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Topics /
AI & Machine Learning
The most popular blog posts about ai & machine learning on Hacker News. 292 posts.
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Playing Mortal Kombat with TensorFlow.js
While experimenting with enhancements of the prediction model of Guess.js, I started looking at deep learning. I’ve focused mainly on recurrent neural networks (RNNs), specifically LSTM because of their “unreasonable effectiveness” in the domain of Guess.js. In the same time, I started playing with convolutional…
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The Google Assistant is going global
Over the past year and a half, the Google Assistant has grown from being available on just one device in one language to across many types of devices, including speakers…
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A Long Peek into Reinforcement Learning
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Dissecting Reinforcement Learning
Explaining the basic ideas behind reinforcement learning. In particular, Markov Decision Process, Bellman equation, Value iteration and Policy Iteration algorithms, policy iteration through linear algebra methods. It includes full working code written in Python.
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Self driving cars and beyond
The story of technology is not unlike what often happens in dramatic stories and plays, where a “deus ex machina” or character drops down magically from the skies onto our world stage. But in reality, technology never comes so suddenly, or in such an isolated manner: There’s an entire ecosystem that grows up around…
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Translating Between Statistics and Machine Learning
This SEI Blog post explores the differences between statistics and machine learning and how to translate statistical models into machine learning models.
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Guide to Speech Recognition with Python
An in-depth tutorial on speech recognition with Python. Learn which speech recognition library gives the best results and build a full-featured "Guess The Word" game with it.
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Nematode Neural System Uploaded to a Computer and Trained to Balance a Pole
TU Wien News. Press releases. Calendar of Events. University Gazette.
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Interactive GPU Programming, Part 2: Hello OpenCL
This is really the same article as Part 1 - Hello CUDA, but with the focus on OpenCL, so I'll skip most of the narration and just show you the code.
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Recurrent Neural Network Tutorial for Artists (2017)
Recurrent Neural Network Tutorial for Artists
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Reptile: A Scalable Meta-Learning Algorithm
Stay up to speed on the rapid advancement of AI technology and the benefits it offers to humanity.
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Do neural networks dream of electric sheep?
If you’ve been on the internet today, you’ve probably interacted with a neural network. They’re a type of machine learning algorithm that’s used for everything from language translation to finance modeling. One of their specialties is image recognition. Several companies - including Google, Microsoft, IBM, and…
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Learning by playing
Getting children (and adults) to tidy up after themselves can be a challenge, but we face an even greater challenge trying to get our AI agents to do the same. Success depends on the mastery of several core visuo-motor skills: approaching an object, grasping and lifting it, opening a box and putting things inside of…
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Real time numbers recognition (MNIST) on an iPhone with CoreML
Learn how to build and train a deep learning network to recognize numbers (MNIST),how to convert it in the CoreML format to then deploy it on your iPhoneX and …
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A New Angle on L2 Regularization
Deep neural networks have been shown to be vulnerable to the adversarial example phenomenon: all models tested so far can have their classifications dramatically altered by small image perturbations . The following predictions were for instance made by a state-of-the-art network trained to recognize celebrities :
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Impala: Scalable Distributed Deep Reinforcement Learning
Deep Reinforcement Learning (DeepRL) has achieved remarkable success in a range of tasks, from continuous control problems in robotics to playing games like Go and Atari. The improvements seen in these domains have so far been limited to individual tasks where a separate agent has been tuned and trained for each task.
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MLflow: An Open Source Machine Learning Platform
MLflow is an open-source platform to manage the ML lifecycle, offering tools for data preparation, model training, and deployment at scale.
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Reproducible machine learning with PyTorch and Quilt
In this article, we'll use Quilt to transfer versioned training data to a remote machine. We'll start with the Berkeley Segmentation Dataset, package the dataset, then train a PyTorch model for super-resolution imaging.
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Open-Source Machine Learning Repos to Inspire Your Next Project
In the last couple of years, machine learning has opened up new horizons in a wide range of industries, with advanced use cases emerging: Facebook’s facial recognition, Netflix’s recommended movies, PrismaAI’s image style transfer, Siri’s voice recognition, Google Allo’s natural… Continue reading 25 Open-Source…
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A Full Hardware Guide to Deep Learning
In this guide I analyse hardware from CPU to SSD and their impact on performance for deep learning so that you can choose the hardware that you really need.
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Nevergrad: A Python library for performing derivative-free ML optimization
Most machine learning tasks — from natural language processing to image classification to translation and many others — rely on derivative-free optimization to tune parameters and/or hyperparameter…
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Foundations of ML and AI: Book recommendations
Here's what I recommend to programmers if they want to understand machine learning. Six fantastic books that you can work with in your spare time to build yo...
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Interpretable Machine Learning Through Teaching
Stay up to speed on the rapid advancement of AI technology and the benefits it offers to humanity.
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Optimizing Siri on HomePod in Far‑Field Settings
The typical audio environment for HomePod has many challenges — echo, reverberation, and noise. Unlike Siri on iPhone, which operates close…
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AI and Blockchain: An Introduction
At the kind invitation of Rob May and the Botchain team, I had the opportunity recently to keynote Brains and Chains, an interesting conference in New York expl