networks

Posted on December 28, 2023 by Pranab

Many solutions for time series forecasting are available, starting from classic ARIMA to Deep Learning Transformer based. Results are varied. There doesn’t appear to be any consensus for the best performing forecasting model overall. In this post, we will go … Continue reading →

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Unreasonable Claim of Reasoning Ability of LLM

Posted on November 24, 2023 by Pranab

Reasoning is critical for problem solving, decision making and in general for human intelligence. There have been various claims of reasoning abilities of LLM. Typically these claims are based on few anecdotal examples, followed by by some broad brush conclusions. … Continue reading →

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Customization of LLM ChatBots with Retrieval Augmented Generation

Posted on October 26, 2023 by Pranab

For many domain specific business application the pre trained LLM is not enough. Customization is necessary. Out of the box pre trained black box models, even with prompt tuning may be inadequate. There are two ways to customize LLM with … Continue reading →

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Time Series Data Exploration with Wavelet Transform

Posted on September 29, 2023 by Pranab

Time series data provides time domain information and Fast Fourier Transform provides frequency domain information only. What if you wanted both, for example you may be interested in frequency domain information only within a specified time range. Wavelet transform provides … Continue reading →

Posted in Anomaly Detection, Data Science, Python, Time Series Analytic | Tagged | Leave a comment

Store Inventory Replenishment with Q Value Reinforcement Learning

Posted on August 25, 2023 by Pranab
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Online Advertisement Placement with Contextual Bandit.

Posted on July 27, 2023 by Pranab

There is a class of algorithms called Multi Arm Bandit (MAB) applicable for Reinforcement Leaning problems without state. Sometimes there is side information available for each action. MAB algorithms that take this side information aka context into account are called … Continue reading →

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