BranchesTags

Repository files navigation

Libraryless-ML

Use nbviewer (https://nbviewer.org/) to view the truncated outputs!

Trying to code out some architectures without using most of the libraries - only using the 'time' module!

Hence, most of these codes wouldn't be optimal due to not using vectorization and optimization concepts, but these are just for building them on my own!

Simple_MLP_Forward_Pass_Example : This contains a simple example of just a forward pass with 2 layers, and only one training example, mainly to affirm the process.

Simple_MLP_Complete_Example : Extension of the previous notebook, here one step of complete backpropogation is done. Loss used is Binary Cross Entropy (BCE) and Activation function used is sigmoid. Further plans are to make a complete architecture for 'n' training examples, and giving the option to the user to decide the number of hidden layers, and neurons in each layer. Batch Gradient Descent is used since examples are less.

Work of iris Dataset:

  1. The first two notebooks contain the code for forward pass and one round of backward propogation on the last layer of the network.

  2. The complete notebook codes out the entire process. I have set my own architecture, which can be changed in the notebook, and different hyperparamters as well. I have run the model on the dataset, for 1000 timesteps, and ended up with a loss of 50.1051 at the final timestep. Note that no optimization techniques were used, and the architecture I used was only 5 layers, with neurons ranging from 3-5. Hence the architecture used was very small.

About

Trying to code out some architectures without using any libraries except the "time" module! There is NO usage of numpy and pandas!

Resources

Readme
Activity

Stars

1 star

Watchers

1 watching

Forks

0 forks
Report repository

Releases

Packages

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
Libraryless-ML
Use nbviewer (https://nbviewer.org/) to view the truncated outputs!
Trying to code out some architectures without using most of the libraries - only using the 'time' module!
Hence, most of these codes wouldn't be optimal due to not using vectorization and optimization concepts, but these are just for building them on my own!
Simple_MLP_Forward_Pass_Example :
This contains a simple example of just a forward pass with 2 layers, and only one training example, mainly to affirm the process.
Simple_MLP_Complete_Example :
Extension of the previous notebook, here one step of complete backpropogation is done. Loss used is Binary Cross Entropy (BCE) and Activation function used is sigmoid.
Further plans are to make a complete architecture for 'n' training examples, and giving the option to the user to decide the number of hidden layers, and neurons in each layer. Batch Gradient Descent is used since examples are less.
Work of iris Dataset:
  1. The first two notebooks contain the code for forward pass and one round of backward propogation on the last layer of the network.
  2. The complete notebook codes out the entire process. I have set my own architecture, which can be changed in the notebook, and different hyperparamters as well.
I have run the model on the dataset, for 1000 timesteps, and ended up with a loss of 50.1051 at the final timestep. Note that no optimization techniques were used, and the architecture I used was only 5 layers, with neurons ranging from 3-5. Hence the architecture used was very small.
About
Trying to code out some architectures without using any libraries except the "time" module! There is NO usage of numpy and pandas!
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
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
Libraryless-ML
Use nbviewer (https://nbviewer.org/) to view the truncated outputs!
Trying to code out some architectures without using most of the libraries - only using the 'time' module!
Hence, most of these codes wouldn't be optimal due to not using vectorization and optimization concepts, but these are just for building them on my own!
Simple_MLP_Forward_Pass_Example :
This contains a simple example of just a forward pass with 2 layers, and only one training example, mainly to affirm the process.
Simple_MLP_Complete_Example :
Extension of the previous notebook, here one step of complete backpropogation is done. Loss used is Binary Cross Entropy (BCE) and Activation function used is sigmoid.
Further plans are to make a complete architecture for 'n' training examples, and giving the option to the user to decide the number of hidden layers, and neurons in each layer. Batch Gradient Descent is used since examples are less.
Work of iris Dataset:
  1. The first two notebooks contain the code for forward pass and one round of backward propogation on the last layer of the network.
  2. The complete notebook codes out the entire process. I have set my own architecture, which can be changed in the notebook, and different hyperparamters as well.
I have run the model on the dataset, for 1000 timesteps, and ended up with a loss of 50.1051 at the final timestep. Note that no optimization techniques were used, and the architecture I used was only 5 layers, with neurons ranging from 3-5. Hence the architecture used was very small.
About
Trying to code out some architectures without using any libraries except the "time" module! There is NO usage of numpy and pandas!
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
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
Libraryless-ML
Use nbviewer (https://nbviewer.org/) to view the truncated outputs!
Trying to code out some architectures without using most of the libraries - only using the 'time' module!
Hence, most of these codes wouldn't be optimal due to not using vectorization and optimization concepts, but these are just for building them on my own!
Simple_MLP_Forward_Pass_Example :
This contains a simple example of just a forward pass with 2 layers, and only one training example, mainly to affirm the process.
Simple_MLP_Complete_Example :
Extension of the previous notebook, here one step of complete backpropogation is done. Loss used is Binary Cross Entropy (BCE) and Activation function used is sigmoid.
Further plans are to make a complete architecture for 'n' training examples, and giving the option to the user to decide the number of hidden layers, and neurons in each layer. Batch Gradient Descent is used since examples are less.
Work of iris Dataset:
  1. The first two notebooks contain the code for forward pass and one round of backward propogation on the last layer of the network.
  2. The complete notebook codes out the entire process. I have set my own architecture, which can be changed in the notebook, and different hyperparamters as well.
I have run the model on the dataset, for 1000 timesteps, and ended up with a loss of 50.1051 at the final timestep. Note that no optimization techniques were used, and the architecture I used was only 5 layers, with neurons ranging from 3-5. Hence the architecture used was very small.
About
Trying to code out some architectures without using any libraries except the "time" module! There is NO usage of numpy and pandas!
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
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
Libraryless-ML
Use nbviewer (https://nbviewer.org/) to view the truncated outputs!
Trying to code out some architectures without using most of the libraries - only using the 'time' module!
Hence, most of these codes wouldn't be optimal due to not using vectorization and optimization concepts, but these are just for building them on my own!
Simple_MLP_Forward_Pass_Example :
This contains a simple example of just a forward pass with 2 layers, and only one training example, mainly to affirm the process.
Simple_MLP_Complete_Example :
Extension of the previous notebook, here one step of complete backpropogation is done. Loss used is Binary Cross Entropy (BCE) and Activation function used is sigmoid.
Further plans are to make a complete architecture for 'n' training examples, and giving the option to the user to decide the number of hidden layers, and neurons in each layer. Batch Gradient Descent is used since examples are less.
Work of iris Dataset:
  1. The first two notebooks contain the code for forward pass and one round of backward propogation on the last layer of the network.
  2. The complete notebook codes out the entire process. I have set my own architecture, which can be changed in the notebook, and different hyperparamters as well.
I have run the model on the dataset, for 1000 timesteps, and ended up with a loss of 50.1051 at the final timestep. Note that no optimization techniques were used, and the architecture I used was only 5 layers, with neurons ranging from 3-5. Hence the architecture used was very small.
About
Trying to code out some architectures without using any libraries except the "time" module! There is NO usage of numpy and pandas!
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
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
Libraryless-ML
Use nbviewer (https://nbviewer.org/) to view the truncated outputs!
Trying to code out some architectures without using most of the libraries - only using the 'time' module!
Hence, most of these codes wouldn't be optimal due to not using vectorization and optimization concepts, but these are just for building them on my own!
Simple_MLP_Forward_Pass_Example :
This contains a simple example of just a forward pass with 2 layers, and only one training example, mainly to affirm the process.
Simple_MLP_Complete_Example :
Extension of the previous notebook, here one step of complete backpropogation is done. Loss used is Binary Cross Entropy (BCE) and Activation function used is sigmoid.
Further plans are to make a complete architecture for 'n' training examples, and giving the option to the user to decide the number of hidden layers, and neurons in each layer. Batch Gradient Descent is used since examples are less.
Work of iris Dataset:
  1. The first two notebooks contain the code for forward pass and one round of backward propogation on the last layer of the network.
  2. The complete notebook codes out the entire process. I have set my own architecture, which can be changed in the notebook, and different hyperparamters as well.
I have run the model on the dataset, for 1000 timesteps, and ended up with a loss of 50.1051 at the final timestep. Note that no optimization techniques were used, and the architecture I used was only 5 layers, with neurons ranging from 3-5. Hence the architecture used was very small.
About
Trying to code out some architectures without using any libraries except the "time" module! There is NO usage of numpy and pandas!
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
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
Libraryless-ML
Use nbviewer (https://nbviewer.org/) to view the truncated outputs!
Trying to code out some architectures without using most of the libraries - only using the 'time' module!
Hence, most of these codes wouldn't be optimal due to not using vectorization and optimization concepts, but these are just for building them on my own!
Simple_MLP_Forward_Pass_Example :
This contains a simple example of just a forward pass with 2 layers, and only one training example, mainly to affirm the process.
Simple_MLP_Complete_Example :
Extension of the previous notebook, here one step of complete backpropogation is done. Loss used is Binary Cross Entropy (BCE) and Activation function used is sigmoid.
Further plans are to make a complete architecture for 'n' training examples, and giving the option to the user to decide the number of hidden layers, and neurons in each layer. Batch Gradient Descent is used since examples are less.
Work of iris Dataset:
  1. The first two notebooks contain the code for forward pass and one round of backward propogation on the last layer of the network.
  2. The complete notebook codes out the entire process. I have set my own architecture, which can be changed in the notebook, and different hyperparamters as well.
I have run the model on the dataset, for 1000 timesteps, and ended up with a loss of 50.1051 at the final timestep. Note that no optimization techniques were used, and the architecture I used was only 5 layers, with neurons ranging from 3-5. Hence the architecture used was very small.
About
Trying to code out some architectures without using any libraries except the "time" module! There is NO usage of numpy and pandas!
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
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
Libraryless-ML
Use nbviewer (https://nbviewer.org/) to view the truncated outputs!
Trying to code out some architectures without using most of the libraries - only using the 'time' module!
Hence, most of these codes wouldn't be optimal due to not using vectorization and optimization concepts, but these are just for building them on my own!
Simple_MLP_Forward_Pass_Example :
This contains a simple example of just a forward pass with 2 layers, and only one training example, mainly to affirm the process.
Simple_MLP_Complete_Example :
Extension of the previous notebook, here one step of complete backpropogation is done. Loss used is Binary Cross Entropy (BCE) and Activation function used is sigmoid.
Further plans are to make a complete architecture for 'n' training examples, and giving the option to the user to decide the number of hidden layers, and neurons in each layer. Batch Gradient Descent is used since examples are less.
Work of iris Dataset:
  1. The first two notebooks contain the code for forward pass and one round of backward propogation on the last layer of the network.
  2. The complete notebook codes out the entire process. I have set my own architecture, which can be changed in the notebook, and different hyperparamters as well.
I have run the model on the dataset, for 1000 timesteps, and ended up with a loss of 50.1051 at the final timestep. Note that no optimization techniques were used, and the architecture I used was only 5 layers, with neurons ranging from 3-5. Hence the architecture used was very small.
About
Trying to code out some architectures without using any libraries except the "time" module! There is NO usage of numpy and pandas!
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages