Markov Chain Markov Process is a kind of random process. The main idea is given the current state of the system, its future state does not depend on its past states.
Author Archives: Irene
PGM 01: Bayesian Networks
lol I started the difficult course of Probabilistic Graphic Models on Coursera.
Deep Learning 10: Sequence Modeling
Learning notes for Lecture 7 Modeling sequences: A brief overview. by Geoffrey Hinton [1]
Deep Learning 09: Small Tricks(2)
Let’s try some ways to speedup our learning!
Deep Learning 08: Small Tricks(1)
I was optimizing my code for the ConvNet these days. Not all the methods are doing good, because I do not have very strong knowledge on the hyper-parameters. Anyway, just write something I’ve learnt here.
TensorFlow 04 : Implement a LeNet-5-like NN to classify notMNIST Images
The blog is a solution of Udacity DL Assignment 4, using a CNN to classify notMNIST images. Visit here to get a full version of my codes.
Lucky or not: Monte Carlo Method
AlphaGo! http://c.brightcove.com/services/viewer/federated_f9?isVid=1&isUI=1 When you play any games, probably you have strategies or experiences. But you could not deny that some times you need luck, which data scientists would say a “random choice”. Monte Carlo Method provides only an approximate optimizer, thus giving you the luck to win a game.
Deep Learning 07: Are you talking to a machine?
Recently working on a shared task job of image annotation. An interesting paper saw on NIPS’15 was proposed by Baidu Research. Find paper here . Official website. This post is the study notes.
Install MPI on Windows, Mac and Ubuntu
（If you meet any errors and need help, please leave them in the comments.）
Deep Learning 06: R-CNN for Object Detection
This post is a learning notes from this paper: Rich feature hierarchies for accurate object detection and semantic segmentation
IreneLi 