AI and other deep technologies are the prevailing themes in the new early-stage cohort from Peak XV Partners, as the largest India and Southeast Asia-focused VC fund intensifies its search for opportu
Research Differentiable neural computers Published 12 October 2016 Authors Gregory Wayne, Alexander Graves In a recent study in Nature, we introduce a form of memory-augmented neural network called a differentiable neural computer, and show that it can learn to use its memory to answer questions about complex, structured data, including artificially generated stories, family trees, and even a map
Distributed TensorFlow Has Arrived Google has open sourced its distributed version of TensorFlow. Get the info on it here, and catch up on some other TensorFlow news at the same time. KDnuggets has taken seriously its role to keep up with the newest releases of major deep learning projects, and in the recent past we have seen landmark such releases from major technology giants and as well as unive
lightning¶ lightning is a library for large-scale linear classification, regression and ranking in Python. Highlights: follows the scikit-learn API conventions supports natively both dense and sparse data representations computationally demanding parts implemented in Cython Solvers supported: primal coordinate descent dual coordinate descent (SDCA, Prox-SDCA) SGD, AdaGrad, SAG, SAGA, SVRG FISTA Ex
After version 2.43, the Python interface of multi-core LIBLINEAR can be installed through PyPI: > pip install -U liblinear-multicore This extension is an OpenMP implementation to significantly reduce the training time in a shared-memory system. Technical details are in the following papers. M.-C. Lee, W.-L. Chiang, and C.-J. Lin. Fast Matrix-vector Multiplications for Large-scale Logistic Regressi
by Hal Daumé III Machine learning is the study of algorithms that learn from data and experience. It is applied in a vast variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential consumer of machine learning. CIML is a set of introductory materials that covers most major aspects of modern machine learni
Ed. Note: this post is in my voice, but it was co-written with Kevin Jamieson. Kevin provided the awesome plots too. It’s all the rage in machine learning these days to build complex, deep pipelines with thousands of tunable parameters. Now, I don’t mean parameters that we learn by stochastic gradient descent. But I mean architectural concerns, like the value of the regularization parameter, the s
画像のスタイルを変換するアルゴリズムとしてGatysらの"A Neural Algorithm of Artistic Style"が知られていますが、これを高速に行う手法が現れました。 以下のつぶやきを見て驚愕したので早速調べました。 testing real-time style transfer published in the last week with #chainer and #openFrameworks pic.twitter.com/KrQaN8TSs9 — Yusuke Tomoto (@_mayfa) 2016年4月7日 2016/4/12追記 Yusuke Tomoto氏による実装が公開されました。Chainerを使っています。 https://github.com/yusuketomoto/chainer-fast-neuralstyle 実装の元になった論文は
Ed. Note: this post is again in my voice, but co-written with Kevin Jamieson. Kevin provided all of the awesome plots, and has a great tutorial for implementing the algorithm I’ll describe in this post In the last post, I argued that random search is a competitive method for black-box parameter tuning in machine learning. This is actually great news! Random search is a incredibly simple algorithm,
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization Kevin Jamieson on joint work with Lisha Li, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar (https://arxiv.org/abs/1603.06560) The hyperparamter optimization literature in recent years has been dominated by hyperparameter selection algorithms (e.g. Bayesian Optimization) that attempt to improve upon grid/random search
If you want to build artificial intelligence into every product, you better retrain your army of coders. Check. Carson Holgate is training to become a ninja. Not in the martial arts — she’s already done that. Holgate, 26, holds a second degree black belt in Tae Kwon Do. This time it’s algorithmic. Holgate is several weeks into a program that will inculcate her in an even more powerful practice tha
Terrain-Adaptive Locomotion Skills Using Deep Reinforcement Learning Transactions on Graphics (Proc. ACM SIGGRAPH 2016) (to appear) Xue Bin Peng Glen Berseth Michiel van de Panne University of British Columbia Reinforcement learning offers a promising methodology for developing skills for simulated characters, but typically requires working with sparse hand-crafted features. Building on re
Google supercharges machine learning tasks with TPU custom chip Editor's Update June 27, 2017: We recently announced Cloud TPUs. Machine learning provides the underlying oomph to many of Google’s most-loved applications. In fact, more than 100 teams are currently using machine learning at Google today, from Street View, to Inbox Smart Reply, to voice search. But one thing we know to be true at Goo
機械学習の分類の話¶分類(Classification)は、教師あり学習の1つで、予測対象はカテゴリなどの離散的な値を予測します。 具体的には、メールがスパムかどうかや、画像に映っているのがどういった物体か、といったバイナリで表現できる値を予測する場合にモデルを作ります。 基本的に、入力データをベクトル $x$ (例えば [1, 3, 4, 8] )と考えた時に、予測対象のカテゴリ $y$ ( ham/spam )を出力します。 入力ベクトルは整数や小数のベクトルの場合が多いですが、「晴れ」「雨」などといったカテゴリ情報も適当な数値に変換して扱うことが多いです。 同様に出力されたカテゴリは、-1や1などの整数で表現することが多いです。 このnotebookでは、分類について以下のモデルを紹介します。 パーセプトロン(Perceptron) ロジスティック回帰(Logistic Regres
Artificial Intelligence - machine learning - Machine Learning Engineer - Machine Learning Nanodegree Program This blog post has been updated on July 2, 2021. Are you interested in machine learning? You’re not alone! More people are getting interested in machine learning every day. In fact, you’d be hard pressed to find a field generating more buzz these days than this one. Machine learning’s inroa
‐‐ ‐‐ Yin Jun‐Feng (Tongji University) 5 . 6 . 2008 House Open NII • • • • ⎩ ⎨ ⎧ = − = + ) 2 ( 2 3 ) 1 ( 5 3 2 L L y x y x ⎩ ⎨ ⎧ = − = + ) 2 ( 2 3 ) 1 ( 5 3 2 L L y x y x 1 , 1 ) 2 ( 2 3 ) 1 ( 5 3 2 = = ⎩ ⎨ ⎧ = − = + y x y x y x L L 1 , 1 ) 2 ( 2 3 ) 1 ( 5 3 2 = = ⎩ ⎨ ⎧ = − = + y x y x y x L L x y 5 3 2 = + y x 2 3 = − y x ) 1 , 1 ( 0 ⎪ ⎩ ⎪ ⎨ ⎧ = + − = − = + ) 3 ( 1 ) 2 ( 2 3 ) 1 ( 5 3 2 L L L y x
はじめに こんにちは。Machine Learning Advent Calendar 2012 、 12/20 を担当させていただく @kazoo04 です。 普段は(株)ウサギィでエンジニアをやっています。 今日の話 今日は Exact Soft Confidence-Weight Learning (Wang et al, ICML2012) (以下SCW)のご紹介を致します。 SCWは線形オンライン形分類器のひとつで、 学習が高速 オンライン学習 ノイズに強い 精度が良い という素晴らしいアルゴリズムです。 SCWはCWを改良したアルゴリズムですが、本記事ではPerceptronから始まり、PA、CWなどを経てSCWに至るまでの過程とSCWのアルゴリズムについてまとめたいと思います。 数式の表記 すみません、はてなブログを始めたばかりで、ベクトルを太字の立体にする方法がイマイチわか
何か僕がシンガポールに出張している間に妙なニュースが流れていたようで。 京大ビッグデータ副作用論文。機械学習知らない私でも疑問なのは、@sz_drさんも指摘してるが y'=a1*SCORE+a2*ACT+a3*GeneID+b (1) という式で、GeneIDという定量的に性質を示す値でないものを線形結合に加えているところだと思う。詳しい人教えて— torusengoku (@torusengoku) 2016年1月25日 (※記事そのものへのリンクは控えました) 見る人が見れば「ああこれはleakageだな」と一瞥して終わるところなんですが、そもそもleakageってどういうことなのかピンと来ない人もいるかと思いますので、以前取り上げたデータ分析題材を例にとって実演してみようと思います。お題はこちら。 何故これを選んだかというと、このテニス四大大会データには上記で話題になっていた"Gen
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