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Numpy Deep Learning

Deep Learning Framework built entirely using numpy. The framework is built with the PyTorch design in mind and is meant to be used almost exactly like one would with with PyTorch. Implemented for the moment are fully connected layers, Tanh(), Sigmoid() and ReLU() activation layers, MSE and BCE loss and mini-batch Stochastic Gradient Descent (Gradient Descent and Stochastic Gradient Descent are available upon choice of mini-batch size).

Installation

Create a virtual python environment and source it.

virtualenvvenv-ppython3.6sourcevenv/bin/activate

Install required packages (NumPy)

pipinstall-rdev.requirements.txt

Run the example code.

pythonexample_usage.py

The example_usage.py is a usage example that generates synthetic binary data and then implements a multi-layer linear model to classify them. The framework however extends to any other usage and dataset.

Use as package

Install the package from the root directory with:

pipinstall-e .

You can then import it with :

importnumpy_dl

Usage examples are provided in the afore mentioned example_usage.py .

Next improvements

Command line functions, optimizers and multi-threading to improve speed.

About

Deep Learning Framework built entirely using numpy

Topics

deep-learningdeep-learning-frameworkminigradnumpypytorch

Resources

Readme
Activity

Stars

2 stars

Watchers

1 watching

Forks

0 forks
Report repository

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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" + '

Skip to content
BranchesTags
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Numpy Deep Learning
Deep Learning Framework built entirely using numpy. The framework is built with the PyTorch design in mind and is
meant to be used almost exactly like one would with with PyTorch. Implemented for the moment are fully connected
layers, Tanh(), Sigmoid() and ReLU() activation layers, MSE and BCE loss and mini-batch Stochastic Gradient Descent
(Gradient Descent and Stochastic Gradient Descent are available upon choice of mini-batch size).
Installation
Create a virtual python environment and source it.
virtualenvvenv-ppython3.6sourcevenv/bin/activate
Install required packages (NumPy)
pipinstall-rdev.requirements.txt
Run the example code.
pythonexample_usage.py
The example_usage.py is a usage example that generates synthetic binary data and then implements a multi-layer
linear model to classify them. The framework however extends to any other usage and dataset.
Use as package
Install the package from the root directory with:
pipinstall-e .
You can then import it with :
importnumpy_dl
Usage examples are provided in the afore mentioned example_usage.py .
Next improvements
Command line functions, optimizers and multi-threading to improve speed.
About
Deep Learning Framework built entirely using numpy
Topics
deep-learningdeep-learning-frameworkminigradnumpypytorch
Resources
Readme
Activity
Stars
2 stars
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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Numpy Deep Learning
Deep Learning Framework built entirely using numpy. The framework is built with the PyTorch design in mind and is
meant to be used almost exactly like one would with with PyTorch. Implemented for the moment are fully connected
layers, Tanh(), Sigmoid() and ReLU() activation layers, MSE and BCE loss and mini-batch Stochastic Gradient Descent
(Gradient Descent and Stochastic Gradient Descent are available upon choice of mini-batch size).
Installation
Create a virtual python environment and source it.
virtualenvvenv-ppython3.6sourcevenv/bin/activate
Install required packages (NumPy)
pipinstall-rdev.requirements.txt
Run the example code.
pythonexample_usage.py
The example_usage.py is a usage example that generates synthetic binary data and then implements a multi-layer
linear model to classify them. The framework however extends to any other usage and dataset.
Use as package
Install the package from the root directory with:
pipinstall-e .
You can then import it with :
importnumpy_dl
Usage examples are provided in the afore mentioned example_usage.py .
Next improvements
Command line functions, optimizers and multi-threading to improve speed.
About
Deep Learning Framework built entirely using numpy
Topics
deep-learningdeep-learning-frameworkminigradnumpypytorch
Resources
Readme
Activity
Stars
2 stars
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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Numpy Deep Learning
Deep Learning Framework built entirely using numpy. The framework is built with the PyTorch design in mind and is
meant to be used almost exactly like one would with with PyTorch. Implemented for the moment are fully connected
layers, Tanh(), Sigmoid() and ReLU() activation layers, MSE and BCE loss and mini-batch Stochastic Gradient Descent
(Gradient Descent and Stochastic Gradient Descent are available upon choice of mini-batch size).
Installation
Create a virtual python environment and source it.
virtualenvvenv-ppython3.6sourcevenv/bin/activate
Install required packages (NumPy)
pipinstall-rdev.requirements.txt
Run the example code.
pythonexample_usage.py
The example_usage.py is a usage example that generates synthetic binary data and then implements a multi-layer
linear model to classify them. The framework however extends to any other usage and dataset.
Use as package
Install the package from the root directory with:
pipinstall-e .
You can then import it with :
importnumpy_dl
Usage examples are provided in the afore mentioned example_usage.py .
Next improvements
Command line functions, optimizers and multi-threading to improve speed.
About
Deep Learning Framework built entirely using numpy
Topics
deep-learningdeep-learning-frameworkminigradnumpypytorch
Resources
Readme
Activity
Stars
2 stars
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
Repository files navigation
Numpy Deep Learning
Deep Learning Framework built entirely using numpy. The framework is built with the PyTorch design in mind and is
meant to be used almost exactly like one would with with PyTorch. Implemented for the moment are fully connected
layers, Tanh(), Sigmoid() and ReLU() activation layers, MSE and BCE loss and mini-batch Stochastic Gradient Descent
(Gradient Descent and Stochastic Gradient Descent are available upon choice of mini-batch size).
Installation
Create a virtual python environment and source it.
virtualenvvenv-ppython3.6sourcevenv/bin/activate
Install required packages (NumPy)
pipinstall-rdev.requirements.txt
Run the example code.
pythonexample_usage.py
The example_usage.py is a usage example that generates synthetic binary data and then implements a multi-layer
linear model to classify them. The framework however extends to any other usage and dataset.
Use as package
Install the package from the root directory with:
pipinstall-e .
You can then import it with :
importnumpy_dl
Usage examples are provided in the afore mentioned example_usage.py .
Next improvements
Command line functions, optimizers and multi-threading to improve speed.
About
Deep Learning Framework built entirely using numpy
Topics
deep-learningdeep-learning-frameworkminigradnumpypytorch
Resources
Readme
Activity
Stars
2 stars
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
Repository files navigation
Numpy Deep Learning
Deep Learning Framework built entirely using numpy. The framework is built with the PyTorch design in mind and is
meant to be used almost exactly like one would with with PyTorch. Implemented for the moment are fully connected
layers, Tanh(), Sigmoid() and ReLU() activation layers, MSE and BCE loss and mini-batch Stochastic Gradient Descent
(Gradient Descent and Stochastic Gradient Descent are available upon choice of mini-batch size).
Installation
Create a virtual python environment and source it.
virtualenvvenv-ppython3.6sourcevenv/bin/activate
Install required packages (NumPy)
pipinstall-rdev.requirements.txt
Run the example code.
pythonexample_usage.py
The example_usage.py is a usage example that generates synthetic binary data and then implements a multi-layer
linear model to classify them. The framework however extends to any other usage and dataset.
Use as package
Install the package from the root directory with:
pipinstall-e .
You can then import it with :
importnumpy_dl
Usage examples are provided in the afore mentioned example_usage.py .
Next improvements
Command line functions, optimizers and multi-threading to improve speed.
About
Deep Learning Framework built entirely using numpy
Topics
deep-learningdeep-learning-frameworkminigradnumpypytorch
Resources
Readme
Activity
Stars
2 stars
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
BranchesTags
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Numpy Deep Learning
Deep Learning Framework built entirely using numpy. The framework is built with the PyTorch design in mind and is
meant to be used almost exactly like one would with with PyTorch. Implemented for the moment are fully connected
layers, Tanh(), Sigmoid() and ReLU() activation layers, MSE and BCE loss and mini-batch Stochastic Gradient Descent
(Gradient Descent and Stochastic Gradient Descent are available upon choice of mini-batch size).
Installation
Create a virtual python environment and source it.
virtualenvvenv-ppython3.6sourcevenv/bin/activate
Install required packages (NumPy)
pipinstall-rdev.requirements.txt
Run the example code.
pythonexample_usage.py
The example_usage.py is a usage example that generates synthetic binary data and then implements a multi-layer
linear model to classify them. The framework however extends to any other usage and dataset.
Use as package
Install the package from the root directory with:
pipinstall-e .
You can then import it with :
importnumpy_dl
Usage examples are provided in the afore mentioned example_usage.py .
Next improvements
Command line functions, optimizers and multi-threading to improve speed.
About
Deep Learning Framework built entirely using numpy
Topics
deep-learningdeep-learning-frameworkminigradnumpypytorch
Resources
Readme
Activity
Stars
2 stars
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); }
})();
})();

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Numpy Deep Learning
Deep Learning Framework built entirely using numpy. The framework is built with the PyTorch design in mind and is
meant to be used almost exactly like one would with with PyTorch. Implemented for the moment are fully connected
layers, Tanh(), Sigmoid() and ReLU() activation layers, MSE and BCE loss and mini-batch Stochastic Gradient Descent
(Gradient Descent and Stochastic Gradient Descent are available upon choice of mini-batch size).
Installation
Create a virtual python environment and source it.
virtualenvvenv-ppython3.6sourcevenv/bin/activate
Install required packages (NumPy)
pipinstall-rdev.requirements.txt
Run the example code.
pythonexample_usage.py
The example_usage.py is a usage example that generates synthetic binary data and then implements a multi-layer
linear model to classify them. The framework however extends to any other usage and dataset.
Use as package
Install the package from the root directory with:
pipinstall-e .
You can then import it with :
importnumpy_dl
Usage examples are provided in the afore mentioned example_usage.py .
Next improvements
Command line functions, optimizers and multi-threading to improve speed.
About
Deep Learning Framework built entirely using numpy
Topics
deep-learningdeep-learning-frameworkminigradnumpypytorch
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages