Publications by MSDS 6372: Jacob Turner: Student: Jessica McPhaul link:
stepbystep2
Step 3: Upload the Training Dataset Now, we will upload the 3D training dataset (3D_GPT_Training_Data.jsonl) to OpenAI for fine-tuning. 3.1 Download the JSONL Training Dataset If you haven’t downloaded it yet, get it here: 📂 Download 3D_GPT_Training_Data.jsonl Make sure the file is in your current working directory (where you will run the...
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StepByStep3
Step 11: Optimize & Scale Your AI-Powered GIS Tool Now that your Quantum GIS GPT dashboard is live, we will: ✅ Improve real-time performance with streaming ✅ Deploy the GIS app on a cloud server for public access ✅ Enhance 3D visualization with WebGL tools 11.1 Optimize GIS Performance with Real-Time Data Streaming To make AI analysis...
35222 sym Python (8204 sym/31 pcs)
annoyed
Step 1: Data Analysis & Visualization The histograms above show: 1. Trace Distribution: Most states have trace ≈ 1, confirming proper normalization. 2. Purity Distribution: Purity (Tr(ρ²)) varies, indicating a mix of pure and mixed states. 3. Eigenvalue Distribution: The eigenvalues are mostly positive and sum to 1, validating the dataset...
36490 sym Python (11635 sym/11 pcs)
quantumGIS
Stream Name Texaschikkita Stream URL https://rpubs.com/Texaschikkita Stream ID 9962324179 Measurement Id G-CV2648GQMK 1. Overview of GIS and Quantum Computing Integration GIS: Handles spatial data, analyzes geographic patterns, and visualizes information using maps. Quantum Computing: Solves problems with exponential complexity using quantum ...
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chatbot\
1. Define Your AI Chatbot Requirements Before development, decide: - Will it be fine-tuned on OpenAI’s GPT or fully custom? - Will it require Quantum, GIS, 3D modeling, molecular tracking datasets? - Do you want real-time data processing from scientific APIs? - Should it be a web-based chatbot or an API-based system? - streamlit - https://ch...
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capstone guided process 12
Step 1: Data Analysis & Visualization The histograms above show: 1. Trace Distribution: Most states have trace ≈ 1, confirming proper normalization. 2. Purity Distribution: Purity (Tr(ρ²)) varies, indicating a mix of pure and mixed states. 3. Eigenvalue Distribution: The eigenvalues are mostly positive and sum to 1, validating the dataset....
56872 sym Python (19079 sym/28 pcs) 3 img
7333_CS5_DRAFT
Abstract Security firewalls generate large volumes of log data detailing whether network connections are allowed or blocked. Manually analyzing these logs is labor-intensive and error-prone, motivating the use of machine learning to automate firewall decisions. This white paper investigates a Support Vector Machine (SVM) and a Stochastic Gradie...
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QTW ds7333 - module 11 : neural networks
Study Guide: Introduction to Neural Networks 1. Understanding Neural Networks Neural networks are inspired by the structure of the human brain. The fundamental unit is the neuron, which receives inputs, processes them, and produces an output. These artificial neurons mimic biological neurons in function. Biological Analogy: Neuron (Biological...
91639 sym Python (10323 sym/30 pcs) 22 tbl
Module10 Out of Core Methods
Study Guide: Large Datasets and Out-of-Core Methods Module 10: Out-of-Core Methods Section 1: Large Datasets Overview Large datasets are a fundamental challenge in modern data science and machine learning. As datasets grow in size, traditional machine learning algorithms face issues related to memory, processing power, and computational effi...
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module9InClass
Polished and Concise Version Regularization (Inverse Scaling) The SVM’s regularization parameter \(C\) controls the trade-off between error minimization and model simplicity: High \(C\): Less regularization, leading to a model that closely fits the training data. Low \(C\): More regularization, resulting in a simpler model that may underf...
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