Huseyin ABANOZ

Objective Transfer Learning technique is used when the dataset is not of sufficient size. It is common to fine tune a network which is pre-trained on large datasets like Imagenet for classification tasks. For further information, the reader is advised to refer to CS231N by Standford . A framework for general purpose transfer learning is proposed. This framework is developed for my MSc. thesis study and made publicly available to let researchers make use of it. Using this framework, the researcher will easily be able to fine-tune a network for a classification task. Audience This article can be useful for anyone seeking information about transfer learning implementation. Python knowledge is required to make use of the supplied code. Introductory information about Keras , a deep learning API, is necessary. Also in order to run the code, a proper deep learning system with a decent graphics card (GPU) with CUDA Compute Capability 3.0 or higher   is necessary is necessary. I

Introduction Obfuscation is the act of reorganizing bytecode such that it becomes hard to decompile. Many developers rely on obfuscation to save their sensitive code from undesired eyes. Publishing jars without obfuscation may hinder competitiveness because rivals may take advantage of easily decompilable nature of java binaries. Objective Spring Boot applications make use of public interfaces, annotations which makes applications harder to obfuscate. Additionally, maven Spring Boot plugin creates a fat jar which contains all dependent jars. It is not viable to obfuscate the whole fat jar. Thus obfuscating Spring Boot applications is different than obfuscating regular java applications and requires a suitable strategy. Audience Those who use Spring Boot and Maven and wish to obfuscate their application using Proguard are the target audience for this article. Sample Application As the sample application, I will use elastic search synch application from my GitHub repository.