Neural Networks: Overviews


Practical Text Classification With Python and Keras – Real Python

#artificialintelligence

Imagine you could know the mood of the people on the Internet. Maybe you are not interested in its entirety, but only if people are today happy on your favorite social media platform. After this tutorial, you'll be equipped to do this. While doing this, you will get a grasp of current advancements of (deep) neural networks and how they can be applied to text. Reading the mood from text with machine learning is called sentiment analysis, and it is one of the prominent use cases in text classification. This falls into the very active research field of natural language processing (NLP). Other common use cases of text classification include detection of spam, auto tagging of customer queries, and categorization of text into defined topics. So how can you do this? Free Bonus: 5 Thoughts On Python Mastery, a free course for Python developers that shows you the roadmap and the mindset you'll need to take your Python skills to the next level. Before we start, let's take a look at what data we have. Go ahead and download the data set from the Sentiment Labelled Sentences Data Set from the UCI Machine Learning Repository. By the way, this repository is a wonderful source for machine learning data sets when you want to try out some algorithms. This data set includes labeled reviews from IMDb, Amazon, and Yelp. Each review is marked with a score of 0 for a negative sentiment or 1 for a positive sentiment. With this data set, you are able to train a model to predict the sentiment of a sentence. Take a quick moment to think about how you would go about predicting the data.


A Primer in Adversarial Machine Learning – The Next Advance in AI – Data Science Central

#artificialintelligence

Summary: What comes next after Deep Learning? How do we get to Artificial General Intelligence? Adversarial Machine Learning is an emerging space that points to that direction and shows that AGI is closer than we think. Deep Learning, Convolutional Neural Nets (CNNs) have given us dramatic improvements in image, speech, and text recognition over the last two years. They suffer from the flaw however that they can be easily fooled by the introduction of even small amounts of noise, random or intentional.


Deep Learning in Neuroradiology

#artificialintelligence

SUMMARY: Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging. Because of the high volume and wealth of multimodal imaging information acquired in typical studies, neuroradiology is poised to be an early adopter of deep learning. Compelling deep learning research applications have been demonstrated, and their use is likely to grow rapidly. This review article describes the reasons, outlines the basic methods used to train and test deep learning models, and presents a brief overview of current and potential clinical applications with an emphasis on how they are likely to change future neuroradiology practice. Facility with these methods among neuroimaging researchers and clinicians will be important to channel and harness the vast potential of this new method. Deep learning is a form of artificial intelligence, roughly modeled on the structure of neurons in the brain, which has shown tremendous promise in solving many problems in computer vision, natural language processing, and robotics.1 It has recently become the dominant form of machine learning, due to a convergence of theoretic advances, openly available computer software, and hardware with sufficient computational power. The current excitement in the field of deep learning stems from new data suggesting its excellent performance in a wide variety of tasks. One benchmark of machine learning performance is the ImageNet Challenge. In this annual competition, teams compete to classify millions of images into discrete categories (tens of different kinds of dogs, fish, cars, and so forth).


Top September Stories: Essential Math for Data Science: Why and How; Machine Learning Cheat Sheets

#artificialintelligence

Here are the most popular posts in KDnuggets in September, based on the number of unique page views (UPV), and social share counts from Facebook, Twitter, and Addthis. Most Shareable (Viral) Blogs Among the top blogs, here are the 5 blogs with the highest ratio of shares/unique views, which suggests that people who read it really liked it. You Aren't So Smart: Cognitive Biases are Making Sure of It, by Matthew Mayo A Winning Game Plan For Building Your Data Science Team, by William Schmarzo What on earth is data science?, by Cassie Kozyrkov Everything You Need to Know About AutoML and Neural Architecture Search, by George Seif The Data Science of "Someone Like You" or Sentiment Analysis of Adele's Songs, by Preetish Panda How many data scientists are there and is there a shortage?, by Gregory Piatetsky Neural Networks and Deep Learning: A Textbook, by Charu Aggarwal 5 Resources to Inspire Your Next Data Science Project, by Conor Dewey Hadoop for Beginners, by Aafreen Dabhoiwala 6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study, by John Sullivan Deep Learning for NLP: An Overview of Recent Trends, by Elvis Saravia (*) Ultimate Guide to Getting Started with TensorFlow, by Brian Zhang (*) How many data scientists are there and is there a shortage?, by Gregory Piatetsky Essential Math for Data Science: 'Why' and'How', by Tirthajyoti Sarkar Journey to Machine Learning - 100 Days of ML Code, by Avik Jain You Aren't So Smart: Cognitive Biases are Making Sure of It, by Matthew Mayo Neural Networks and Deep Learning: A Textbook, by Charu Aggarwal (*) You Aren't So Smart: Cognitive Biases are Making Sure of It, by Matthew Mayo How many data scientists are there and is there a shortage?, by Gregory Piatetsky You Aren't So Smart: Cognitive Biases are Making Sure of It, by Matthew Mayo A Winning Game Plan For Building Your Data Science Team, by William Schmarzo What on earth is data science?, by Cassie Kozyrkov Everything You Need to Know About AutoML and Neural Architecture Search, by George Seif The Data Science of "Someone Like You" or Sentiment Analysis of Adele's Songs, by Preetish Panda You Aren't So Smart: Cognitive Biases are Making Sure of It, by Matthew Mayo What on earth is data science?, by Cassie Kozyrkov


A Data-Efficient Framework for Training and Sim-to-Real Transfer of Navigation Policies

arXiv.org Artificial Intelligence

Learning effective visuomotor policies for robots purely from data is challenging, but also appealing since a learning-based system should not require manual tuning or calibration. In the case of a robot operating in a real environment the training process can be costly, time-consuming, and even dangerous since failures are common at the start of training. For this reason, it is desirable to be able to leverage \textit{simulation} and \textit{off-policy} data to the extent possible to train the robot. In this work, we introduce a robust framework that plans in simulation and transfers well to the real environment. Our model incorporates a gradient-descent based planning module, which, given the initial image and goal image, encodes the images to a lower dimensional latent state and plans a trajectory to reach the goal. The model, consisting of the encoder and planner modules, is trained through a meta-learning strategy in simulation first. We subsequently perform adversarial domain transfer on the encoder by using a bank of unlabelled but random images from the simulation and real environments to enable the encoder to map images from the real and simulated environments to a similarly distributed latent representation. By fine tuning the entire model (encoder + planner) with far fewer real world expert demonstrations, we show successful planning performances in different navigation tasks.


End-to-End Content and Plan Selection for Data-to-Text Generation

arXiv.org Artificial Intelligence

Learning to generate fluent natural language from structured data with neural networks has become an common approach for NLG. This problem can be challenging when the form of the structured data varies between examples. This paper presents a survey of several extensions to sequence-to-sequence models to account for the latent content selection process, particularly variants of copy attention and coverage decoding. We further propose a training method based on diverse ensembling to encourage models to learn distinct sentence templates during training. An empirical evaluation of these techniques shows an increase in the quality of generated text across five automated metrics, as well as human evaluation.


The 30-Year Cycle In The AI Debate

arXiv.org Artificial Intelligence

The recent practical successes [26] of Artificial Intelligence (AI) programs of the Reinforcement Learning and Deep Learning varieties in game playing, natural language processing and image classification, are now calling attention to the envisioned pitfalls of their hypothetical extension to wider domains of human behavior. Several voices from the industry and academia are now routinely raising concerns over the advances [49] of often heavily media-covered representatives of this new generation of programs such as Deep Blue, Watson, Google Translate, AlphaGo and AlphaZero. Most of these cutting-edge algorithms generally fall under the class of supervised learning, a branch of the still evolving taxonomy of Machine Learning techniques in AI research. In most cases the implementation choice is artificial neural networks software, the workhorse of the Connectionism school of thought in both AI and Cognitive Psychology. Confronting the current wave of connectionist architectures, critics usually raise issues of interpretability (Can the remarkable predictive capabilities be 1 trusted in real-life tasks? Are these capabilities transferable to unfamiliar situations or to different tasks altogether? How informative are the results about the real world; about human cognition?


Hybrid Active Inference

arXiv.org Artificial Intelligence

We describe a framework of hybrid cognition by formulating a hybrid cognitive agent that performs hierarchical active inference across a human and a machine part. We suggest that, in addition to enhancing human cognitive functions with an intelligent and adaptive interface, integrated cognitive processing could accelerate emergent properties within artificial intelligence. To establish this, a machine learning part learns to integrate into human cognition by explaining away multi-modal sensory measurements from the environment and physiology simultaneously with the brain signal. With ongoing training, the amount of predictable brain signal increases. This lends the agent the ability to self-supervise on increasingly high levels of cognitive processing in order to further minimize surprise in predicting the brain signal. Furthermore, with increasing level of integration, the access to sensory information about environment and physiology is substituted with access to their representation in the brain. While integrating into a joint embodiment of human and machine, human action and perception are treated as the machine's own. The framework can be implemented with invasive as well as non-invasive sensors for environment, body and brain interfacing. Online and offline training with different machine learning approaches are thinkable. Building on previous research on shared representation learning, we suggest a first implementation leading towards hybrid active inference with non-invasive brain interfacing and state of the art probabilistic deep learning methods. We further discuss how implementation might have effect on the meta-cognitive abilities of the described agent and suggest that with adequate implementation the machine part can continue to execute and build upon the learned cognitive processes autonomously.


AI Benchmark: Running Deep Neural Networks on Android Smartphones

arXiv.org Artificial Intelligence

Over the last years, the computational power of mobile devices such as smartphones and tablets has grown dramatically, reaching the level of desktop computers available not long ago. While standard smartphone apps are no longer a problem for them, there is still a group of tasks that can easily challenge even high-end devices, namely running artificial intelligence algorithms. In this paper, we present a study of the current state of deep learning in the Android ecosystem and describe available frameworks, programming models and the limitations of running AI on smartphones. We give an overview of the hardware acceleration resources available on four main mobile chipset platforms: Qualcomm, HiSilicon, MediaTek and Samsung. Additionally, we present the real-world performance results of different mobile SoCs collected with AI Benchmark that are covering all main existing hardware configurations.