This page is currently not much more than an extended advertisment for doing content analysis in Python. In time it might expand to a full tutorial, should anyone express interest in reading one. The scripts presented here are not intended to teach programming; I assume you have at least a vague idea about that already. Nor are they intended to exemplify fine coding style. The point is to show how easy things can be, if you pick the right tools. The concept of identity seems to be all the rage now in the social sciences.

We know that Artificial Intelligence (AI) is the main force moving the society into the future described in the movies in the past couple of decades. There are new heights achieved every day in different fields using Artificial Intelligence methods. Artificial Intelligence is a huge field and contains a lot of subfields, that contain a lot of subfields themselves. There are huge amounts of sources that claim that can help you learn Artificial Intelligence. These sources come in different forms and types, from books, blogs, projects, videos, etc. Today we are going to talk about the video sources, more precisely YouTube videos.

The architecture behind Michelangelo uses a modern but complex stack based on technologies such as HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

We can't hang out somewhere nice, it's been so long since we last met our friends or families who are stuck overseas and we can't hope for a major change or revolution because the future is that uncertain and this uncertainty just grows every day. It's miserable and sad to see the current state people are in, but kudos to the people who are fighting with their or their loved ones' lives every day, struggling in these trying times, and to the front liners in the health sector who are trying their best to put every possible effort to provide and save the lives of the masses.

"Whenever you compete, you have to accept simple rules – someone wins, someone loses, and usually the winner takes it all." For this week's ML practitioner's series, Analytics India Magazine got in touch with Oleg Yaroshevskiy from Ukraine. In this interview, he shares his experiences from his journey to the top 20 in one of the toughest data science competitions in the world. Oleg majored in maths and statistics from Cybernetics Faculty of Taras Shevchenko National University of Kyiv, which was co-founded by Victor Glushkov, one of the cybernetics pioneers who played a key role in the advancement of theoretical computer science, including artificial intelligence. Oleg had a formal introduction to machine learning (ML) during his graduation days where he had studied neural networks along with the popular Andrew NG's course on Coursera back in 2013.

You may have also used or heard of their equally high quality deep learning, machine learning, linear algebra, and natural language processing courses. It has also been a major protagonist in the development of transfer learning for natural language processing; performed an investigation and evaluation of the research into the use of face masks for suppressing the spread of COVID-19; been a voice at the forefront of applied data ethics. This library provides easier API access to a variety of machine learning-related functionality, especially when it comes to neural networks. Much of this aspect of the library sits atop PyTorch, making the creation of neural networks with this lower level library easier and flexible for machine learning coders of all skill levels. As a bridge between their courseware and the fastai library which it uses, Jeremey Howard and Sylvain Gugger are working on a book titled Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD, which is not yet available.

The choice of a proper learning rate is paramount for good Artificial Neural Network training and performance. In the past, one had to rely on experience and trial-and-error to find an adequate learning rate. Presently, a plethora of state of the art automatic methods exist that make the search for a good learning rate easier. While these techniques are effective and have yielded good results over the years, they are general solutions. This means the optimization of learning rate for specific network topologies remains largely unexplored. This work presents AutoLR, a framework that evolves Learning Rate Schedulers for a specific Neural Network Architecture using Structured Grammatical Evolution. The system was used to evolve learning rate policies that were compared with a commonly used baseline value for learning rate. Results show that training performed using certain evolved policies is more efficient than the established baseline and suggest that this approach is a viable means of improving a neural network's performance.

As robots become ubiquitous in the workforce, it is essential that human-robot collaboration be both intuitive and adaptive. A robot's quality improves based on its ability to explicitly reason about the time-varying (i.e. learning curves) and stochastic capabilities of its human counterparts, and adjust the joint workload to improve efficiency while factoring human preferences. We introduce a novel resource coordination algorithm that enables robots to explore the relative strengths and learning abilities of their human teammates, by constructing schedules that are robust to stochastic and time-varying human task performance. We first validate our algorithmic approach using data we collected from a user study (n = 20), showing we can quickly generate and evaluate a robust schedule while discovering the latest individual worker proficiency. Second, we conduct a between-subjects experiment (n = 90) to validate the efficacy of our coordinating algorithm. Results from the human-subjects experiment indicate that scheduling strategies favoring exploration tend to be beneficial for human-robot collaboration as it improves team fluency (p = 0.0438), while also maximizing team efficiency (p < 0.001).

Although few-shot learning and one-class classification (OCC), i.e. learning a binary classifier with data from only one class, have been separately well studied, their intersection remains rather unexplored. Our work addresses the few-shot OCC problem and presents a method to modify the episodic data sampling strategy of the model-agnostic meta-learning (MAML) algorithm to learn a model initialization particularly suited for learning few-shot OCC tasks. This is done by explicitly optimizing for an initialization which only requires few gradient steps with one-class minibatches to yield a performance increase on class-balanced test data. We provide a theoretical analysis that explains why our approach works in the few-shot OCC scenario, while other meta-learning algorithms, including MAML, fail. Our experiments on eight datasets from the image and time-series domains show that our method leads to higher results than classical OCC and few-shot classification approaches, and demonstrate the ability to learn unseen tasks from only few normal class samples. Moreover, we successfully train anomaly detectors for a real-world application on sensor readings recorded during industrial manufacturing of workpieces with a CNC milling machine using few normal examples. Finally, we empirically demonstrate that the proposed data sampling technique increases the performance of more recent meta-learning algorithms in few-shot OCC.

Messenger advertisements (ads) give direct and personal user experience yielding high conversion rates and sales. However, people are skeptical about ads and sometimes perceive them as spam, which eventually leads to a decrease in user satisfaction. Targeted advertising, which serves ads to individuals who may exhibit interest in a particular advertising message, is strongly required. The key to the success of precise user targeting lies in learning the accurate user and ad representation in the embedding space. Most of the previous studies have limited the representation learning in the Euclidean space, but recent studies have suggested hyperbolic manifold learning for the distinct projection of complex network properties emerging from real-world datasets such as social networks, recommender systems, and advertising. We propose a framework that can effectively learn the hierarchical structure in users and ads on the hyperbolic space, and extend to the Multi-Manifold Learning. Our method constructs multiple hyperbolic manifolds with learnable curvatures and maps the representation of user and ad to each manifold. The origin of each manifold is set as the centroid of each user cluster. The user preference for each ad is estimated using the distance between two entities in the hyperbolic space, and the final prediction is determined by aggregating the values calculated from the learned multiple manifolds. We evaluate our method on public benchmark datasets and a large-scale commercial messenger system LINE, and demonstrate its effectiveness through improved performance.