Understand TensorFlow by mimicking its API from scratch

TensorFlow is a very powerful and open source library for implementing and deploying large-scale machine learning models. This makes it perfect for research and production. Over the years it has become one of the most popular libraries for deep learning. The goal of this post is to build an intuition and understanding for how deep learning libraries work under the hood, specifically TensorFlow. To achieve this goal, we will mimic its API and implement its core building blocks from scratch. This has the neat little side effect that, by the end of this post, you will be able to use TensorFlow with confidence, because you’ll have a deep conceptual understanding of the inner workings. You will also gain further understanding of things like variables, tensors, sessions or operations. So let’s get started, shall we?

A New Approach to Understanding How Machines Think

Neural networks are famously incomprehensible – a computer can come up with a good answer, but not be able to explain what led to the conclusion. Been Kim is developing a ‘translator for humans’ so that we can understand when artificial intelligence breaks down.

Taking Human out of Learning Applications: A Survey on Automated Machine Learning

Machine learning techniques have deeply rooted in our everyday life. However, since it is knowledge- and labor-intensive to pursue good learning performance, human experts are heavily involved in every aspect of machine learning. In order to make machine learning techniques easier to apply and reduce the demand for experienced human experts, automated machine learning (AutoML) has emerged as a hot topic with both industrial and academic interest. In this paper, we provide an up to date survey on AutoML. First, we introduce and define the AutoML problem, with inspiration from both realms of automation and machine learning. Then, we propose a general AutoML framework that not only covers most existing approaches to date but also can guide the design for new methods. Subsequently, we categorize and review the existing works from two aspects, i.e., the problem setup and the employed techniques. Finally, we provide a detailed analysis of AutoML approaches and explain the reasons underneath their successful applications. We hope this survey can serve as not only an insightful guideline for AutoML beginners but also an inspiration for future research.