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KARPATHY TWITTER
Andrej Karpathy blog. Mar 27, 2021 Short Story on AI: Forward Pass. A story of an existential crisis under the hood of a humble forward pass. Jun 11, 2020 Biohacking Lite. Dipping toes into biochemistry, energy metabolism and running some biohacking lite experiments. Apr 25, 2019 A Recipe for Training Neural Networks. HACKER'S GUIDE TO NEURAL NETWORKS Chapter 1: Real-valued Circuits. In my opinion, the best way to think of Neural Networks is as real-valued circuits, where real values (instead of boolean values {0,1}) “flow” along edges and interact in gates. However, instead of gates such as AND, OR, NOT, etc, we have binary gates such as * (multiply), + (add), max or unary gates such as A RECIPE FOR TRAINING NEURAL NETWORKS A Recipe for Training Neural Networks. Apr 25, 2019. Some few weeks ago I posted a tweet on “the most common neural net mistakes”, listing a few common gotchas related to training neural nets. The tweet got quite a bit more engagement than I anticipated (including a webinar:)).Clearly, a lot of people have personally encountered the large gap between “here is how a convolutional layer A SURVIVAL GUIDE TO A PHD DEEP REINFORCEMENT LEARNING: PONG FROM PIXELSANDREJ KARPATHY BLOGANDREJ KARPATHY BLOGANDREJ KARPATHY LINKEDIN Deep Reinforcement Learning: Pong from Pixels. This is a long overdue blog post on Reinforcement Learning (RL). RL is hot! You may have noticed that computers can now automatically learn to play ATARI games (from raw game pixels!), they are beating world champions at Go, simulated quadrupeds are learning to run and leap, and robots arelearning
WHAT I LEARNED FROM COMPETING AGAINST A CONVNET ON IMAGENETSEE MORE ONKARPATHY.GITHUB.IO
THE STATE OF COMPUTER VISION AND AI: WE ARE REALLY, REALLY Now consider that the state of the art techniques in Computer Vision are tested on things like Imagenet (task of assigning 1-of-k labels for entire images), or Pascal VOC detection challenge (+ include bounding boxes). There is also quite a bit of work on pose estimation, action recognition, etc., but it is all specific, disconnected, andonly
THE UNREASONABLE EFFECTIVENESS OF RECURRENT NEURAL NETWORKS The Unreasonable Effectiveness of Recurrent Neural Networks. May 21, 2015. There’s something magical about Recurrent Neural Networks (RNNs). I still remember when I trained my first recurrent network for Image Captioning.Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of BREAKING LINEAR CLASSIFIERS ON IMAGENETSEE MORE ON KARPATHY.GITHUB.IO (STARTED POSTING ON MEDIUM INSTEAD) Jan 20, 2018. The current state of this blog (with the last post 2 years ago) makes it look like I’ve disappeared. I’ve certainly become less active on blogs since I’ve joined Tesla, but whenever I do get a chance to post something I have recently been defaulting to doing it on Medium because it is much faster and easier. ANDREJ KARPATHY BLOGHACKER'S GUIDE TO NEURAL NETWORKSABOUTANDREJ KARPATHY BLOGANDREJ KARPATHY BLOGANDREJ KARPATHY DEEP LEARNINGANDREJ KARPATHY RNNANDREJ KARPATHY SALARYANDREJ KARPATHY SOFTWARE 2 0ANDREJKARPATHY TWITTER
Andrej Karpathy blog. Mar 27, 2021 Short Story on AI: Forward Pass. A story of an existential crisis under the hood of a humble forward pass. Jun 11, 2020 Biohacking Lite. Dipping toes into biochemistry, energy metabolism and running some biohacking lite experiments. Apr 25, 2019 A Recipe for Training Neural Networks. HACKER'S GUIDE TO NEURAL NETWORKS Chapter 1: Real-valued Circuits. In my opinion, the best way to think of Neural Networks is as real-valued circuits, where real values (instead of boolean values {0,1}) “flow” along edges and interact in gates. However, instead of gates such as AND, OR, NOT, etc, we have binary gates such as * (multiply), + (add), max or unary gates such as A RECIPE FOR TRAINING NEURAL NETWORKS A Recipe for Training Neural Networks. Apr 25, 2019. Some few weeks ago I posted a tweet on “the most common neural net mistakes”, listing a few common gotchas related to training neural nets. The tweet got quite a bit more engagement than I anticipated (including a webinar:)).Clearly, a lot of people have personally encountered the large gap between “here is how a convolutional layer A SURVIVAL GUIDE TO A PHD DEEP REINFORCEMENT LEARNING: PONG FROM PIXELSANDREJ KARPATHY BLOGANDREJ KARPATHY BLOGANDREJ KARPATHY LINKEDIN Deep Reinforcement Learning: Pong from Pixels. This is a long overdue blog post on Reinforcement Learning (RL). RL is hot! You may have noticed that computers can now automatically learn to play ATARI games (from raw game pixels!), they are beating world champions at Go, simulated quadrupeds are learning to run and leap, and robots arelearning
WHAT I LEARNED FROM COMPETING AGAINST A CONVNET ON IMAGENETSEE MORE ONKARPATHY.GITHUB.IO
THE STATE OF COMPUTER VISION AND AI: WE ARE REALLY, REALLY Now consider that the state of the art techniques in Computer Vision are tested on things like Imagenet (task of assigning 1-of-k labels for entire images), or Pascal VOC detection challenge (+ include bounding boxes). There is also quite a bit of work on pose estimation, action recognition, etc., but it is all specific, disconnected, andonly
THE UNREASONABLE EFFECTIVENESS OF RECURRENT NEURAL NETWORKS The Unreasonable Effectiveness of Recurrent Neural Networks. May 21, 2015. There’s something magical about Recurrent Neural Networks (RNNs). I still remember when I trained my first recurrent network for Image Captioning.Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of BREAKING LINEAR CLASSIFIERS ON IMAGENETSEE MORE ON KARPATHY.GITHUB.IO (STARTED POSTING ON MEDIUM INSTEAD) Jan 20, 2018. The current state of this blog (with the last post 2 years ago) makes it look like I’ve disappeared. I’ve certainly become less active on blogs since I’ve joined Tesla, but whenever I do get a chance to post something I have recently been defaulting to doing it on Medium because it is much faster and easier. SHORT STORY ON AI: FORWARD PASS Short Story on AI: Forward Pass. Mar 27, 2021. The inspiration for this short story came to me while reading Kevin Lacker’s Giving GPT-3 a Turing Test.It is probably worth it (though not required) to skim this post to get a bit of a background on some of this story.BIOHACKING LITE
Biohacking Lite. Jun 11, 2020. Throughout my life I never paid too much attention to health, exercise, diet or nutrition. I knew that you’re supposed to get some exercise and eat vegetables or something, but it stopped at that (“mom said”-) level ofabstraction.
SHORT STORY ON AI: A COGNITIVE DISCONTINUITY. Short Story on AI: A Cognitive Discontinuity. Nov 14, 2015. The idea of writing a collection of short stories has been on my mind for a while. This post is my first ever half-serious attempt at a story, and what better way to kick things off than with a story on AI and what that might look like if you extrapolate our current technology and make the (sensible) assumption that we might achieve CHROME EXTENSION PROGRAMMING: ILLUSTRATING A BASIC Chrome Extension Programming: Illustrating a Basic Survival Skill with a Twitter Case Study. Nov 23, 2013. Extension Hacking. I wanted to share a few examples of a powerful skill that I’ve been gradually picking up over the last year. THE STATE OF COMPUTER VISION AND AI: WE ARE REALLY, REALLY The state of Computer Vision and AI: we are really, really far away. Oct 22, 2012. The picture above is funny. But for me it is also one of those examples that make me sad about the outlook for AI and forComputer Vision.
QUANTIFYING PRODUCTIVITY Musings of a Computer Scientist. This view is a little dense so let me unpack it one by one: The Notes feature (on top) allows me to enter arbitrary notes for any time of day. Notice I also wrote an (optional) feature that looks for notes about coffee and calculates my levels of caffeine based on actual half-life of coffee. I am curious what caffeine does to my productivity! (STARTED POSTING ON MEDIUM INSTEAD) Jan 20, 2018. The current state of this blog (with the last post 2 years ago) makes it look like I’ve disappeared. I’ve certainly become less active on blogs since I’ve joined Tesla, but whenever I do get a chance to post something I have recently been defaulting to doing it on Medium because it is much faster and easier. QUANTIFYING HACKER NEWS WITH 50 DAYS OF DATA Quantifying Hacker News with 50 days of data. Nov 27, 2013. Quantifying Hacker News. I thought it would be fun to analyze the activity on one of my favorite sources of interesting links and information, Hacker News.My source of data is a script I’ve set up some time in August that downloads HN (the Front page and the New stories page) every minute. SWITCHING BLOG FROM WORDPRESS TO JEKYLL Switching Blog from Wordpress to Jekyll. Jul 1, 2014. Inspired by Mark Reid’s blog post Switching from Jekyll to Hakyll I decided to abandon Wordpress and give Jekyll a try (note, I currently do not yet feel pro enough to switch to Haskell-based Hakyll). I can confidently say that I could not be happier about this decision. Wordpress Monster INTERVIEW WITH DATA SCIENCE WEEKLY ON NEURAL NETS AND Interview with Data Science Weekly on Neural Nets and ConvNetJS. Apr 26, 2014. I thought I should link this: I’ve given an interview ~two months ago about ConvNetJS, some of my background and a few perspectives on neural net trends and where the field seems to be going, at least in academia. ANDREJ KARPATHY BLOGHACKER'S GUIDE TO NEURAL NETWORKSABOUTANDREJ KARPATHY BLOGANDREJ KARPATHY BLOGANDREJ KARPATHY DEEP LEARNINGANDREJ KARPATHY RNNANDREJ KARPATHY SALARYANDREJ KARPATHY SOFTWARE 2 0ANDREJKARPATHY TWITTER
Mar 27, 2021 Short Story on AI: Forward Pass A story of an existential crisis under the hood of a humble forward pass. Jun 11, 2020 Biohacking Lite Dipping toes into biochemistry, energy metabolism and running some biohacking lite experiments. HACKER'S GUIDE TO NEURAL NETWORKS Lets walk through x for example. We turned the knob from x to x + h and the circuit responded by giving a higher value (note again that yes, -5.9997 is higher than -6: -5.9997 > -6).The division by h is there to normalize the circuit’s response by the (arbitrary) value of h we chose to use here. Technically, you want the value of h to be infinitesimal (the precise mathematical definition of A RECIPE FOR TRAINING NEURAL NETWORKS A Recipe for Training Neural Networks. Apr 25, 2019. Some few weeks ago I posted a tweet on “the most common neural net mistakes”, listing a few common gotchas related to training neural nets. The tweet got quite a bit more engagement than I anticipated (including a webinar:)).Clearly, a lot of people have personally encountered the large gap between “here is how a convolutional layer DEEP REINFORCEMENT LEARNING: PONG FROM PIXELSANDREJ KARPATHY BLOGANDREJ KARPATHY BLOGANDREJ KARPATHY LINKEDIN Left: The game of Pong.Right: Pong is a special case of a Markov Decision Process (MDP): A graph where each node is a particular game state and each edge is a possible (in general probabilistic) transition.Each edge also gives a reward, and the goal is to compute the optimal way of acting in any state to maximize rewards. A SURVIVAL GUIDE TO A PHD WHAT I LEARNED FROM COMPETING AGAINST A CONVNET ON IMAGENETSEE MORE ONKARPATHY.GITHUB.IO
THE UNREASONABLE EFFECTIVENESS OF RECURRENT NEURAL NETWORKS The Unreasonable Effectiveness of Recurrent Neural Networks. May 21, 2015. There’s something magical about Recurrent Neural Networks (RNNs). I still remember when I trained my first recurrent network for Image Captioning.Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of THE STATE OF COMPUTER VISION AND AI: WE ARE REALLY, REALLY The state of Computer Vision and AI: we are really, really far away. Oct 22, 2012. The picture above is funny. But for me it is also one of those examples that make me sad about the outlook for AI and forComputer Vision.
BREAKING LINEAR CLASSIFIERS ON IMAGENETSEE MORE ON KARPATHY.GITHUB.IO (STARTED POSTING ON MEDIUM INSTEAD) (started posting on Medium instead) Jan 20, 2018. The current state of this blog (with the last post 2 years ago) makes it look like I’vedisappeared.
ANDREJ KARPATHY BLOGHACKER'S GUIDE TO NEURAL NETWORKSABOUTANDREJ KARPATHY BLOGANDREJ KARPATHY BLOGANDREJ KARPATHY DEEP LEARNINGANDREJ KARPATHY RNNANDREJ KARPATHY SALARYANDREJ KARPATHY SOFTWARE 2 0ANDREJKARPATHY TWITTER
Mar 27, 2021 Short Story on AI: Forward Pass A story of an existential crisis under the hood of a humble forward pass. Jun 11, 2020 Biohacking Lite Dipping toes into biochemistry, energy metabolism and running some biohacking lite experiments. HACKER'S GUIDE TO NEURAL NETWORKS Lets walk through x for example. We turned the knob from x to x + h and the circuit responded by giving a higher value (note again that yes, -5.9997 is higher than -6: -5.9997 > -6).The division by h is there to normalize the circuit’s response by the (arbitrary) value of h we chose to use here. Technically, you want the value of h to be infinitesimal (the precise mathematical definition of A RECIPE FOR TRAINING NEURAL NETWORKS A Recipe for Training Neural Networks. Apr 25, 2019. Some few weeks ago I posted a tweet on “the most common neural net mistakes”, listing a few common gotchas related to training neural nets. The tweet got quite a bit more engagement than I anticipated (including a webinar:)).Clearly, a lot of people have personally encountered the large gap between “here is how a convolutional layer DEEP REINFORCEMENT LEARNING: PONG FROM PIXELSANDREJ KARPATHY BLOGANDREJ KARPATHY BLOGANDREJ KARPATHY LINKEDIN Left: The game of Pong.Right: Pong is a special case of a Markov Decision Process (MDP): A graph where each node is a particular game state and each edge is a possible (in general probabilistic) transition.Each edge also gives a reward, and the goal is to compute the optimal way of acting in any state to maximize rewards. A SURVIVAL GUIDE TO A PHD WHAT I LEARNED FROM COMPETING AGAINST A CONVNET ON IMAGENETSEE MORE ONKARPATHY.GITHUB.IO
THE UNREASONABLE EFFECTIVENESS OF RECURRENT NEURAL NETWORKS The Unreasonable Effectiveness of Recurrent Neural Networks. May 21, 2015. There’s something magical about Recurrent Neural Networks (RNNs). I still remember when I trained my first recurrent network for Image Captioning.Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of THE STATE OF COMPUTER VISION AND AI: WE ARE REALLY, REALLY The state of Computer Vision and AI: we are really, really far away. Oct 22, 2012. The picture above is funny. But for me it is also one of those examples that make me sad about the outlook for AI and forComputer Vision.
BREAKING LINEAR CLASSIFIERS ON IMAGENETSEE MORE ON KARPATHY.GITHUB.IO (STARTED POSTING ON MEDIUM INSTEAD) (started posting on Medium instead) Jan 20, 2018. The current state of this blog (with the last post 2 years ago) makes it look like I’vedisappeared.
SHORT STORY ON AI: FORWARD PASS Short Story on AI: Forward Pass. Mar 27, 2021. The inspiration for this short story came to me while reading Kevin Lacker’s Giving GPT-3 a Turing Test.It is probably worth it (though not required) to skim this post to get a bit of a background on some of this story.BIOHACKING LITE
Biohacking Lite. Jun 11, 2020. Throughout my life I never paid too much attention to health, exercise, diet or nutrition. I knew that you’re supposed to get some exercise and eat vegetables or something, but it stopped at that (“mom said”-) level ofabstraction.
SHORT STORY ON AI: A COGNITIVE DISCONTINUITY. Short Story on AI: A Cognitive Discontinuity. Nov 14, 2015. The idea of writing a collection of short stories has been on my mind for a while. This post is my first ever half-serious attempt at a story, and what better way to kick things off than with a story on AI and what that might look like if you extrapolate our current technology and make the (sensible) assumption that we might achieve CHROME EXTENSION PROGRAMMING: ILLUSTRATING A BASIC Chrome Extension Programming: Illustrating a Basic Survival Skill with a Twitter Case Study. Nov 23, 2013. Extension Hacking. I wanted to share a few examples of a powerful skill that I’ve been gradually picking up over the last year. THE STATE OF COMPUTER VISION AND AI: WE ARE REALLY, REALLY The state of Computer Vision and AI: we are really, really far away. Oct 22, 2012. The picture above is funny. But for me it is also one of those examples that make me sad about the outlook for AI and forComputer Vision.
QUANTIFYING PRODUCTIVITY Musings of a Computer Scientist. This view is a little dense so let me unpack it one by one: The Notes feature (on top) allows me to enter arbitrary notes for any time of day. Notice I also wrote an (optional) feature that looks for notes about coffee and calculates my levels of caffeine based on actual half-life of coffee. I am curious what caffeine does to my productivity! QUANTIFYING HACKER NEWS WITH 50 DAYS OF DATA Quantifying Hacker News with 50 days of data. Nov 27, 2013. Quantifying Hacker News. I thought it would be fun to analyze the activity on one of my favorite sources of interesting links and information, Hacker News.My source of data is a script I’ve set up some time in August that downloads HN (the Front page and the New stories page) every minute. (STARTED POSTING ON MEDIUM INSTEAD) (started posting on Medium instead) Jan 20, 2018. The current state of this blog (with the last post 2 years ago) makes it look like I’vedisappeared.
SWITCHING BLOG FROM WORDPRESS TO JEKYLL Switching Blog from Wordpress to Jekyll. Jul 1, 2014. Inspired by Mark Reid’s blog post Switching from Jekyll to Hakyll I decided to abandon Wordpress and give Jekyll a try (note, I currently do not yet feel pro enough to switch to Haskell-based Hakyll). I can confidently say that I could not be happier about this decision. Wordpress Monster INTERVIEW WITH DATA SCIENCE WEEKLY ON NEURAL NETS AND Interview with Data Science Weekly on Neural Nets and ConvNetJS. Apr 26, 2014. I thought I should link this: I’ve given an interview ~two months ago about ConvNetJS, some of my background and a few perspectives on neural net trends and where the field seems to be going, at least in academia. Andrej Karpathy blog About Hacker's guide to Neural Networks * Apr 25, 2019 A Recipe for Training Neural Networks A collection of practical advice for the process of achieving strong results with neural networks. * Jan 20, 2018 (started posting on Medium instead) Yes I'm still around but, I've started posting on Medium instead ofhere.
* Sep 7, 2016 A Survival Guide to a PhD A collection of tips/tricks for navigating the PhD experience. * May 31, 2016 Deep Reinforcement Learning: Pong from Pixels I'll discuss the core ideas, pros and cons of policy gradients, a standard approach to the rapidly growing and exciting area of deep reinforcement learning. As a running example we'll learn to play ATARI 2600 Pong from raw pixels. * Nov 14, 2015 Short Story on AI: A Cognitive Discontinuity. The first part of a short story collection that has been on my mind for a long while. Exciting! :) * Oct 25, 2015 What a Deep Neural Network thinks about your #selfie We will look at Convolutional Neural Networks, with a fun example of training them to classify #selfies as good/bad based on a scraped dataset of 2 million selfies. * May 21, 2015 The Unreasonable Effectiveness of Recurrent NeuralNetworks
We'll train and sample from character-level RNN language models that learn to write poetry, latex math and code. We'll also analyze the models and get hints of future research directions. * Mar 30, 2015 Breaking Linear Classifiers on ImageNet There have been a few recent papers that fool ConvNets by taking a correctly classified image and perturbing it in an imperceptible way to produce an image that is misclassified. In this post I show that ConvNets are an overkill: Simple linear classifiers are in fact susceptible to the same fooling strategy. * Sep 2, 2014 What I learned from competing against a ConvNet onImageNet
The latest state of the art Image Classification networks have only 6.7% Hit@5 error on ILSVRC 2014 classification task. How do humanscompare?
* Aug 3, 2014 Quantifying Productivity Describing a new pet project that tracks active windows and keystroke frequencies over the duration of a day (on Ubuntu/OSX) and creates pretty HTML visualizations of the data. This allows me to gain nice insights into my productivity. Code on Github. * Jul 3, 2014 Feature Learning Escapades Some reflections on the last two years of my research: The Quest for Unsupervised Feature Learning algorithms for visual data. Where it was, where it is, and where it's going. Maybe. * Jul 2, 2014 Visualizing Top Tweeps with t-SNE, in Javascript A writeup of a recent mini-project: I scraped tweets of the top 500 Twitter accounts and used t-SNE to visualize the accounts so that people who tweet similar things are nearby. My final Javascript implementation of t-SNE is released on Github as tsnejs. * Jul 1, 2014 Switching Blog from Wordpress to Jekyll I can't believe I lasted this long on Wordpress. I am switching permanently to Jekyll for hosting my blog, and so should you :)Details inside.
* Apr 26, 2014 Interview with Data Science Weekly on Neural Nets andConvNetJS
I gave a (long) interview about my background and perspectives onneural nets.
* Nov 27, 2013 Quantifying Hacker News with 50 days of data I scraped Hacker News Front Page and New Page every minute for 50 days and analyzed the results. How do stories rise and fall on Hacker News? What makes a successful post? Find out in this post :) * Nov 23, 2013 Chrome Extension Programming: Illustrating a Basic Survival Skill with a Twitter Case Study I illustrate a very valuable skill (Chrome Extension Programming) using a Twitter Case study. We will give Twitter a face lift, get it to refresh new tweets automatically, and highlight tweets from people who rarely tweet. All with a few lines of Javascript! * Oct 22, 2012 The state of Computer Vision and AI: we are really,really far away.
A depressing look at the state of Computer Vision Research and AI in general. For those who like to think that AI is anywhere close. * Apr 27, 2011 Lessons learned from manually classifying CIFAR-10 CIFAR-10 is a popular dataset small dataset for testing out Computer Vision Deep Learning learning methods. We're seeing a lot of improvements. But what is the human baseline? * Andrej Karpathy blog* karpathy
* karpathy
Musings of a Computer Scientist.Details
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