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You will get a CNN model for image classification using Python and Keras
Rising Talent
Rising Talent
Project details
You will get a fully trained CNN (Convolutional Neural Network) model tailored to your image classification task — fast, accurate, and well-documented. Whether you're working on cat vs dog images, defect detection, or facial classification, I will help you go from raw images to a trained model with clear evaluation metrics and a delivery-ready notebook.
As a Computer Engineer with hands-on experience in deep learning, I specialize in building and optimizing custom models using TensorFlow/Keras, NumPy, and OpenCV. I’ve worked on real-world datasets involving complex image classes and delivered robust models that are ready to be deployed or integrated into applications.
✅ What sets this project apart:
Clean, reproducible Jupyter Notebook
Professional model tuning (avoiding overfitting)
Transparent communication and fast delivery
Optional deployment support (on request)
I’m committed to delivering work that is both technically sound and aligned with your business goals. Let's build something powerful together.
As a Computer Engineer with hands-on experience in deep learning, I specialize in building and optimizing custom models using TensorFlow/Keras, NumPy, and OpenCV. I’ve worked on real-world datasets involving complex image classes and delivered robust models that are ready to be deployed or integrated into applications.
✅ What sets this project apart:
Clean, reproducible Jupyter Notebook
Professional model tuning (avoiding overfitting)
Transparent communication and fast delivery
Optional deployment support (on request)
I’m committed to delivering work that is both technically sound and aligned with your business goals. Let's build something powerful together.
Machine Learning Tools
Azure Machine Learning, deeplearn.js, Keras, Microsoft Power BI, NumPy, OpenCV, pandas, Python Scikit-Learn, PyTorch, scikit-learn, SQL, TensorFlowWhat's included
| Service Tiers |
Starter
$20
|
Standard
$40
|
Advanced
$60
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 3 days |
Number of Revisions | 2 | 3 | 5 |
Number of Model Variations | 0 | 2 | 3 |
Number of Scenarios | 2 | 3 | 2 |
Number of Graphs/Charts | 3 | 5 | 5 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$10 - $100
Additional Revision
+$5
Additional Graph/Chart
(+ 1 Day)
+$2
Model Documentation
(+ 2 Days)
+$5Frequently asked questions
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MH
Mazhar H.
Jul 17, 2026
Fix AI Phone Bot Booking Issue
Saad quickly identified the root cause of my Vapi AI phone bot's date handling issue and fixed it efficiently. He explained everything clearly, communicated well, and treated the project with real professionalism. I'd definitely hire him again.
About Rana Saad
AI Agent Developer | RAG, n8n Automation, Vapi Voice Agents | Python
100%
Job Success
Lahore, Pakistan - 10:00 am local time
Most AI projects stall at the same point: the demo works, then real documents arrive and the agent starts making things up. No logging, no fallback, nobody knows what broke. I build the part that comes after the demo.
✅ Production AI Agents: LangGraph, LangChain, Python, FastAPI
✅ RAG Systems grounded in YOUR data, with citations, not guesses
✅ Voice Agents: Vapi, Retell (build new ones, fix broken ones)
✅ Automation Workflows: n8n, APIs, PostgreSQL, Redis
➤ 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗜'𝘃𝗲 𝗯𝘂𝗶𝗹𝘁:
🔹 𝗣𝗼𝗹𝗶𝗰𝘆𝗠𝗶𝗻𝗱: RAG documentation assistant (LangGraph + FAISS). Answers come from the client's documents with citations, zero invented facts.
🔹 𝗟𝗲𝗮𝗱𝗣𝗶𝗹𝗼𝘁: Multi-tenant WhatsApp lead qualifier. LangGraph agent, FastAPI backend, PostgreSQL and Redis. Multiple client accounts, one system.
🔹 𝗟𝗟𝗠 𝗡𝗲𝘄𝘀 𝗧𝗿𝗮𝗱𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲: Live pipeline from Reuters/Eikon into an LLM judge with structured JSON output, then into Interactive Brokers with confidence-tiered order sizing. Includes offline auto-labeler and review UI, because a trading agent you can't audit is a liability.
🔹 𝗩𝗼𝗶𝗰𝗲 𝗔𝗴𝗲𝗻𝘁 𝗥𝗲𝘀𝗰𝘂𝗲: Client's Vapi phone bot was mishandling booking dates. Found the root cause, fixed it in a day. His words: "explained everything clearly, communicated well."
➤ 𝗘𝘃𝗲𝗿𝘆 𝗯𝘂𝗶𝗹𝗱 𝗶𝗻𝗰𝗹𝘂𝗱𝗲𝘀:
✔️ Error handling, retries, and logging, so failures surface instead of going silent
✔️ Human approval steps wherever the agent touches money or client data
✔️ Documentation + walkthrough recording, so your team runs it without me
✔️ Honest scoping first. If automation is the wrong answer, I'll say so before you spend
🛠️ 𝗦𝘁𝗮𝗰𝗸: Python · LangGraph · LangChain · FastAPI · n8n · PostgreSQL · Redis · FAISS · Claude API · OpenAI API · Vapi · Retell · REST APIs · Webhooks
🎓 I've taught Claude Code and AI automation to 100+ people across 11+ countries in live sessions. You get plain English, not jargon.
💬 Send me the process you want automated or the agent that's misbehaving. Within 24 hours I'll tell you how I'd build it, where it could fail, and what it costs, before anything is agreed.
Steps for completing your project
After purchasing the project, send requirements so Rana Saad can start the project.
Delivery time starts when Rana Saad receives requirements from you.
Rana Saad works on your project following the steps below.
Revisions may occur after the delivery date.
Dataset Review & Understanding
I will review the dataset, understand the labels and structure, and confirm feasibility for CNN training.
Preprocessing & Data Augmentation
I will clean, resize, and augment the dataset to improve generalization during training.