Hyperparameter optimization is a big part of deep learning. The reason is that neural networks are notoriously difficult to configure and there are a lot of parameters that need to be set. On top of that, individual models can be very slow to train.…
斯坦福课程CS224d: Deep Learning for Natural Language Processing lecture13:Convolutional neural networks -- for sentence classification 主要是学习笔记,卷积神经网络(CNN),因为其特殊的结构,在图像处理和语音识别方面都有很出色的表现.这里主要整理CNN在自然语言处理的应用和现状. 一.RNNs to CNNs 学过前面lecture的朋友,应该比较清楚.RNNs一般只能获…
Deep Learning Research Review Week 2: Reinforcement Learning 转载自: https://adeshpande3.github.io/adeshpande3.github.io/Deep-Learning-Research-Review-Week-2-Reinforcement-Learning This is the 2nd installment of a new series called Deep Learning Resea…
Explaining Titanic hypothesis with decision trees decision trees are very simple yet powerful supervised learning methods, which constructs a decision tree model, which will be used to make predictions. The main advantage of this model is that a huma…
FireCaffe Forrest N. Iandola FireCaffe: near-linear acceleration of deep neural network training on computer clusters 2016.1 Problem statements from data scientists 4 key pain points summarized by Jeff Dean from Google: 1. DNN researchers and users w…
转载 - Recurrent Neural Network Tutorial, Part 4 – Implementing a GRU/LSTM RNN with Python and Theano The code for this post is on Github. This is part 4, the last part of the Recurrent Neural Network Tutorial. The previous parts are: Recurrent Neural…
2.1. Binary Variables 1. Bernoulli distribution, p(x = 1|µ) = µ 2.Binomial distribution + 3.beta distribution(Conjugate Prior of Bernoulli distribution) The parameters a and b are often called hyperparameters because they control the distribution of…
colah's blog Blog About Contact Neural Networks, Manifolds, and Topology Posted on April 6, 2014 topology, neural networks, deep learning, manifold hypothesis Recently, there’s been a great deal of excitement and interest in deep neural networks beca…
Let's make a DQN 系列 Let's make a DQN: Theory September 27, 2016DQN This article is part of series Let's make a DQN. 1. Theory2. Implementation3. Debugging4. Full DQN5. Double DQN and Prioritized experience replay (available soon) Introduction In Febr…
HOME ABOUT CONTACT SUBSCRIBE VIA RSS DEEP LEARNING FOR ENTERPRISE Distributed Deep Learning, Part 1: An Introduction to Distributed Training of Neural Networks Oct 3, 2016 3:00:00 AM / by Alex Black and Vyacheslav Kokorin Tweet inShare27 This pos…
Byte Tank Posts Archive Deep Reinforcement Learning: Playing a Racing Game OCT 6TH, 2016 Agent playing Out Run, session 201609171218_175epsNo time limit, no traffic, 2X time lapse Above is the built deep Q-network (DQN) agent playing Out Run, trained…
Adit Deshpande CS Undergrad at UCLA ('19) Blog About Resume Deep Learning Research Review Week 1: Generative Adversarial Nets Starting this week, I’ll be doing a new series called Deep Learning Research Review. Every couple weeks or so, I’ll be summa…
A Beginner's Guide To Understanding Convolutional Neural Networks Introduction Convolutional neural networks. Sounds like a weird combination of biology and math with a little CS sprinkled in, but these networks have been some of the most influential…
http://deeplearning4j.org/lstm.html A Beginner’s Guide to Recurrent Networks and LSTMs Contents Feedforward Networks Recurrent Networks Backpropagation Through Time Vanishing and Exploding Gradients Long Short-Term Memory Units (LSTMs) Capturing Dive…
The Joys of Conjugate Priors (Warning: this post is a bit technical.) Suppose you are a Bayesian reasoning agent. While going about your daily activities, you observe an event of type . Because you're a good Bayesian, you have some internal paramet…
PRML Chapter 2. Probability Distributions P68 conjugate priors In Bayesian probability theory, if the posterior distributions p(θ|x) are in the same family as the prior probability distributionp(θ), the prior and posterior are then called conjugate d…