The App covers a wide range of topics for the engineering students. The app lists 15 subjects, 90 units, 1200 topics on AI, machine learning and related computer science courses. The app contains Online content since adding offline content will make the app heavy. It consumes very small amount of data since it is only notes and diagrams which are being fetched. The the app includes the following subject courses 1. Introduction to Artificial Intelligence 2. Control Systems 3. Real Time Systems 4. Discrete Mathematics 5. Numerical Methods 6. Automata 7. Neural Network & Fuzzy systems 8. Design Analysis of algorithms 9. Physics for Engineers 10.

For more on derivatives and differentiability, see the rest of that tutorial.) Why do we care that the stack (as a function) is differentiable? Well, we used the "derivative" of the function to move the error around (more specifically... to backpropagate). For more on this, please see the Tutorial I Wrote on Basic Neural Networks, Gradient Descent, and Recurrent Neural Networks. I particularly recommend the last one because it demontrates backpropgating through somewhat more arbitrary vector operations... kindof like what we're going to do here.

We often get questions about whether to use Python or R – and we've come to a conclusion thanks to insight from our community of mentors and learners. Data science is the sexiest job of the 21st century. Data scientists around the world are presented with exciting problems to solve. Within the complex questions they have to ask, a growing mountain of data rests a set of insights that can change entire industries. In order to get there, data scientists often rely on programming languages and tools. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May.

We often get questions about whether to use Python or R – and we've come to a conclusion thanks to insight from our community of mentors and learners. Data science is the sexiest job of the 21st century. Data scientists around the world are presented with exciting problems to solve. Within the complex questions they have to ask, a growing mountain of data rests a set of insights that can change entire industries. In order to get there, data scientists often rely on programming languages and tools.

It's also critical to understand the differences between a Data Analyst and a Machine Learning engineer. In simplest form, the key distinction has to do with the end goal. As a Data Analyst, you're analyzing data in order to tell a story, and to produce actionable insights. The emphasis is on dissemination--charts, models, visualizations. The analysis is performed and presented by human beings, to other human beings who may then go on to make business decisions based on what's been presented.

Modern Workplace is a monthly thought-leadership webcast series that gives you access to entrepreneurs and thought leaders who are changing the future, brought to you by Microsoft. Join us as we explore cognitive technology, and learn what advances in artificial intelligence will impact analytics and demand for human interaction on the next episode of Modern Workplace, Machine Learning: How technology advances human intelligence, airing April 12th at 8:00 AM PDT / 3:00 PM GMT. This month, two of the industry's most forward thinkers share their predictions of where business is going with big data and how we can take advantage of the changes in technology to come. Gain an insider perspective as we dissect the world of cognitive technology, learn what technological capabilities will be available, and what tasks will still require human input.

Abstract: In this webinar targeted at developers who are new to Machine Learning, I'll present the type of predictive features that can be integrated in any app (mobile, desktop, web…) and powered by data and machine learning. We'll also look at what's at stake when using ML in production and what the steps are to integrate this technology into real-world apps. In particular, we'll review open source libraries and cloud platforms that make it easier to create and experiment with ML models, but also to deploy them to production. I'll demo how to create a simple predictive model in Python with scikit-learn, how to deploy it as a REST API, and how to use that API. About your host: Louis Dorard is General Chair of the PAPIs.io Louis is also the instructor of the Operational Machine Learning workshop.

This workshop will attempt to present some of the very recent developments on non-convex analysis and optimization, as reported in diverse research fields: from machine learning and mathematical programming to statistics and theoretical computer science. We believe that this workshop can bring researchers closer, in order to facilitate a discussion regarding why tackling non-convexity is important, where it is found, why non-convex schemes work well in practice and, how we can progress further with interesting research directions and open problems.

This series of tutorial is about evolutionary computation: what it is, how it works and how to implement it in your projects and games. At the end of this series you'll be able to harness the power of evolution to find the solution to problems you have no idea how to solve. As a toy example, this tutorial will show how evolutionary computation can be used to teach a simple creature to walk. If you want to try the power of evolutionary computation directly in your browser, try Genetic Algorithm Walkers. As a programmer, you might be familiar with the concept of algorithm.

Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge. In this course, you'll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets. We'll show you how to train and optimize basic neural networks, convolutional neural networks, and long short term memory networks. Complete learning systems in TensorFlow will be introduced via projects and assignments. You will learn to solve new classes of problems that were once thought prohibitively challenging, and come to better appreciate the complex nature of human intelligence as you solve these same problems effortlessly using deep learning methods.