What is Deep Learning ??

This article basically just a translation version of the article of deep learning previously in bahasa Indonesia.

This time I want to share about what is deep learning, at least as far as I have learned untul this day (when this article was made). Before I have tried to created a startup company that tried to implement machine learning and deep learning algorithm engine that we made (with my friend).

Many references that we can use on deep learning, especially from big companies such as google, facebook, Baidu, microsoft, amazon, nvidia and others. What was Deep Learning? how important or how valuable deep learning? especially for business, who figures that a lot of research or build deep learning? And why me and my friends want to build their own engine for machine learning before? not a lot of framework, libraries and services (especially such as azure and aws) for machine learning? (when this article was made)

I will not answer all of the questions above, because it will take time to write hahaha, I write as my fingers Moves hahaha Continue reading

Backpropagation and Deep Neural Network engine

After discuss with my friend Eko kurniawan, finally we share our deep learning engine project to github. So you can try the engine or contribute to develop the engine (we hope there are a lot of people interested with this project).

Our project started in 2015 when we tried to create a startup company that concern with data analytics or data science. We trying to create the general engine for deep neural network that able to customize the stack of methods, so can be fit with some cases in the real world. And I used this engine for my theses in ITB (Institut Teknologi Bandung) to finished my study in master of informatics (computer science) program.

I hope this engine can be use to resolve a lot of problem that need machine learning implementation, especially for automation system. We call this project “DEEPWISSEN”, hope it will be usefull.

*before, I have explained about Deep Learning in an article in this blog using Bahasa Indonesia :  https://situkangsayur.wordpress.com/2015/07/27/deep-learning/

or in english : https://situkangsayur.wordpress.com/2016/12/25/what-is-deep-learning/


You can check the project repo in this link : https://github.com/situkangsayur/machine-learning

and for the jar : https://github.com/situkangsayur/deepwissen-jar

Apa itu Deep Learning ??


Kali ini saya ingin share mengenai apa itu deep learning, setidaknya sejauh yang saya pelajari hingga hari ini, kebetulan di startup yang kami bangun (bareng teman-teman saya) di starlabs.id (PT. Starlabs Global Teknologi) sedang mencoba mengimplementasikan engine untuk machine learning dan dua algoritma deep learning.

Banyak referensi yang bisa kita gunakan mengenai deep learning, terutama dari berbagai perusahaan besar dunia sekelas google, facebook, baidu, microsoft, amazon, nvidia dan lain-lain. Apa itu Deep Learning? seberapa penting atau seberapa bernilai deep learning? terutama untuk business, siapa tokoh yang banyak melakukan riset atau membangun deep learning? Dan kenapa saya dan teman-teman ingin membangun engine sendiri untuk machine learning? bukankah banyak framework, library dan services (terutama seperti azure dan aws) untuk machine learning? Continue reading

Bayesian Belief Network


Kali ini saya ingin mencoba share mengenai bayesian belief network, suatu metode dalam machine learning yang masuk ke dalam kategori supervised learning. Pada dasarnya belief network mengambil teori dasar bayessian, yang memanfaatkan distribusi probabilitas pada setiap features yang diketahui. Namun yang membedakan adalah dalam belief network menggunakan suatu network yang merepresentasikan kondisi pengetahuan dependensi dan in-dependensi setiap features yang ada dalam suatu kasus atau dataset. Berbeda dengan naive bayes yang tidak melihat kemungkinan dependensi dan in-dependensi setiap features (attributes), dan itu dapat dilihat dari representasi metode yang digunakan naive bayes. Tentunya dengan melihat model yang dihasilkan adalah berupa inferences yaitu distribusi probabilitas setiap features dalam network maka belief network dapat menjadi alternatif metode yang lebih baik dibanding naive bayes yang tidak melihat kemungkinan tersebut. Continue reading