We propose design guidelines for a probabilistic programming facility suitable for deployment as a part of a production software system. As a reference implementation, we introduce Infergo, a probabilistic programming facility for Go, a modern programming language of choice for server-side software development. We argue that a similar probabilistic programming facility can be added to most modern general-purpose programming languages. Probabilistic programming enables automatic tuning of program parameters and algorithmic decision making through probabilistic inference based on the data. To facilitate addition of probabilistic programming capabilities to other programming languages, we share implementation choices and techniques employed in development of Infergo. We illustrate applicability of Infergo to various use cases on case studies, and evaluate Infergo's performance on several benchmarks, comparing Infergo to dedicated inference-centric probabilistic programming frameworks.

At Accelirate, an automation startup, few newcomers to the IT staff claim to be experts in critical areas like robotic process automation (RPA) or machine learning, but everyone has the chance to become one. The Edison, N.J.-based company, which was launched last year to assist companies on the automation track, is now up to 120 employees, 90% residing in IT, and it has debuted on Computerworld's annual Best Places to Work in IT list as the No. 11 small organization. Since RPA and related technologies are treading new ground, Accelirate found itself facing a dearth of expert talent, which could put a damper on its plan for fast-paced growth. The solution: building an in-house, three-month training program that gets all new IT hires, both first-time job holders and seasoned veterans, quickly up to speed. "Not too many people have prior experience with the platforms or technologies we were working with -- finding someone who'd done RPA before was few and far between," says Ahmed Zaidi, Accelirate's chief automation officer.

During your application development workflow, there is always a moment when you need to get out of a tight spot. Through a recipe-based approach, this book will help you overcome common programming problems and get your applications ready to face the modern world. We start with C# 6, giving you hands-on experience with the new language features. Next, we work through the tasks that you perform on a daily basis such as working with strings, generics, and lots more. Gradually, we move on to more advanced topics such as the concept of object-oriented programming, asynchronous programming, reactive extensions, and code contracts.

None of the candidates could give a satisfactory answer. May be, they thought becoming a data scientist has nothing to do with following them. Think back, when you were a kid and played sports, didn't you admire any sports player and aimed to be like him / her, when you grow up? The path to becoming a data scientist is exhausting, just like a marathon. To ensure you don't fall out, it is important that you keep seeking motivation from what others are doing.

The workshop will be held at Casa Paganini, InfoMus Research Centre, Piazza di Santa Maria in Passione, 34 – Genoa (Italy). The cost is € 80,00 for each participant. Registration closes on July 1st, 2019. Due to the limited availability of seats, early registration is strongly recommended to ensure participation. Please note that your registration is completed only after the registration form and payment are received.

We propose a lifelong learning architecture, the Neural Computer Agent (NCA), where a Reinforcement Learning agent is paired with a predictive model of the environment learned by a Differentiable Neural Computer (DNC). The agent and DNC model are trained in conjunction iteratively. The agent improves its policy in simulations generated by the DNC model and rolls out the policy to the live environment, collecting experiences in new portions or tasks of the environment for further learning. Experiments in two synthetic environments show that DNC models can continually learn from pixels alone to simulate new tasks as they are encountered by the agent, while the agents can be successfully trained to solve the tasks using Proximal Policy Optimization entirely in simulations.

Machine learning, artificial intelligence (ML & AI) and big data form up a new niche area that is seeing a fast-paced growth rate in India. To clarify terminologies for a layperson, AI is basically all about mimicking human intelligence in machines, ML is a sub-set of AI and is about techniques that enable these machines to continuously learn on their own through data and perform a desired set of processes. Big Data analytics is about extracting huge data and observing unanticipated patterns from the same, while ML uses the same to provide incremental data/information to help the machine learn on its own. Data science and big data industry in India is growing at 33per cent CAGR (Compounded annual growth rate) and stood at $2.71 Billion in 2018. While the Finance & Banking industry leads the share in the analytics market, travel-hospitality and healthcare saw the fastest growth in recent years, in terms of analytics-use.

Machine learning develops rapidly, which has made many theoretical breakthroughs and is widely applied in various fields. Optimization, as an important part of machine learning, has attracted much attention of researchers. With the exponential growth of data amount and the increase of model complexity, optimization methods in machine learning face more and more challenges. A lot of work on solving optimization problems or improving optimization methods in machine learning has been proposed successively. The systematic retrospect and summary of the optimization methods from the perspective of machine learning are of great significance, which can offer guidance for both developments of optimization and machine learning research. In this paper, we first describe the optimization problems in machine learning. Then, we introduce the principles and progresses of commonly used optimization methods. Next, we summarize the applications and developments of optimization methods in some popular machine learning fields. Finally, we explore and give some challenges and open problems for the optimization in machine learning.

Curriculum learning is often employed in deep reinforcement learning to let the agent progress more quickly towards better behaviors. Numerical methods for curriculum learning in the literature provides only initial heuristic solutions, with little to no guarantee on their quality. We define a new gray-box function that, including a suitable scheduling problem, can be effectively used to reformulate the curriculum learning problem. We propose different efficient numerical methods to address this gray-box reformulation. Preliminary numerical results on a benchmark task in the curriculum learning literature show the viability of the proposed approach.

Throughout this article, I will discuss some of the more complex aspects of convolutional neural networks and how they related to specific tasks such as object detection and facial recognition. This article is a natural extension to my article titled: Simple Introductions to Neural Networks. I recommend looking at this before tackling the rest of this article if you are not well-versed in the idea and function of convolutional neural networks. Due to the excessive length of the original article, I have decided to leave out several topics related to object detection and facial recognition systems, as well as some of the more esoteric network architectures and practices currently being trialed in the research literature. I will likely discuss these in a future article related more specifically to the application of deep learning for computer vision.