Testimonials

  • Pinned
    I am a beginner in OpenseePy and requested a one-on-one session. The explanation from sir is absolutely crystal clear and its not just the coding, the technical details are explained in a lucid manner which is hard to find otherwise. I am extremely thankful for the session and enabling me to start my OpenseePy journey with confidence. I highly recommend such one-on-one sessions for focused discussions addressing the doubts and challenges faced by beginners, as well as by researchers. Thanks again !!!
    Shivaji Sarvade

Services

Priority DM
4.7

Source Code Request - YouTube Video Tutorials

Exclusive Source Code Access Made Easy!
FREE
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Digital Product

Prediction Using Neural Networks (MLP) - PyTorch

Dataset, Model Development, Saving and Model Inferencing
149
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Digital Product

Prediction Using Linear GAM

on Concrete Strength Prediction
149
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Digital Product

Semantic Search App with Embeddings

Build AI-Powered Product Search using FAISS & Streamlit
149
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Digital Product

Prediction Using Random Forest Regression

on Concrete Strength Prediction
149
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Digital Product

RAG Deep Dive #7 - Build an ArXiv Search Tool

Open WebUI Tool Development From Scratch
149199
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Digital Product

Build a Free Speech-to-Text App

with OpenAI Whisper and Streamlit – No API Keys Needed
149
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Digital Product

RAG Deep Dive #6 - Open WebUI Tools Explained

How LLMs Use Tools + Build a Currency Converter Tool
149199
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Digital Product

5 Widely Used Gradient Boosting Algorithms

on Concrete Strength Prediction
149
Best Seller
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Digital Product
5

HyDE RAG Explained

Hypothetical Document Embeddings for Better Retrieval
149
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Digital Product

Query Decomposition + Fusion RAG Explained

Balanced Context and Better Retrieval
149
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Digital Product
249299
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Digital Product

DeepXDE Tutorial #9 - Nonlinear System of PDEs

Schrödinger Equation with PINNs || PyTorch
249299
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Digital Product

PINNs - Solving System of ODEs

A Beginner's Tutorial
249299
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Digital Product

PINNs - Inverse PINNs for Coupled ODEs

Estimate Multi Solution and Multi Parameters Simultaneously
249299
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Digital Product
5
249299
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Digital Product

DeepXDE Tutorial #2: Setting Up ODEs/PDEs

Boundary Conditions with Jacobian and Hessian
249299
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Digital Product
249299
Video meeting . 60 mins
1,000
Courses
1,000
Package . 5 products

Multiple 1-1 Session Package (cc)

1 - 1 Personal Guidance
Video Meeting
5
7,0007,500
Best Deal
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Digital Product

Building Agentic RAG from Scratch

Complete Guide to Turning Research Papers into Code
149
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Digital Product

Prediction Using Multivariable Linear Regression

Dataset, Model Development, Saving and Model Inferencing
149
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Digital Product

Automated Machine Learning Using TPOT

on Concrete Strength Prediction
149
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Digital Product

GPT-Powered Automatic Quiz Generation

Deployment via Google Forms + Apps Script
149
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Digital Product

Six Most Important Plots in Studies

on Concrete Strength Prediction
149
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Digital Product

RAG Deep Dive #8 - Open WebUI Tool Servers

FastAPI Currency Converter | Standalone Tool vs Tool Server
149199
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Digital Product

RAG Deep Dive #9 - Open WebUI Pipelines Explained

Build from Scratch | From Setup to First Workflow
149199
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Digital Product

RAG Deep Dive #10 - Open WebUI Pipelines FusionRAG

Fusion RAG with FAISS & Query Decomposition
149199
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Digital Product
5
149
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Digital Product

Ask Your PDFs Anything with AI (RAG Tutorial)

Build Your Own ChatGPT for Documents
149
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Digital Product

PINNs - Solving Inverse Burgers' Equation

Simultaneous Solution & Viscosity Parameter Estimation
249299
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Digital Product
249299
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Digital Product
249299
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Digital Product
249299
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Digital Product

DeepXDE Tutorial #8 - Mastering Callbacks

To Automate & Visualize Neural Network Training Predictions
249299
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Digital Product
249299
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Digital Product
249299
Video meeting . 60 mins

Civil Engineering Career Guidance

Build your career with clarity 🌟🌟🌟🌟🌟
800
Video meeting . 60 mins
1,000
Video meeting . 60 mins
5

1 - 1 Personal Guidance

For Both Users and Developers
1,500
Popular

Ratings and feedback

4.9/5
17 ratings
10
Testimonials

About me

We're experts in structural mechanics simulation, specializing in Machine Learning and Finite Element Analysis (FEA) - Linear and Nonlinear. Whether it's understanding the theory or hands-on programming or software packages, we've got you covered. Our trainings dive deep into two key areas: 1 - Machine Learning: Get a solid foundation in theory and learn to use Python libraries like Scikit-learn, PyTorch, Keras, and TensorFlow etc to program your ML models from scratch. 2 - Finite Element Analysis (FEA) - Linear and Nonlinear: Get a solid foundation in theory and learn to program your own FEA programs from scratch in Python. We also cover practical applications using tools like OpenSees, OpenSeesPy, LS-Dyna, ANSYS, COMSOL and Abaqus. So, if you're keen on mastering these skills, you're in the right place!