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Image Classifier - Machine Learning Engineering Project

#Overview I built an Image Classifier that attained 96% accuracy by implementing PyTorch fundamental modules from scratch listed below. After building the fundamentals In addition, I enhanced 25% computation efficiency through parallel programming on GPU with NumbaJit and CUDA.

  1. Auto Differentiation
  2. Back-Propagation
  3. Tensor Broadcasting
  4. Derivative and Scalar programming.

NLP and CV training scripts in project/run_sentiment.py and project/run_mnist_multiclass.py. This script has the same basic training setup as :doc:module3, but now adapted to sentiment and image classification. You need to implement Conv1D, Conv2D, and Network for both files. Use Streamlit for visualization.

Visualization on Different Data Set

Xor Data Set: Xor Data Set: 7 hidden layers

Simple Data Set: Simple Data Set: 4 hidden layers

Split Data Set: Split Data Set: 7 hidden layers

Diag Data Set: Diag Data Set: 7 hidden layers

My guidance:

Sentiment and NMist Result

nmist result
sentiment result

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Machine Learning Engineering Project - Image Classifier

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, '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" + '

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Image Classifier - Machine Learning Engineering Project
#Overview
I built an Image Classifier that attained 96% accuracy by implementing PyTorch fundamental modules from scratch listed below. After building the fundamentals In addition, I enhanced 25% computation efficiency through parallel programming on GPU with NumbaJit and CUDA.
  1. Auto Differentiation
  2. Back-Propagation
  3. Tensor Broadcasting
  4. Derivative and Scalar programming.
NLP and CV training scripts in project/run_sentiment.py and project/run_mnist_multiclass.py. This script has the same basic training setup as :doc:module3, but now adapted to sentiment and image classification. You need to implement Conv1D, Conv2D, and Network for both files. Use Streamlit for visualization.
Visualization on Different Data Set
Xor Data Set:
Xor Data Set: 7 hidden layers 
Simple Data Set:
Simple Data Set: 4 hidden layers
Split Data Set:
Split Data Set: 7 hidden layers
Diag Data Set:
Diag Data Set: 7 hidden layers
My guidance:
Sentiment and NMist Result
nmist result
sentiment result
About
Machine Learning Engineering Project - Image Classifier
Resources
Readme
Activity
Stars
1 star
Watchers
1 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('^' + ".*" + '

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Image Classifier - Machine Learning Engineering Project
#Overview
I built an Image Classifier that attained 96% accuracy by implementing PyTorch fundamental modules from scratch listed below. After building the fundamentals In addition, I enhanced 25% computation efficiency through parallel programming on GPU with NumbaJit and CUDA.
  1. Auto Differentiation
  2. Back-Propagation
  3. Tensor Broadcasting
  4. Derivative and Scalar programming.
NLP and CV training scripts in project/run_sentiment.py and project/run_mnist_multiclass.py. This script has the same basic training setup as :doc:module3, but now adapted to sentiment and image classification. You need to implement Conv1D, Conv2D, and Network for both files. Use Streamlit for visualization.
Visualization on Different Data Set
Xor Data Set:
Xor Data Set: 7 hidden layers 
Simple Data Set:
Simple Data Set: 4 hidden layers
Split Data Set:
Split Data Set: 7 hidden layers
Diag Data Set:
Diag Data Set: 7 hidden layers
My guidance:
Sentiment and NMist Result
nmist result
sentiment result
About
Machine Learning Engineering Project - Image Classifier
Resources
Readme
Activity
Stars
1 star
Watchers
1 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('^' + ".*" + '

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Image Classifier - Machine Learning Engineering Project
#Overview
I built an Image Classifier that attained 96% accuracy by implementing PyTorch fundamental modules from scratch listed below. After building the fundamentals In addition, I enhanced 25% computation efficiency through parallel programming on GPU with NumbaJit and CUDA.
  1. Auto Differentiation
  2. Back-Propagation
  3. Tensor Broadcasting
  4. Derivative and Scalar programming.
NLP and CV training scripts in project/run_sentiment.py and project/run_mnist_multiclass.py. This script has the same basic training setup as :doc:module3, but now adapted to sentiment and image classification. You need to implement Conv1D, Conv2D, and Network for both files. Use Streamlit for visualization.
Visualization on Different Data Set
Xor Data Set:
Xor Data Set: 7 hidden layers 
Simple Data Set:
Simple Data Set: 4 hidden layers
Split Data Set:
Split Data Set: 7 hidden layers
Diag Data Set:
Diag Data Set: 7 hidden layers
My guidance:
Sentiment and NMist Result
nmist result
sentiment result
About
Machine Learning Engineering Project - Image Classifier
Resources
Readme
Activity
Stars
1 star
Watchers
1 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
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Image Classifier - Machine Learning Engineering Project
#Overview
I built an Image Classifier that attained 96% accuracy by implementing PyTorch fundamental modules from scratch listed below. After building the fundamentals In addition, I enhanced 25% computation efficiency through parallel programming on GPU with NumbaJit and CUDA.
  1. Auto Differentiation
  2. Back-Propagation
  3. Tensor Broadcasting
  4. Derivative and Scalar programming.
NLP and CV training scripts in project/run_sentiment.py and project/run_mnist_multiclass.py. This script has the same basic training setup as :doc:module3, but now adapted to sentiment and image classification. You need to implement Conv1D, Conv2D, and Network for both files. Use Streamlit for visualization.
Visualization on Different Data Set
Xor Data Set:
Xor Data Set: 7 hidden layers 
Simple Data Set:
Simple Data Set: 4 hidden layers
Split Data Set:
Split Data Set: 7 hidden layers
Diag Data Set:
Diag Data Set: 7 hidden layers
My guidance:
Sentiment and NMist Result
nmist result
sentiment result
About
Machine Learning Engineering Project - Image Classifier
Resources
Readme
Activity
Stars
1 star
Watchers
1 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
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Image Classifier - Machine Learning Engineering Project
#Overview
I built an Image Classifier that attained 96% accuracy by implementing PyTorch fundamental modules from scratch listed below. After building the fundamentals In addition, I enhanced 25% computation efficiency through parallel programming on GPU with NumbaJit and CUDA.
  1. Auto Differentiation
  2. Back-Propagation
  3. Tensor Broadcasting
  4. Derivative and Scalar programming.
NLP and CV training scripts in project/run_sentiment.py and project/run_mnist_multiclass.py. This script has the same basic training setup as :doc:module3, but now adapted to sentiment and image classification. You need to implement Conv1D, Conv2D, and Network for both files. Use Streamlit for visualization.
Visualization on Different Data Set
Xor Data Set:
Xor Data Set: 7 hidden layers 
Simple Data Set:
Simple Data Set: 4 hidden layers
Split Data Set:
Split Data Set: 7 hidden layers
Diag Data Set:
Diag Data Set: 7 hidden layers
My guidance:
Sentiment and NMist Result
nmist result
sentiment result
About
Machine Learning Engineering Project - Image Classifier
Resources
Readme
Activity
Stars
1 star
Watchers
1 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
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Image Classifier - Machine Learning Engineering Project
#Overview
I built an Image Classifier that attained 96% accuracy by implementing PyTorch fundamental modules from scratch listed below. After building the fundamentals In addition, I enhanced 25% computation efficiency through parallel programming on GPU with NumbaJit and CUDA.
  1. Auto Differentiation
  2. Back-Propagation
  3. Tensor Broadcasting
  4. Derivative and Scalar programming.
NLP and CV training scripts in project/run_sentiment.py and project/run_mnist_multiclass.py. This script has the same basic training setup as :doc:module3, but now adapted to sentiment and image classification. You need to implement Conv1D, Conv2D, and Network for both files. Use Streamlit for visualization.
Visualization on Different Data Set
Xor Data Set:
Xor Data Set: 7 hidden layers 
Simple Data Set:
Simple Data Set: 4 hidden layers
Split Data Set:
Split Data Set: 7 hidden layers
Diag Data Set:
Diag Data Set: 7 hidden layers
My guidance:
Sentiment and NMist Result
nmist result
sentiment result
About
Machine Learning Engineering Project - Image Classifier
Resources
Readme
Activity
Stars
1 star
Watchers
1 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
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Image Classifier - Machine Learning Engineering Project
#Overview
I built an Image Classifier that attained 96% accuracy by implementing PyTorch fundamental modules from scratch listed below. After building the fundamentals In addition, I enhanced 25% computation efficiency through parallel programming on GPU with NumbaJit and CUDA.
  1. Auto Differentiation
  2. Back-Propagation
  3. Tensor Broadcasting
  4. Derivative and Scalar programming.
NLP and CV training scripts in project/run_sentiment.py and project/run_mnist_multiclass.py. This script has the same basic training setup as :doc:module3, but now adapted to sentiment and image classification. You need to implement Conv1D, Conv2D, and Network for both files. Use Streamlit for visualization.
Visualization on Different Data Set
Xor Data Set:
Xor Data Set: 7 hidden layers 
Simple Data Set:
Simple Data Set: 4 hidden layers
Split Data Set:
Split Data Set: 7 hidden layers
Diag Data Set:
Diag Data Set: 7 hidden layers
My guidance:
Sentiment and NMist Result
nmist result
sentiment result
About
Machine Learning Engineering Project - Image Classifier
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages