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摘要: 粒子群算法(Particle swarm optimization,PSO)是模擬群體智能所建立起來的一種優化算法,主要用於解決最優化問題(optimization problems)。 1995年由 Eberhart和Kennedy 提出,是基於對鳥群覓食行為的研究和模擬而來的。

摘要: In this post we will go through the mathematics of machine learning and code from scratch, in Python, a small library to build neural networks with a variety of layers (Fully Connected, Convolutional, etc.). Eventually, we will be able to create networks in a modular fashion

摘要: 目前,深度學習模型需要大量算力,內存和電量。當我們需要執行實時推斷,在設備端運行資源有限的情況下運行瀏覽器時,這就是瓶頸。能耗是人們對於當前學習模型的主要擔憂。而解決這一問題的方法之一是提高推斷效率。

摘要: 在摩爾定律的暮色中,GPU和其他硬件加速器極大地加速了神經網絡的訓練。但是隨著加速器的不斷改進,前期階段所花費的時間將逐漸成為訓練速度的瓶頸。谷歌大腦團隊提出了“數據回送(數據呼應)”算法,在部分情況下,數據回送甚至可以將訓練速度提升4倍。

摘要: Here are some articles from Microsoft regarding cloud data science products and updates. This week includes IoT hubs, Time Series Insights, Deep Learning Virtual Machine, Python sample code for cognitive services, and more.

摘要: Latent Profile Analysis (LPA) tries to identify clusters of individuals (i.e., latent profiles) based on responses to a series of continuous variables (i.e., indicators). LPA assumes that there are unobserved latent profiles that generate patterns of responses on indicator items.