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A New AI Study May Explain Why Deep Learning Works

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The resurgence of artificial intelligence (AI) is largely due to advances in pattern-recognition due to deep learning, a form of machine learning that does not require explicit hard-coding. The architecture of deep neural networks is somewhat inspired by the biological brain and neuroscience. Like the biological brain, the inner workings of exactly why deep networks work are largely unexplained, and there is no single unifying theory. Recently researchers at the Massachusetts Institute of Technology (MIT) revealed new insights about how deep learning networks work to help further demystify the black box of AI machine learning. The MIT research trio of Tomaso Poggio, Andrzej Banburski, and Quianli Liao at the Center for Brains, Minds, and Machines developed a new theory as to why deep networks work and published their study published on June 9, 2020 in PNAS (Proceedings of the National Academy of Sciences of the United States of America).


Introduction To Recommender Systems- 2: Deep Neural Network Based Recommendation Systems

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It is my second article on the Recommendation systems. In my previous article, I have talked about content-based and collaborative filtering systems. I will encourage you to go through the article if you have any confusion. In this article, we are going to see how Deep Learning is used in Recommender systems. We will go through the recommender system's candidate generation architecture of Youtube.


Using Machine Learning To Automate Data Coding At The Bureau Of Labor Statistics (BLS)

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Government agencies are awash in documents. Many of these documents are paper-based, but even for the electronic documents a human is still often needed to process and understand those documents to make use of them for vital services. Federal agencies are increasingly looking to AI to help improve those document and human-bound processes by applying advanced machine learning, neural network, and natural language processing (NLP) technologies. While for many these technologies might be fairly new in their organization, in some government agencies, they have been using that technology for many years, augmenting and enhancing various workflows and tasks. In the case of the Bureau of Labor Statistics (BLS), the agency is mandated to conduct a Survey of Occupational Injuries and Illnesses to determine workplace injuries and help guide policy.


Fooling deep neural networks for object detection with adversarial 3-D logos – IAM Network

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Examples of the researchers' 3D adversarial logo attack using different 3D object meshes, with the aim of fooling a YOLOV2 detector. Over the past decade, researchers have developed a growing number of deep neural networks that can be trained to complete a variety of tasks, including recognizing people or objects in images. While many of these computational techniques have achieved remarkable results, they can sometimes be fooled into misclassifying data. An adversarial attack is a type of cyberattack that specifically targets deep neural networks, tricking them into misclassifying data. It does this by creating adversarial data that closely resembles and yet differs from the data typically analyzed by a deep neural network, prompting the network to make incorrect predictions, failing to recognize the slight differences between real and adversarial data.


Unsupervised Deep Learning in Python

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Online Courses Udemy Unsupervised Deep Learning in Python, Theano / Tensorflow: Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA Created by Lazy Programmer Inc. Students also bought Advanced AI: Deep Reinforcement Learning in Python Deep Learning: Recurrent Neural Networks in Python Ensemble Machine Learning in Python: Random Forest, AdaBoost Deep Learning: GANs and Variational Autoencoders Deep Learning Prerequisites: Linear Regression in Python Machine Learning and AI: Support Vector Machines in Python Preview this course GET COUPON CODE Description This course is the next logical step in my deep learning, data science, and machine learning series. I've done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? In these course we'll start with some very basic stuff - principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding). Next, we'll look at a special type of unsupervised neural network called the autoencoder.


Fooling Neural Networks by changing just one pixel

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Deep Neural networks, being extremely effective in image classification tasks, can classify images with remarkable accuracy when trained on large enough sample. But in most cases, Deep Neural Networks are used to maximize the accuracy of a classification as a result of which the robustness of the classifier often takes a back seat. As a result of this myriad Neural Network defeating techniques have come into play. These are called adversarial attacks on a Neural Net. One important variant is known as the Fast Gradient sign method, by Ian GoodFellow et al, as seen in the paper Explaining and Harnessing Adversarial Examples.


Understanding Entropy: the Golden Measurement of Machine Learning

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TL;DR: Entropy is a measure of chaos in a system. Because it is much more dynamic than other more rigid metrics like accuracy or even mean squared error, using flavors of entropy to optimize algorithms from decision trees to deep neural networks has shown to increase speed and performance. It appears everywhere in machine learning: from the construction of decision trees to the training of deep neural networks, entropy is an essential measurement in machine learning. Entropy has roots in physics -- it is a measure of disorder, or unpredictability, in a system. For instance, consider two gases in a box: initially, the system has low entropy, in that the two gasses are cleanly separable; after some time, however, the gasses intermingle and the system's entropy increases. It is said that in an isolated system, the entropy never decreases -- the chaos never dims down without external force.


linkedin/detext

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DeText is a Deep Text understanding framework for NLP related ranking, classification, and language generation tasks. It leverages semantic matching using deep neural networks to understand member intents in search and recommender systems. As a general NLP framework, currently DeText can be applied to many tasks, including search & recommendation ranking, multi-class classification and query understanding tasks. A general framework with great flexibility to meet requirement of different production applications. Reaching a good balance between effectiveness and efficiency to meet the industry requirements.


Top 15 Best Machine Learning Frameworks In 2020 - SPEC INDIA

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"Machine intelligence is that last invention that humanity will ever need to make." – Nick Bostrom Machine Learning (ML) is on a roll right now. The world is now understanding the significance of ML and how best suited it is for a technically driven society. Machine Learning that is prevalent today is totally focussed on innovative computing advancements based on pattern recognition. It has been driving technology stalwarts like Google, Facebook, YouTube, Uber Eats, Apple's Siri, Amazon's Alexa and many more. "Machine Learning will automate jobs that most people thought could only be done by people."


Guide to Interpretable Machine Learning

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If you can't explain it simply, you don't understand it well enough. Disclaimer: This article draws and expands upon material from (1) Christoph Molnar's excellent book on Interpretable Machine Learning which I definitely recommend to the curious reader, (2) a deep learning visualization workshop from Harvard ComputeFest 2020, as well as (3) material from CS282R at Harvard University taught by Ike Lage and Hima Lakkaraju, who are both prominent researchers in the field of interpretability and explainability. This article is meant to condense and summarize the field of interpretable machine learning to the average data scientist and to stimulate interest in the subject. Machine learning systems are becoming increasingly employed in complex high-stakes settings such as medicine (e.g. Despite this increased utilization, there is still a lack of sufficient techniques available to be able to explain and interpret the decisions of these deep learning algorithms. This can be very problematic in some areas where the decisions of algorithms must be explainable or attributable to certain features due to laws or regulations (such as the right to explanation), or where accountability is required. The need for algorithmic accountability has been highlighted many times, the most notable cases of which are Google's facial recognition algorithm that labeled some black people as gorillas, and Uber's self-driving car which ran a stop sign. Due to the inability of Google to fix the algorithm and remove the algorithmic bias that resulted in this issue, they solved the problem by removing words relating to monkeys from Google Photo's search engine. This illustrates the alleged black box nature of many machine learning algorithms. The black box problem is predominantly associated with the supervised machine learning paradigm due to its predictive nature. Accuracy alone is no longer enough. Academics in deep learning are acutely aware of this interpretability and explainability problem, and whilst some argue that these models are essentially black boxes, there have been several developments in recent years which have been developed for visualizing aspects of deep neural networks such the features and representations they have learned. The term info-besity has been thrown around to refer to the difficulty of providing transparency when decisions are made on the basis of many individual features, due to an overload of information.