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Neural Network From Scratch

This repository contains a Python library for building simple Deep Neural Networks from scratch, using only vectorized operations with NumPy.

The goal of this project is to develop further understanding in the inner workings of neural networks and provide a foundation for building a Production-ready Python library.

Features

The current version of the library includes the following features:

  • A customizable neural network architecture with support for multiple layers and activation functions
  • Stochastic gradient descent optimization algorithm with support for different loss functions [comment]<> : (
  • A suite of evaluation metrics, such as accuracy and mean squared error, for measuring model performance
  • A suite of utilities for data preprocessing, including normalization and one-hot encoding )

Getting started

To use this library, you need to install it on your system. You can install it by running

pip install --no-cache-dir .

Once you have it installed, you can import the library into your Python code using:

fromneural_networkimportDeepNN

From there, you can create an instance of the Deep Neural Network architecture as a python class and customize it to fit your needs. For example, to create a neural network with one hidden layer and a sigmoid activation function, you can use the following code:

nn=DeepNN(layers_dims=[input_size, hidden_size, output_size], activations=["sigmoid", "sigmoid"])

Things to try

This library is a work in progress, and there are several areas where you can contribute and improve its functionality. Here are some ideas:

  • Optimizing performance: While the library is currently optimized for efficiency using NumPy, you can try using a GPU-accelerated computing library like CuPy and CuPyx to further improve its performance.
  • Developing new architectures of NNs: The current library supports only a simple feedforward neural network architecture, but you can explore other architectures, such as convolutional neural networks or recurrent neural networks, and implement them using the existing framework.
  • Adding more evaluation metrics: The library currently supports a limited set of evaluation metrics. You can add more metrics, such as precision and recall, to provide a more comprehensive view of model performance.

Contributing

If you are interested in contributing to this project, please check out the contribution guidelines for more information on how to get started.

License

This project is licensed under the MIT License.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Neural Network From Scratch
This repository contains a Python library for building simple Deep Neural Networks from scratch, using only vectorized operations with NumPy.
The goal of this project is to develop further understanding in the inner workings of neural networks and provide a foundation for building a Production-ready Python library.
Features
The current version of the library includes the following features:
  • A customizable neural network architecture with support for multiple layers and activation functions
  • Stochastic gradient descent optimization algorithm with support for different loss functions
[comment]<> : (
  • A suite of evaluation metrics, such as accuracy and mean squared error, for measuring model performance
  • A suite of utilities for data preprocessing, including normalization and one-hot encoding
)
Getting started
To use this library, you need to install it on your system. You can install it by running
pip install --no-cache-dir .
Once you have it installed, you can import the library into your Python code using:
fromneural_networkimportDeepNN
From there, you can create an instance of the Deep Neural Network architecture as a python class and customize it to fit your needs. For example, to create a neural network with one hidden layer and a sigmoid activation function, you can use the following code:
nn=DeepNN(layers_dims=[input_size, hidden_size, output_size], activations=["sigmoid", "sigmoid"])
Things to try
This library is a work in progress, and there are several areas where you can contribute and improve its functionality. Here are some ideas:
  • Optimizing performance: While the library is currently optimized for efficiency using NumPy, you can try using a GPU-accelerated computing library like CuPy and CuPyx to further improve its performance.
  • Developing new architectures of NNs: The current library supports only a simple feedforward neural network architecture, but you can explore other architectures, such as convolutional neural networks or recurrent neural networks, and implement them using the existing framework.
  • Adding more evaluation metrics: The library currently supports a limited set of evaluation metrics. You can add more metrics, such as precision and recall, to provide a more comprehensive view of model performance.
Contributing
If you are interested in contributing to this project, please check out the contribution guidelines for more information on how to get started.
License
This project is licensed under the MIT License.
About
NNs from scratch
Resources
Readme
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Contributing
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, '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");
}
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Neural Network From Scratch
This repository contains a Python library for building simple Deep Neural Networks from scratch, using only vectorized operations with NumPy.
The goal of this project is to develop further understanding in the inner workings of neural networks and provide a foundation for building a Production-ready Python library.
Features
The current version of the library includes the following features:
  • A customizable neural network architecture with support for multiple layers and activation functions
  • Stochastic gradient descent optimization algorithm with support for different loss functions
[comment]<> : (
  • A suite of evaluation metrics, such as accuracy and mean squared error, for measuring model performance
  • A suite of utilities for data preprocessing, including normalization and one-hot encoding
)
Getting started
To use this library, you need to install it on your system. You can install it by running
pip install --no-cache-dir .
Once you have it installed, you can import the library into your Python code using:
fromneural_networkimportDeepNN
From there, you can create an instance of the Deep Neural Network architecture as a python class and customize it to fit your needs. For example, to create a neural network with one hidden layer and a sigmoid activation function, you can use the following code:
nn=DeepNN(layers_dims=[input_size, hidden_size, output_size], activations=["sigmoid", "sigmoid"])
Things to try
This library is a work in progress, and there are several areas where you can contribute and improve its functionality. Here are some ideas:
  • Optimizing performance: While the library is currently optimized for efficiency using NumPy, you can try using a GPU-accelerated computing library like CuPy and CuPyx to further improve its performance.
  • Developing new architectures of NNs: The current library supports only a simple feedforward neural network architecture, but you can explore other architectures, such as convolutional neural networks or recurrent neural networks, and implement them using the existing framework.
  • Adding more evaluation metrics: The library currently supports a limited set of evaluation metrics. You can add more metrics, such as precision and recall, to provide a more comprehensive view of model performance.
Contributing
If you are interested in contributing to this project, please check out the contribution guidelines for more information on how to get started.
License
This project is licensed under the MIT License.
About
NNs from scratch
Resources
Readme
Contributing
Contributing
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Stars
2 stars
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1 watching
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1 fork
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, '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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Neural Network From Scratch
This repository contains a Python library for building simple Deep Neural Networks from scratch, using only vectorized operations with NumPy.
The goal of this project is to develop further understanding in the inner workings of neural networks and provide a foundation for building a Production-ready Python library.
Features
The current version of the library includes the following features:
  • A customizable neural network architecture with support for multiple layers and activation functions
  • Stochastic gradient descent optimization algorithm with support for different loss functions
[comment]<> : (
  • A suite of evaluation metrics, such as accuracy and mean squared error, for measuring model performance
  • A suite of utilities for data preprocessing, including normalization and one-hot encoding
)
Getting started
To use this library, you need to install it on your system. You can install it by running
pip install --no-cache-dir .
Once you have it installed, you can import the library into your Python code using:
fromneural_networkimportDeepNN
From there, you can create an instance of the Deep Neural Network architecture as a python class and customize it to fit your needs. For example, to create a neural network with one hidden layer and a sigmoid activation function, you can use the following code:
nn=DeepNN(layers_dims=[input_size, hidden_size, output_size], activations=["sigmoid", "sigmoid"])
Things to try
This library is a work in progress, and there are several areas where you can contribute and improve its functionality. Here are some ideas:
  • Optimizing performance: While the library is currently optimized for efficiency using NumPy, you can try using a GPU-accelerated computing library like CuPy and CuPyx to further improve its performance.
  • Developing new architectures of NNs: The current library supports only a simple feedforward neural network architecture, but you can explore other architectures, such as convolutional neural networks or recurrent neural networks, and implement them using the existing framework.
  • Adding more evaluation metrics: The library currently supports a limited set of evaluation metrics. You can add more metrics, such as precision and recall, to provide a more comprehensive view of model performance.
Contributing
If you are interested in contributing to this project, please check out the contribution guidelines for more information on how to get started.
License
This project is licensed under the MIT License.
About
NNs from scratch
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Readme
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, '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" + '

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Neural Network From Scratch
This repository contains a Python library for building simple Deep Neural Networks from scratch, using only vectorized operations with NumPy.
The goal of this project is to develop further understanding in the inner workings of neural networks and provide a foundation for building a Production-ready Python library.
Features
The current version of the library includes the following features:
  • A customizable neural network architecture with support for multiple layers and activation functions
  • Stochastic gradient descent optimization algorithm with support for different loss functions
[comment]<> : (
  • A suite of evaluation metrics, such as accuracy and mean squared error, for measuring model performance
  • A suite of utilities for data preprocessing, including normalization and one-hot encoding
)
Getting started
To use this library, you need to install it on your system. You can install it by running
pip install --no-cache-dir .
Once you have it installed, you can import the library into your Python code using:
fromneural_networkimportDeepNN
From there, you can create an instance of the Deep Neural Network architecture as a python class and customize it to fit your needs. For example, to create a neural network with one hidden layer and a sigmoid activation function, you can use the following code:
nn=DeepNN(layers_dims=[input_size, hidden_size, output_size], activations=["sigmoid", "sigmoid"])
Things to try
This library is a work in progress, and there are several areas where you can contribute and improve its functionality. Here are some ideas:
  • Optimizing performance: While the library is currently optimized for efficiency using NumPy, you can try using a GPU-accelerated computing library like CuPy and CuPyx to further improve its performance.
  • Developing new architectures of NNs: The current library supports only a simple feedforward neural network architecture, but you can explore other architectures, such as convolutional neural networks or recurrent neural networks, and implement them using the existing framework.
  • Adding more evaluation metrics: The library currently supports a limited set of evaluation metrics. You can add more metrics, such as precision and recall, to provide a more comprehensive view of model performance.
Contributing
If you are interested in contributing to this project, please check out the contribution guidelines for more information on how to get started.
License
This project is licensed under the MIT License.
About
NNs from scratch
Resources
Readme
Contributing
Contributing
Activity
Stars
2 stars
Watchers
1 watching
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1 fork
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, '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('^' + ".*" + '

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Neural Network From Scratch
This repository contains a Python library for building simple Deep Neural Networks from scratch, using only vectorized operations with NumPy.
The goal of this project is to develop further understanding in the inner workings of neural networks and provide a foundation for building a Production-ready Python library.
Features
The current version of the library includes the following features:
  • A customizable neural network architecture with support for multiple layers and activation functions
  • Stochastic gradient descent optimization algorithm with support for different loss functions
[comment]<> : (
  • A suite of evaluation metrics, such as accuracy and mean squared error, for measuring model performance
  • A suite of utilities for data preprocessing, including normalization and one-hot encoding
)
Getting started
To use this library, you need to install it on your system. You can install it by running
pip install --no-cache-dir .
Once you have it installed, you can import the library into your Python code using:
fromneural_networkimportDeepNN
From there, you can create an instance of the Deep Neural Network architecture as a python class and customize it to fit your needs. For example, to create a neural network with one hidden layer and a sigmoid activation function, you can use the following code:
nn=DeepNN(layers_dims=[input_size, hidden_size, output_size], activations=["sigmoid", "sigmoid"])
Things to try
This library is a work in progress, and there are several areas where you can contribute and improve its functionality. Here are some ideas:
  • Optimizing performance: While the library is currently optimized for efficiency using NumPy, you can try using a GPU-accelerated computing library like CuPy and CuPyx to further improve its performance.
  • Developing new architectures of NNs: The current library supports only a simple feedforward neural network architecture, but you can explore other architectures, such as convolutional neural networks or recurrent neural networks, and implement them using the existing framework.
  • Adding more evaluation metrics: The library currently supports a limited set of evaluation metrics. You can add more metrics, such as precision and recall, to provide a more comprehensive view of model performance.
Contributing
If you are interested in contributing to this project, please check out the contribution guidelines for more information on how to get started.
License
This project is licensed under the MIT License.
About
NNs from scratch
Resources
Readme
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Contributing
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, '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('^' + ".*" + '

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Neural Network From Scratch
This repository contains a Python library for building simple Deep Neural Networks from scratch, using only vectorized operations with NumPy.
The goal of this project is to develop further understanding in the inner workings of neural networks and provide a foundation for building a Production-ready Python library.
Features
The current version of the library includes the following features:
  • A customizable neural network architecture with support for multiple layers and activation functions
  • Stochastic gradient descent optimization algorithm with support for different loss functions
[comment]<> : (
  • A suite of evaluation metrics, such as accuracy and mean squared error, for measuring model performance
  • A suite of utilities for data preprocessing, including normalization and one-hot encoding
)
Getting started
To use this library, you need to install it on your system. You can install it by running
pip install --no-cache-dir .
Once you have it installed, you can import the library into your Python code using:
fromneural_networkimportDeepNN
From there, you can create an instance of the Deep Neural Network architecture as a python class and customize it to fit your needs. For example, to create a neural network with one hidden layer and a sigmoid activation function, you can use the following code:
nn=DeepNN(layers_dims=[input_size, hidden_size, output_size], activations=["sigmoid", "sigmoid"])
Things to try
This library is a work in progress, and there are several areas where you can contribute and improve its functionality. Here are some ideas:
  • Optimizing performance: While the library is currently optimized for efficiency using NumPy, you can try using a GPU-accelerated computing library like CuPy and CuPyx to further improve its performance.
  • Developing new architectures of NNs: The current library supports only a simple feedforward neural network architecture, but you can explore other architectures, such as convolutional neural networks or recurrent neural networks, and implement them using the existing framework.
  • Adding more evaluation metrics: The library currently supports a limited set of evaluation metrics. You can add more metrics, such as precision and recall, to provide a more comprehensive view of model performance.
Contributing
If you are interested in contributing to this project, please check out the contribution guidelines for more information on how to get started.
License
This project is licensed under the MIT License.
About
NNs from scratch
Resources
Readme
Contributing
Contributing
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Stars
2 stars
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1 watching
Forks
1 fork
Report repository
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, '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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Neural Network From Scratch
This repository contains a Python library for building simple Deep Neural Networks from scratch, using only vectorized operations with NumPy.
The goal of this project is to develop further understanding in the inner workings of neural networks and provide a foundation for building a Production-ready Python library.
Features
The current version of the library includes the following features:
  • A customizable neural network architecture with support for multiple layers and activation functions
  • Stochastic gradient descent optimization algorithm with support for different loss functions
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  • A suite of evaluation metrics, such as accuracy and mean squared error, for measuring model performance
  • A suite of utilities for data preprocessing, including normalization and one-hot encoding
)
Getting started
To use this library, you need to install it on your system. You can install it by running
pip install --no-cache-dir .
Once you have it installed, you can import the library into your Python code using:
fromneural_networkimportDeepNN
From there, you can create an instance of the Deep Neural Network architecture as a python class and customize it to fit your needs. For example, to create a neural network with one hidden layer and a sigmoid activation function, you can use the following code:
nn=DeepNN(layers_dims=[input_size, hidden_size, output_size], activations=["sigmoid", "sigmoid"])
Things to try
This library is a work in progress, and there are several areas where you can contribute and improve its functionality. Here are some ideas:
  • Optimizing performance: While the library is currently optimized for efficiency using NumPy, you can try using a GPU-accelerated computing library like CuPy and CuPyx to further improve its performance.
  • Developing new architectures of NNs: The current library supports only a simple feedforward neural network architecture, but you can explore other architectures, such as convolutional neural networks or recurrent neural networks, and implement them using the existing framework.
  • Adding more evaluation metrics: The library currently supports a limited set of evaluation metrics. You can add more metrics, such as precision and recall, to provide a more comprehensive view of model performance.
Contributing
If you are interested in contributing to this project, please check out the contribution guidelines for more information on how to get started.
License
This project is licensed under the MIT License.
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