Collaborating Authors

Top 10 Deep Learning Models that will Help Make Advanced AI


Deep learning has gained massive popularity in scientific computing, and deep learning models are widely used by industries that solve complex problems. Deep learning uses artificial neural networks to perform sophisticated computations on large amounts of data. It is a type of machine learning that works based on the structure and function of the human brain. Deep learning has offered noteworthy capabilities and advances in voice recognition, image comprehension, self-driving car, natural language procession, search engine optimization, and more. Understanding AI has become one of the most demanded skills across the industry.

Deep Learning & Neural Networks Python - Keras : For Dummies


Hi this is Abhilash Nelson and I am thrilled to introduce you to my new course Deep Learning and Neural Networks using Python: For Dummies. The world has been revolving much around the terms "Machine Learning" and "Deep Learning" recently. With or without our knowledge every day we are using these technologies. There are tons of other applications too. No wonder why "Deep Learning" and "Machine Learning along with Data Science" are the most sought after talent in the technology world now a days.

Building your First Neural Network on a Structured Dataset (using Keras)


Have you ever applied a neural network model on a structured dataset? If the answer is no, which of the following reasons are applicable for you? In this article, I will focus on the first three reasons and showcase how easily you can apply a neural network model on a structured dataset using a popular high-level library - "keras". We will work on the Black Friday dataset in this article. It is a regression challenge where we need to predict the purchase amount of a customer against various products.

Hybrid Neural Models For Sequence Modelling: The Best Of Three Worlds Machine Learning

We propose a neural architecture with the main characteristics of the most successful neural models of the last years: bidirectional RNNs, encoder-decoder, and the Transformer model. Evaluation on three sequence labelling tasks yields results that are close to the state-of-the-art for all tasks and better than it for some of them, showing the pertinence of this hybrid architecture for this kind of tasks.

Deep Neural Network Fingerprinting by Conferrable Adversarial Examples


In Machine Learning as a Service, a provider trains a deep neural network and provides many users access to it. However, the hosted (source) model is susceptible to model stealing attacks where an adversary derives a surrogate model from API access to the source model. For post hoc detection of such attacks, the provider needs a robust method to determine whether a suspect model is a surrogate of their model or not. We propose a fingerprinting method for deep neural networks that extracts a set of inputs from the source model so that only surrogates agree with the source model on the classification of such inputs. These inputs are a specifically crafted subclass of targeted transferable adversarial examples which we call conferrable adversarial examples that transfer exclusively from a source model to its surrogates.