BranchesTags

Repository files navigation

RWOP

My slides and jupyter notebook for my random walk on physics talk on neural networks. To get them:

  • You can download as zip from github
  • Using the command line: git clone https://github.com/lsgos/RWOP

To run and view the ipython notebook (ipynb) file requires jupyter: you can get this by

  • using pip: pip install jupyter
  • Installing anaconda, which will come with jupyter.

Once you have jupyter installed, open a terminal/command prompt, navigate to the directory where you downloaded this repository and run jupyter notebook to start the notebook interface.

In order to run all the code in the notebook itself, you're going to need to install some extra python libraries /programs:

Note that caffe is a huge pain to install, and I'm not totally sure it works at all on windows. There is a community macOS port though I think. If you are having trouble then there is some help here

In case you are just looking for my last slide and don't want to/can't manage to install all of this stuff, here's my 'further reading' list from the talk.

  • Neural networks and deep learning: An online book that derives backpropagation from scratch and shows how to implement a neural net totally 'by hand' in numpy
  • The unreasonable effectiveness of recurrent neural networks: A fantastic blog post on recurrent nets (which are very cool indeed) and some crazy stuff thats been done with them
  • Atari A neural network plays atari!
  • Grabber Farm: Robots learning to pick stuff up
  • Colah's Blog: Whole series of posts about deep learning and neural nets, including some more mathematical treatment of data as manifolds.
  • Tensorflow: An open-source neural net library, which has some great hands on tutorials.
  • Caffe: Another one, more tailored for image recognition, which comes with pre-trained models. WARNING: CAFFE IS A NIGHTMARE TO INSTALL
  • Torch: Yet another neural net library, with the quirk of being in lua.
  • Scikit-Learn: Easy machine learning in python, has a nice set of tutorials too.
  • Fork this talk on github: This entire presentation is a jupyter notebook, and I plan to make the source avaliable on my github page, so you can see and run all the code I used to generate this slideshow!

About

My slides and jupyter notebook for the random walk on physics talk on neural networks

Resources

Readme
Activity

Stars

1 star

Watchers

1 watching

Forks

1 fork
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
 blocks
(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
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;';
btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
btn.onclick = function() {
navigator.clipboard.writeText(codeBlock.textContent).then(function() {
btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
};
codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
});
}
addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} 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
RWOP
My slides and jupyter notebook for my random walk on physics talk on neural networks. To get them:
  • You can download as zip from github
  • Using the command line: git clone https://github.com/lsgos/RWOP
To run and view the ipython notebook (ipynb) file requires jupyter: you can get this by
  • using pip: pip install jupyter
  • Installing anaconda, which will come with jupyter.
Once you have jupyter installed, open a terminal/command prompt, navigate to the directory where you downloaded this repository and run
jupyter notebook
to start the notebook interface.
In order to run all the code in the notebook itself, you're going to need to install some extra python libraries /programs:
Note that caffe is a huge pain to install, and I'm not totally sure it works at all on windows.
There is a community macOS port though I think. If you are having trouble then there is some help here
In case you are just looking for my last slide and don't want to/can't manage to install all of this stuff, here's my 'further reading' list from the talk.
  • Neural networks and deep learning: An online book that derives backpropagation from scratch and shows how to implement a neural net totally 'by hand' in numpy
  • The unreasonable effectiveness of recurrent neural networks: A fantastic blog post on recurrent nets (which are very cool indeed) and some crazy stuff thats been done with them
  • Atari A neural network plays atari!
  • Grabber Farm: Robots learning to pick stuff up
  • Colah's Blog: Whole series of posts about deep learning and neural nets, including some more mathematical treatment of data as manifolds.
  • Tensorflow: An open-source neural net library, which has some great hands on tutorials.
  • Caffe: Another one, more tailored for image recognition, which comes with pre-trained models. WARNING: CAFFE IS A NIGHTMARE TO INSTALL
  • Torch: Yet another neural net library, with the quirk of being in lua.
  • Scikit-Learn: Easy machine learning in python, has a nice set of tutorials too.
  • Fork this talk on github: This entire presentation is a jupyter notebook, and I plan to make the source avaliable on my github page, so you can see and run all the code I used to generate this slideshow!
About
My slides and jupyter notebook for the random walk on physics talk on neural networks
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
.markdown-body table th, .markdown-body table td { border-color: #30363d !important; }
.markdown-body img { background: #0d1117; }
.markdown-body blockquote { border-left-color: #8b949e; }
.markdown-body hr { border-color: #30363d; }
';
document.head.appendChild(style);
})();
}
} 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
RWOP
My slides and jupyter notebook for my random walk on physics talk on neural networks. To get them:
  • You can download as zip from github
  • Using the command line: git clone https://github.com/lsgos/RWOP
To run and view the ipython notebook (ipynb) file requires jupyter: you can get this by
  • using pip: pip install jupyter
  • Installing anaconda, which will come with jupyter.
Once you have jupyter installed, open a terminal/command prompt, navigate to the directory where you downloaded this repository and run
jupyter notebook
to start the notebook interface.
In order to run all the code in the notebook itself, you're going to need to install some extra python libraries /programs:
Note that caffe is a huge pain to install, and I'm not totally sure it works at all on windows.
There is a community macOS port though I think. If you are having trouble then there is some help here
In case you are just looking for my last slide and don't want to/can't manage to install all of this stuff, here's my 'further reading' list from the talk.
  • Neural networks and deep learning: An online book that derives backpropagation from scratch and shows how to implement a neural net totally 'by hand' in numpy
  • The unreasonable effectiveness of recurrent neural networks: A fantastic blog post on recurrent nets (which are very cool indeed) and some crazy stuff thats been done with them
  • Atari A neural network plays atari!
  • Grabber Farm: Robots learning to pick stuff up
  • Colah's Blog: Whole series of posts about deep learning and neural nets, including some more mathematical treatment of data as manifolds.
  • Tensorflow: An open-source neural net library, which has some great hands on tutorials.
  • Caffe: Another one, more tailored for image recognition, which comes with pre-trained models. WARNING: CAFFE IS A NIGHTMARE TO INSTALL
  • Torch: Yet another neural net library, with the quirk of being in lua.
  • Scikit-Learn: Easy machine learning in python, has a nice set of tutorials too.
  • Fork this talk on github: This entire presentation is a jupyter notebook, and I plan to make the source avaliable on my github page, so you can see and run all the code I used to generate this slideshow!
About
My slides and jupyter notebook for the random walk on physics talk on neural networks
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

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

Skip to content
BranchesTags
Repository files navigation
RWOP
My slides and jupyter notebook for my random walk on physics talk on neural networks. To get them:
  • You can download as zip from github
  • Using the command line: git clone https://github.com/lsgos/RWOP
To run and view the ipython notebook (ipynb) file requires jupyter: you can get this by
  • using pip: pip install jupyter
  • Installing anaconda, which will come with jupyter.
Once you have jupyter installed, open a terminal/command prompt, navigate to the directory where you downloaded this repository and run
jupyter notebook
to start the notebook interface.
In order to run all the code in the notebook itself, you're going to need to install some extra python libraries /programs:
Note that caffe is a huge pain to install, and I'm not totally sure it works at all on windows.
There is a community macOS port though I think. If you are having trouble then there is some help here
In case you are just looking for my last slide and don't want to/can't manage to install all of this stuff, here's my 'further reading' list from the talk.
  • Neural networks and deep learning: An online book that derives backpropagation from scratch and shows how to implement a neural net totally 'by hand' in numpy
  • The unreasonable effectiveness of recurrent neural networks: A fantastic blog post on recurrent nets (which are very cool indeed) and some crazy stuff thats been done with them
  • Atari A neural network plays atari!
  • Grabber Farm: Robots learning to pick stuff up
  • Colah's Blog: Whole series of posts about deep learning and neural nets, including some more mathematical treatment of data as manifolds.
  • Tensorflow: An open-source neural net library, which has some great hands on tutorials.
  • Caffe: Another one, more tailored for image recognition, which comes with pre-trained models. WARNING: CAFFE IS A NIGHTMARE TO INSTALL
  • Torch: Yet another neural net library, with the quirk of being in lua.
  • Scikit-Learn: Easy machine learning in python, has a nice set of tutorials too.
  • Fork this talk on github: This entire presentation is a jupyter notebook, and I plan to make the source avaliable on my github page, so you can see and run all the code I used to generate this slideshow!
About
My slides and jupyter notebook for the random walk on physics talk on neural networks
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} 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
RWOP
My slides and jupyter notebook for my random walk on physics talk on neural networks. To get them:
  • You can download as zip from github
  • Using the command line: git clone https://github.com/lsgos/RWOP
To run and view the ipython notebook (ipynb) file requires jupyter: you can get this by
  • using pip: pip install jupyter
  • Installing anaconda, which will come with jupyter.
Once you have jupyter installed, open a terminal/command prompt, navigate to the directory where you downloaded this repository and run
jupyter notebook
to start the notebook interface.
In order to run all the code in the notebook itself, you're going to need to install some extra python libraries /programs:
Note that caffe is a huge pain to install, and I'm not totally sure it works at all on windows.
There is a community macOS port though I think. If you are having trouble then there is some help here
In case you are just looking for my last slide and don't want to/can't manage to install all of this stuff, here's my 'further reading' list from the talk.
  • Neural networks and deep learning: An online book that derives backpropagation from scratch and shows how to implement a neural net totally 'by hand' in numpy
  • The unreasonable effectiveness of recurrent neural networks: A fantastic blog post on recurrent nets (which are very cool indeed) and some crazy stuff thats been done with them
  • Atari A neural network plays atari!
  • Grabber Farm: Robots learning to pick stuff up
  • Colah's Blog: Whole series of posts about deep learning and neural nets, including some more mathematical treatment of data as manifolds.
  • Tensorflow: An open-source neural net library, which has some great hands on tutorials.
  • Caffe: Another one, more tailored for image recognition, which comes with pre-trained models. WARNING: CAFFE IS A NIGHTMARE TO INSTALL
  • Torch: Yet another neural net library, with the quirk of being in lua.
  • Scikit-Learn: Easy machine learning in python, has a nice set of tutorials too.
  • Fork this talk on github: This entire presentation is a jupyter notebook, and I plan to make the source avaliable on my github page, so you can see and run all the code I used to generate this slideshow!
About
My slides and jupyter notebook for the random walk on physics talk on neural networks
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} 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
RWOP
My slides and jupyter notebook for my random walk on physics talk on neural networks. To get them:
  • You can download as zip from github
  • Using the command line: git clone https://github.com/lsgos/RWOP
To run and view the ipython notebook (ipynb) file requires jupyter: you can get this by
  • using pip: pip install jupyter
  • Installing anaconda, which will come with jupyter.
Once you have jupyter installed, open a terminal/command prompt, navigate to the directory where you downloaded this repository and run
jupyter notebook
to start the notebook interface.
In order to run all the code in the notebook itself, you're going to need to install some extra python libraries /programs:
Note that caffe is a huge pain to install, and I'm not totally sure it works at all on windows.
There is a community macOS port though I think. If you are having trouble then there is some help here
In case you are just looking for my last slide and don't want to/can't manage to install all of this stuff, here's my 'further reading' list from the talk.
  • Neural networks and deep learning: An online book that derives backpropagation from scratch and shows how to implement a neural net totally 'by hand' in numpy
  • The unreasonable effectiveness of recurrent neural networks: A fantastic blog post on recurrent nets (which are very cool indeed) and some crazy stuff thats been done with them
  • Atari A neural network plays atari!
  • Grabber Farm: Robots learning to pick stuff up
  • Colah's Blog: Whole series of posts about deep learning and neural nets, including some more mathematical treatment of data as manifolds.
  • Tensorflow: An open-source neural net library, which has some great hands on tutorials.
  • Caffe: Another one, more tailored for image recognition, which comes with pre-trained models. WARNING: CAFFE IS A NIGHTMARE TO INSTALL
  • Torch: Yet another neural net library, with the quirk of being in lua.
  • Scikit-Learn: Easy machine learning in python, has a nice set of tutorials too.
  • Fork this talk on github: This entire presentation is a jupyter notebook, and I plan to make the source avaliable on my github page, so you can see and run all the code I used to generate this slideshow!
About
My slides and jupyter notebook for the random walk on physics talk on neural networks
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

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

Skip to content
BranchesTags
Repository files navigation
RWOP
My slides and jupyter notebook for my random walk on physics talk on neural networks. To get them:
  • You can download as zip from github
  • Using the command line: git clone https://github.com/lsgos/RWOP
To run and view the ipython notebook (ipynb) file requires jupyter: you can get this by
  • using pip: pip install jupyter
  • Installing anaconda, which will come with jupyter.
Once you have jupyter installed, open a terminal/command prompt, navigate to the directory where you downloaded this repository and run
jupyter notebook
to start the notebook interface.
In order to run all the code in the notebook itself, you're going to need to install some extra python libraries /programs:
Note that caffe is a huge pain to install, and I'm not totally sure it works at all on windows.
There is a community macOS port though I think. If you are having trouble then there is some help here
In case you are just looking for my last slide and don't want to/can't manage to install all of this stuff, here's my 'further reading' list from the talk.
  • Neural networks and deep learning: An online book that derives backpropagation from scratch and shows how to implement a neural net totally 'by hand' in numpy
  • The unreasonable effectiveness of recurrent neural networks: A fantastic blog post on recurrent nets (which are very cool indeed) and some crazy stuff thats been done with them
  • Atari A neural network plays atari!
  • Grabber Farm: Robots learning to pick stuff up
  • Colah's Blog: Whole series of posts about deep learning and neural nets, including some more mathematical treatment of data as manifolds.
  • Tensorflow: An open-source neural net library, which has some great hands on tutorials.
  • Caffe: Another one, more tailored for image recognition, which comes with pre-trained models. WARNING: CAFFE IS A NIGHTMARE TO INSTALL
  • Torch: Yet another neural net library, with the quirk of being in lua.
  • Scikit-Learn: Easy machine learning in python, has a nice set of tutorials too.
  • Fork this talk on github: This entire presentation is a jupyter notebook, and I plan to make the source avaliable on my github page, so you can see and run all the code I used to generate this slideshow!
About
My slides and jupyter notebook for the random walk on physics talk on neural networks
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

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

Skip to content
BranchesTags
Repository files navigation
RWOP
My slides and jupyter notebook for my random walk on physics talk on neural networks. To get them:
  • You can download as zip from github
  • Using the command line: git clone https://github.com/lsgos/RWOP
To run and view the ipython notebook (ipynb) file requires jupyter: you can get this by
  • using pip: pip install jupyter
  • Installing anaconda, which will come with jupyter.
Once you have jupyter installed, open a terminal/command prompt, navigate to the directory where you downloaded this repository and run
jupyter notebook
to start the notebook interface.
In order to run all the code in the notebook itself, you're going to need to install some extra python libraries /programs:
Note that caffe is a huge pain to install, and I'm not totally sure it works at all on windows.
There is a community macOS port though I think. If you are having trouble then there is some help here
In case you are just looking for my last slide and don't want to/can't manage to install all of this stuff, here's my 'further reading' list from the talk.
  • Neural networks and deep learning: An online book that derives backpropagation from scratch and shows how to implement a neural net totally 'by hand' in numpy
  • The unreasonable effectiveness of recurrent neural networks: A fantastic blog post on recurrent nets (which are very cool indeed) and some crazy stuff thats been done with them
  • Atari A neural network plays atari!
  • Grabber Farm: Robots learning to pick stuff up
  • Colah's Blog: Whole series of posts about deep learning and neural nets, including some more mathematical treatment of data as manifolds.
  • Tensorflow: An open-source neural net library, which has some great hands on tutorials.
  • Caffe: Another one, more tailored for image recognition, which comes with pre-trained models. WARNING: CAFFE IS A NIGHTMARE TO INSTALL
  • Torch: Yet another neural net library, with the quirk of being in lua.
  • Scikit-Learn: Easy machine learning in python, has a nice set of tutorials too.
  • Fork this talk on github: This entire presentation is a jupyter notebook, and I plan to make the source avaliable on my github page, so you can see and run all the code I used to generate this slideshow!
About
My slides and jupyter notebook for the random walk on physics talk on neural networks
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages