Machine Learning is incredibly exciting and it's not just science fiction. This 5-class series is a practical, hands-on dive into machine learning, after which you will be ready to deliver immediate value to any organization. You will learn by doing, and create, optimize and debug your own models. Your time is precious - our classes are fast-paced and cover as much material as possible.

It feels impossible to keep up with every new concept and technology in data science and machine learning. You have multiple languages, libraries and design principles. We have written pieces on different resources that can help data professionals keep up to date with all the various technologies. However, many of these courses cost money. But coursera offers an opportunity to take online courses for free from actual colleges and educational institutions.

T raditionally, mathematical formulations of dynamical systems in the context of Signal Processing and Control Theory have been a lynchpin of today's Financial Engineering. More recently, advances in sequential decision making, mainly through the concept of Reinforcement Learning, have been instrumental in the development of multistage stochastic optimization, a key component in sequential portfolio optimization (asset allocation) strategies. In this thesis, we develop a comprehensive account of the expressive power, modelling efficiency, and performance advantages of so called trading agents (i.e., Deep Soft Recurrent Q-Network (DSRQN) and Mixture of Score Machines (MSM)), based on both traditional system identification (model-based approach) as well as on context-independent agents (model-free approach). The analysis provides a conclusive support for the ability of model-free reinforcement learning methods to act as universal trading agents, which are not only capable of reducing the computational and memory complexity (owing to their linear scaling with size of the universe), but also serve as generalizing strategies across assets and markets, regardless of the trading universe on which they have been trained. The relatively low volume of daily returns in financial market data is addressed via data augmentation (a generative approach) and a choice of pre-training strategies, both of which are validated against current state-of-the-art models. For rigour, a risk-sensitive framework which includes transaction costs is considered, and its performance advantages are demonstrated in a variety of scenarios, from synthetic time-series (sinusoidal, sawtooth and chirp waves), ii simulated market series (surrogate data based), through to real market data (S&P 500 and EURO STOXX 50). The analysis and simulations confirm the superiority of universal model-free reinforcement learning agents over current portfolio management model in asset allocation strategies, with the achieved performance advantage of as much as 9.2% in annualized cumulative returns and 13.4% in annualized Sharpe Ratio.

A strong submission to the workshop typically presents a new learning method that yields new biological insights, or applies an existing learning method to a new biological problem. However, submissions that improve upon existing methods for solving previously studied problems will also be considered. Examples of research presented in previous years can be found online at http://raetschlab.org:10080/nipscompbio/previous. We specially encourage submissions describing work in progress and early results, for generating discussions helpful in shaping the presented work.

The loss function for variational graph autoencoder is pretty much the same as before. The first part is the reconstruction loss between the input adjacency matrix and the reconstructed adjacency matrix. More specifically, it is the binary cross-entropy between the target (A) and output (A') logits. The second part is the KL-divergence between q (Z X, A) and p(Z), where p(Z) N(0,1). It measures how closely our q (Z X, A) matches to p(Z).

Continuing on my #100DaysOfMLCode, these are some of the courses I'm following and recommend if you are interested in learning ML and DL. One of the most stunning statistics in the area of Machine Learning (ML) was released by Tractica. According to the company, ML will grow from its $1.4 billon value as at 2016 to $59.8 billion by 2025. That is some massive growth to be recorded in just under ten years. The interesting thing is, not many are taking advantage of this market yet.

BENGALURU: The Central Board of Secondary Education (CBSE) had earlier announced the introduction of Artificial Intelligence (AI) as an elective for students in classes 9 to 12. The curriculum for the subject is now being developed from scratch by a team from IBM India along with members of its global team and other subject experts. To begin with, IBM will conduct a pilot project in 1,000 schools in Bengaluru, Delhi, Kolkata, Bhubaneswar, Hyderabad and Chennai, before finalising the curriculum and embedding it in the CBSE curriculum from the next academic year. The pilot is being launched in Delhi on Wednesday. The project will start with creating awareness for school principals, followed by a two-and-a half day training of teachers on the foundational skills for the subject.

This book is a superb practical guide for any developer considering refactoring their code with Visual Studio. Its accessible approach covers all aspects of updating your C# code base to make it maintainable and adaptable. Overview Make your code base maintainable with refactoring Support new features more easily by making your system adaptable Enhance your system with an improved object-oriented design and increased encapsulation and componentization Concepts are presented in a comfortable one-on-one, pair-programming style A practical approach that's packed with examples to enrich understanding What you will learn from this book Improve code readability by applying effective concepts and techniques Spot discrepancies in code with code smells Catch and eliminate dead code and learn several such smart methods Make changes to your code without introducing adverse side-effects with code maintainability Learn smart ways to improve code navigability Improve your design by applying design principles and methodologies Enhance the maintainability of your code base by refactoring it to be more cohesive and less coupled Obtain an optimal level of flexibility by refactoring your code to a loosely-coupled design Minimize the side effects and support changes to your code by refactoring to a layered architecture Support your evolving code base by refactoring architectural behavior Back up the refactoring effort by ensuring quality with Unit Testing Approach This book focuses on real-world, easy-to-understand examples of code in a one-on-one style. Refactoring examples are performed and reasons why this refactoring should be applied are detailed. The book is structured from less advanced topics to more advanced topics; but is designed so that reading from beginning to end is not necessary. Who this book is written for This book is primarily for developers who want to refactor their code in Visual Studio.

In the past ten years, the world of recruitment and Human Resource has changed a lot. Shaped by several different and mostly technological factors, the HR department has drastically transformed from sorting resume papers manually to imbibing technology in the recruitment process. Currently, all the Talent pioneers are recognizing the urgency to start embracing emerging technologies, such as Artificial Intelligence, analytics, cognitive, AR and VR to reinvent how people work in the organization and how new talents are being hired. HR professionals also believe, to bridge the gap, organizations must focus on their employee strategies to yield productivity, experience, collaboration, streamlining processes, simplify work, and setting up new goals. With technologies like AI, organizations can invent, reinvent, and transform the processes.

From automating simple tasks to predicting efficiencies, AI has much to offer business. Yet we have also been warned: AI will reinforce biases, hide important decisions, and deplete employment. Are we headed to a smarter workplace, or a dumber future? We will go beyond siloed debate: computer scientists, ethicists, academics, policy makers, and business leaders will come together to share ambitions, experiences, concerns, and visions for the future of AI@Work. By taking part, participants will develop their own take on how AI is shaping the workplace, and what needs to happen next.