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Welcome to our open-source project focused on applying Machine Learning (ML) and Deep Learning (DL) techniques to Machine Scheduling and Time Management, often referred to as Optimal Processing. Our goal is to revolutionize the way tasks and processes are managed in various projects by leveraging advanced computational methods to optimize efficiency and productivity.

Installation

To install TiLearn using PyPI, run the following command:

pip install TiLearn

Then, in the TiLearn repository that you cloned, simply run:

pip install .

Documentation and Usage

For in-depth instructions on installation and building the documentation, see the TiLearn Documentation Guide and Tutorial.

Link: https://bancie.github.io/TiLearn/

Project Goals

  • Optimized Scheduling: Develop algorithms that can create optimal schedules for machines, minimizing downtime and maximizing throughput.
  • Predictive Maintenance: Implement predictive models to foresee and mitigate potential machine failures, ensuring continuous and efficient operations.
  • Time Management: Utilize deep learning models to enhance time management practices, helping businesses or individuals and team allocate resources more effectively and meet deadlines.
  • Scalability: Design solutions that are scalable and adaptable to different industrial environments and varying sizes of operations.

Responsibilities

As part of this open-source project, contributors are encouraged to:

  • Algorithm Development: Create and refine machine learning and deep learning algorithms tailored to scheduling and time management.
  • Data Collection and Preprocessing: Gather and preprocess data from various sources to train and validate models.
  • Model Training and Evaluation: Train models using the collected data and evaluate their performance to ensure accuracy and reliability.
  • Integration and Testing: Integrate developed models into real-world scheduling systems and conduct extensive testing to validate their effectiveness.
  • Documentation and Support: Maintain comprehensive documentation of the project, providing clear guidelines for usage and contribution. Assist users and other contributors through forums and issue tracking.

For inquiries, please contact me at chibangn1@gmail.com

About

A Python library for machine scheduling.

bancie.github.io/TiLearn/

Topics

deep-learningmachine-learningmachine-schedulingoperations-researchoptimizationtime-management

Resources

Readme
MIT license
Activity

Stars

13 stars

Watchers

3 watching

Forks

0 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Bancie logo
homepagePyPI
Welcome to our open-source project focused on applying Machine Learning (ML) and Deep Learning (DL) techniques to Machine Scheduling and Time Management, often referred to as Optimal Processing. Our goal is to revolutionize the way tasks and processes are managed in various projects by leveraging advanced computational methods to optimize efficiency and productivity.
Installation
To install TiLearn using PyPI, run the following command:
pip install TiLearn

Then, in the TiLearn repository that you cloned, simply run:
pip install .

Documentation and Usage
For in-depth instructions on installation and building the documentation, see the TiLearn Documentation Guide and Tutorial.
Link: https://bancie.github.io/TiLearn/
Project Goals
  • Optimized Scheduling: Develop algorithms that can create optimal schedules for machines, minimizing downtime and maximizing throughput.
  • Predictive Maintenance: Implement predictive models to foresee and mitigate potential machine failures, ensuring continuous and efficient operations.
  • Time Management: Utilize deep learning models to enhance time management practices, helping businesses or individuals and team allocate resources more effectively and meet deadlines.
  • Scalability: Design solutions that are scalable and adaptable to different industrial environments and varying sizes of operations.
Responsibilities
As part of this open-source project, contributors are encouraged to:
  • Algorithm Development: Create and refine machine learning and deep learning algorithms tailored to scheduling and time management.
  • Data Collection and Preprocessing: Gather and preprocess data from various sources to train and validate models.
  • Model Training and Evaluation: Train models using the collected data and evaluate their performance to ensure accuracy and reliability.
  • Integration and Testing: Integrate developed models into real-world scheduling systems and conduct extensive testing to validate their effectiveness.
  • Documentation and Support: Maintain comprehensive documentation of the project, providing clear guidelines for usage and contribution. Assist users and other contributors through forums and issue tracking.
For inquiries, please contact me at chibangn1@gmail.com
About
A Python library for machine scheduling.
bancie.github.io/TiLearn/
Topics
deep-learningmachine-learningmachine-schedulingoperations-researchoptimizationtime-management
Resources
Readme
MIT license
Activity
Stars
13 stars
Watchers
3 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Bancie logo
homepagePyPI
Welcome to our open-source project focused on applying Machine Learning (ML) and Deep Learning (DL) techniques to Machine Scheduling and Time Management, often referred to as Optimal Processing. Our goal is to revolutionize the way tasks and processes are managed in various projects by leveraging advanced computational methods to optimize efficiency and productivity.
Installation
To install TiLearn using PyPI, run the following command:
pip install TiLearn

Then, in the TiLearn repository that you cloned, simply run:
pip install .

Documentation and Usage
For in-depth instructions on installation and building the documentation, see the TiLearn Documentation Guide and Tutorial.
Link: https://bancie.github.io/TiLearn/
Project Goals
  • Optimized Scheduling: Develop algorithms that can create optimal schedules for machines, minimizing downtime and maximizing throughput.
  • Predictive Maintenance: Implement predictive models to foresee and mitigate potential machine failures, ensuring continuous and efficient operations.
  • Time Management: Utilize deep learning models to enhance time management practices, helping businesses or individuals and team allocate resources more effectively and meet deadlines.
  • Scalability: Design solutions that are scalable and adaptable to different industrial environments and varying sizes of operations.
Responsibilities
As part of this open-source project, contributors are encouraged to:
  • Algorithm Development: Create and refine machine learning and deep learning algorithms tailored to scheduling and time management.
  • Data Collection and Preprocessing: Gather and preprocess data from various sources to train and validate models.
  • Model Training and Evaluation: Train models using the collected data and evaluate their performance to ensure accuracy and reliability.
  • Integration and Testing: Integrate developed models into real-world scheduling systems and conduct extensive testing to validate their effectiveness.
  • Documentation and Support: Maintain comprehensive documentation of the project, providing clear guidelines for usage and contribution. Assist users and other contributors through forums and issue tracking.
For inquiries, please contact me at chibangn1@gmail.com
About
A Python library for machine scheduling.
bancie.github.io/TiLearn/
Topics
deep-learningmachine-learningmachine-schedulingoperations-researchoptimizationtime-management
Resources
Readme
MIT license
Activity
Stars
13 stars
Watchers
3 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Bancie logo
homepagePyPI
Welcome to our open-source project focused on applying Machine Learning (ML) and Deep Learning (DL) techniques to Machine Scheduling and Time Management, often referred to as Optimal Processing. Our goal is to revolutionize the way tasks and processes are managed in various projects by leveraging advanced computational methods to optimize efficiency and productivity.
Installation
To install TiLearn using PyPI, run the following command:
pip install TiLearn

Then, in the TiLearn repository that you cloned, simply run:
pip install .

Documentation and Usage
For in-depth instructions on installation and building the documentation, see the TiLearn Documentation Guide and Tutorial.
Link: https://bancie.github.io/TiLearn/
Project Goals
  • Optimized Scheduling: Develop algorithms that can create optimal schedules for machines, minimizing downtime and maximizing throughput.
  • Predictive Maintenance: Implement predictive models to foresee and mitigate potential machine failures, ensuring continuous and efficient operations.
  • Time Management: Utilize deep learning models to enhance time management practices, helping businesses or individuals and team allocate resources more effectively and meet deadlines.
  • Scalability: Design solutions that are scalable and adaptable to different industrial environments and varying sizes of operations.
Responsibilities
As part of this open-source project, contributors are encouraged to:
  • Algorithm Development: Create and refine machine learning and deep learning algorithms tailored to scheduling and time management.
  • Data Collection and Preprocessing: Gather and preprocess data from various sources to train and validate models.
  • Model Training and Evaluation: Train models using the collected data and evaluate their performance to ensure accuracy and reliability.
  • Integration and Testing: Integrate developed models into real-world scheduling systems and conduct extensive testing to validate their effectiveness.
  • Documentation and Support: Maintain comprehensive documentation of the project, providing clear guidelines for usage and contribution. Assist users and other contributors through forums and issue tracking.
For inquiries, please contact me at chibangn1@gmail.com
About
A Python library for machine scheduling.
bancie.github.io/TiLearn/
Topics
deep-learningmachine-learningmachine-schedulingoperations-researchoptimizationtime-management
Resources
Readme
MIT license
Activity
Stars
13 stars
Watchers
3 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Bancie logo
homepagePyPI
Welcome to our open-source project focused on applying Machine Learning (ML) and Deep Learning (DL) techniques to Machine Scheduling and Time Management, often referred to as Optimal Processing. Our goal is to revolutionize the way tasks and processes are managed in various projects by leveraging advanced computational methods to optimize efficiency and productivity.
Installation
To install TiLearn using PyPI, run the following command:
pip install TiLearn

Then, in the TiLearn repository that you cloned, simply run:
pip install .

Documentation and Usage
For in-depth instructions on installation and building the documentation, see the TiLearn Documentation Guide and Tutorial.
Link: https://bancie.github.io/TiLearn/
Project Goals
  • Optimized Scheduling: Develop algorithms that can create optimal schedules for machines, minimizing downtime and maximizing throughput.
  • Predictive Maintenance: Implement predictive models to foresee and mitigate potential machine failures, ensuring continuous and efficient operations.
  • Time Management: Utilize deep learning models to enhance time management practices, helping businesses or individuals and team allocate resources more effectively and meet deadlines.
  • Scalability: Design solutions that are scalable and adaptable to different industrial environments and varying sizes of operations.
Responsibilities
As part of this open-source project, contributors are encouraged to:
  • Algorithm Development: Create and refine machine learning and deep learning algorithms tailored to scheduling and time management.
  • Data Collection and Preprocessing: Gather and preprocess data from various sources to train and validate models.
  • Model Training and Evaluation: Train models using the collected data and evaluate their performance to ensure accuracy and reliability.
  • Integration and Testing: Integrate developed models into real-world scheduling systems and conduct extensive testing to validate their effectiveness.
  • Documentation and Support: Maintain comprehensive documentation of the project, providing clear guidelines for usage and contribution. Assist users and other contributors through forums and issue tracking.
For inquiries, please contact me at chibangn1@gmail.com
About
A Python library for machine scheduling.
bancie.github.io/TiLearn/
Topics
deep-learningmachine-learningmachine-schedulingoperations-researchoptimizationtime-management
Resources
Readme
MIT license
Activity
Stars
13 stars
Watchers
3 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Bancie logo
homepagePyPI
Welcome to our open-source project focused on applying Machine Learning (ML) and Deep Learning (DL) techniques to Machine Scheduling and Time Management, often referred to as Optimal Processing. Our goal is to revolutionize the way tasks and processes are managed in various projects by leveraging advanced computational methods to optimize efficiency and productivity.
Installation
To install TiLearn using PyPI, run the following command:
pip install TiLearn

Then, in the TiLearn repository that you cloned, simply run:
pip install .

Documentation and Usage
For in-depth instructions on installation and building the documentation, see the TiLearn Documentation Guide and Tutorial.
Link: https://bancie.github.io/TiLearn/
Project Goals
  • Optimized Scheduling: Develop algorithms that can create optimal schedules for machines, minimizing downtime and maximizing throughput.
  • Predictive Maintenance: Implement predictive models to foresee and mitigate potential machine failures, ensuring continuous and efficient operations.
  • Time Management: Utilize deep learning models to enhance time management practices, helping businesses or individuals and team allocate resources more effectively and meet deadlines.
  • Scalability: Design solutions that are scalable and adaptable to different industrial environments and varying sizes of operations.
Responsibilities
As part of this open-source project, contributors are encouraged to:
  • Algorithm Development: Create and refine machine learning and deep learning algorithms tailored to scheduling and time management.
  • Data Collection and Preprocessing: Gather and preprocess data from various sources to train and validate models.
  • Model Training and Evaluation: Train models using the collected data and evaluate their performance to ensure accuracy and reliability.
  • Integration and Testing: Integrate developed models into real-world scheduling systems and conduct extensive testing to validate their effectiveness.
  • Documentation and Support: Maintain comprehensive documentation of the project, providing clear guidelines for usage and contribution. Assist users and other contributors through forums and issue tracking.
For inquiries, please contact me at chibangn1@gmail.com
About
A Python library for machine scheduling.
bancie.github.io/TiLearn/
Topics
deep-learningmachine-learningmachine-schedulingoperations-researchoptimizationtime-management
Resources
Readme
MIT license
Activity
Stars
13 stars
Watchers
3 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Bancie logo
homepagePyPI
Welcome to our open-source project focused on applying Machine Learning (ML) and Deep Learning (DL) techniques to Machine Scheduling and Time Management, often referred to as Optimal Processing. Our goal is to revolutionize the way tasks and processes are managed in various projects by leveraging advanced computational methods to optimize efficiency and productivity.
Installation
To install TiLearn using PyPI, run the following command:
pip install TiLearn

Then, in the TiLearn repository that you cloned, simply run:
pip install .

Documentation and Usage
For in-depth instructions on installation and building the documentation, see the TiLearn Documentation Guide and Tutorial.
Link: https://bancie.github.io/TiLearn/
Project Goals
  • Optimized Scheduling: Develop algorithms that can create optimal schedules for machines, minimizing downtime and maximizing throughput.
  • Predictive Maintenance: Implement predictive models to foresee and mitigate potential machine failures, ensuring continuous and efficient operations.
  • Time Management: Utilize deep learning models to enhance time management practices, helping businesses or individuals and team allocate resources more effectively and meet deadlines.
  • Scalability: Design solutions that are scalable and adaptable to different industrial environments and varying sizes of operations.
Responsibilities
As part of this open-source project, contributors are encouraged to:
  • Algorithm Development: Create and refine machine learning and deep learning algorithms tailored to scheduling and time management.
  • Data Collection and Preprocessing: Gather and preprocess data from various sources to train and validate models.
  • Model Training and Evaluation: Train models using the collected data and evaluate their performance to ensure accuracy and reliability.
  • Integration and Testing: Integrate developed models into real-world scheduling systems and conduct extensive testing to validate their effectiveness.
  • Documentation and Support: Maintain comprehensive documentation of the project, providing clear guidelines for usage and contribution. Assist users and other contributors through forums and issue tracking.
For inquiries, please contact me at chibangn1@gmail.com
About
A Python library for machine scheduling.
bancie.github.io/TiLearn/
Topics
deep-learningmachine-learningmachine-schedulingoperations-researchoptimizationtime-management
Resources
Readme
MIT license
Activity
Stars
13 stars
Watchers
3 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
Bancie logo
homepagePyPI
Welcome to our open-source project focused on applying Machine Learning (ML) and Deep Learning (DL) techniques to Machine Scheduling and Time Management, often referred to as Optimal Processing. Our goal is to revolutionize the way tasks and processes are managed in various projects by leveraging advanced computational methods to optimize efficiency and productivity.
Installation
To install TiLearn using PyPI, run the following command:
pip install TiLearn

Then, in the TiLearn repository that you cloned, simply run:
pip install .

Documentation and Usage
For in-depth instructions on installation and building the documentation, see the TiLearn Documentation Guide and Tutorial.
Link: https://bancie.github.io/TiLearn/
Project Goals
  • Optimized Scheduling: Develop algorithms that can create optimal schedules for machines, minimizing downtime and maximizing throughput.
  • Predictive Maintenance: Implement predictive models to foresee and mitigate potential machine failures, ensuring continuous and efficient operations.
  • Time Management: Utilize deep learning models to enhance time management practices, helping businesses or individuals and team allocate resources more effectively and meet deadlines.
  • Scalability: Design solutions that are scalable and adaptable to different industrial environments and varying sizes of operations.
Responsibilities
As part of this open-source project, contributors are encouraged to:
  • Algorithm Development: Create and refine machine learning and deep learning algorithms tailored to scheduling and time management.
  • Data Collection and Preprocessing: Gather and preprocess data from various sources to train and validate models.
  • Model Training and Evaluation: Train models using the collected data and evaluate their performance to ensure accuracy and reliability.
  • Integration and Testing: Integrate developed models into real-world scheduling systems and conduct extensive testing to validate their effectiveness.
  • Documentation and Support: Maintain comprehensive documentation of the project, providing clear guidelines for usage and contribution. Assist users and other contributors through forums and issue tracking.
For inquiries, please contact me at chibangn1@gmail.com
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A Python library for machine scheduling.
bancie.github.io/TiLearn/
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deep-learningmachine-learningmachine-schedulingoperations-researchoptimizationtime-management
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