Category Archives: So what

popular technologies in deep learning

For more info : NN for Language

Presenter : Juno

This seminar introduces several popular technologies in deep learning or neural network for natural language processing such as distributed representation of word, neural network language model, recurrent neural network, and LSTM (Long short term memory). In particular, recent advances and a couple of application examples will also be discussed in the last part.

- Reference: Using Neural Network for Modeling  and Representing Natural Languages by Tomas Mikolov (Facebook)
- Reference: Recurrent Nets and LSTM by Nando de Freitas (Oxford Univ.)

Now we are in PARTNERSHIP with various educational institutions !


EDGE WRITINGS와  연세대학교, 아주대학교 학생복지처 장학취업팀이 함께

재학생 및 취업 준비생에게 도움을 주기 위해 ,

영문 이력서, 자기소개서 등의  영문첨삭을 제공하게 되었습니다.

또한,  총 9개의 미국 내 교육기관 및 단체와 제휴를 맺고 서비스를 제공할 예정입니다.

제휴 대상에는 미국 대표 공교육 지원 비영리단체로 알려진

TFA(Teach for America)를 비롯해 뉴욕 할렘에서 한국식 교육의 도입으로 화제가 됐던

공립학교인 Democracy Prep, 저소득층 가정의 인재를 발굴해

체계적인 교육을 실천하고 있는 TEAK Foundation 등이 포함되어 있습니다!

전세계  학생들의 영어교육을 위한 라이팅센터로

자리매김할 수 있도록 노력하겠습니다 !



Deep Learning

For more info : DEEP LEARNING.compressed

Reference :  (

Presenter : Juno

This seminar introduces Deep learning, which has been a buzz word in machine learning area for recent years. Since it is based on neural network, the seminar first introduces the basic theory and history of Neural network and explains why Neural network failed in 1990s and early 2000s. Unsupervised way of feature learning, is then followed to describe the core of Deep learning. The latter half of the seminar focuses on the algorithms that learn features from data in an unsupervised way. As a result, main algorithms such as Hopfield Net, Boltzmann machine, and Restricted Boltzmann machine will be discussed.


Brief Introduction to H/W Design

For more info : Brief Introduction to H_W Design_FORAMY

Presenter : Neil

I first introduced the most essential circuit elements for developing the hardware device and then explained how this element works.

Then I explained the two ways in which the signal runs through the circuit, how the aforementioned elements control the two signals, and how it applies practically.

And finally, with the introduction of the theories and additional information for the planning and development of the hardware, I have explained how to develop applications for the Wand product which I have created.

I have introduced all of the development sources and programs to our colleagues.

However, I have been deleted from our company blog to prevent improper use.


A Basic Tutorial to Logistic Regression

For more info  : log

presenter : Jay

The seminar covered the basics of logistic regression, which is the simplest method for classification. After going through an numeric example and the principle of maximum likelihood, an overview of logistic regression was given in relationship with other algorithms.The following was shown:
1. The equivalence between the Maximum Entropy algorithm and logistic regression
2. The equivalence between Naive bayes and logistic regression
3. How Conditional Random Fields are basically logistic regression with selective mapping of feature functions
Conclusively, it was shown that logistic regression is a simple algorithm which works well when classifying linearly separable data with a reasonable running time.