Are You an Ecologist or Conservationist Interested in Learning GIS and Machine Learning in R? Then this course is for you! I will take you on an adventure into the amazing of field Machine Learning and GIS for ecological modelling. You will learn how to implement species distribution modelling/map suitable habitats for species in R. My name is MINERVA SINGH and i am an Oxford University MPhil (Geography and Environment) graduate. I finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life spatial data from different sources and producing publications for international peer reviewed journals.

Helsinki University in Finland has launched a course on artificial intelligence -- one that's completely free and open to everyone around the world. Unlike Carnegie Mellon's new undergrad degree in AI, which the institution created to train future experts in the field, Helsinki's offering is more of a beginner course for those who want to know more about it. A lot of tech giants like Google now have divisions working on artificial intelligence projects, and even whole non-tech industries already depend on AI for various tasks. But as Janina Fagerlund from the university's project partner (tech strategy firm Reaktor) said, people might not know that their lives are already affected by AI every day. Fagerlund mentioned the use of AI in the food industry to sort produce and other items at facilities as an example.

The inputs were 96x96 images, and the target outputs were 30-value vectors indicating x,y pairs for 15 facial keypoints. We had to design a CNN from scratch to perform the task. My architecture was three convolutional layers, each followed by a max pooling layer with dropout, then a two-layer dense regression network at the end. Training was done on an EC2 p2xlarge GPU instance, and took around 10 minutes to perform 250 epochs (though there was a lot of trial and error so all told I spent a few hours on training different architectures). The dataset came from this Kaggle competition!

Artificial Intelligence is one of the hottest fields in computer science right now and has taken the world by storm as a major field of research and development. Python has surfaced as a dominant language in AI/ML programming because of its simplicity and flexibility, as well as its great support for open source libraries such as Scikit-learn, Keras, spaCy, and TensorFlow. If you're a Python developer who wants to take first steps in the world of artificial intelligent solutions using easy-to-follow projects, then go for this learning path. This comprehensive 2-in-1 course is designed to teach you the fundamentals of deep learning and use them to build intelligent systems. You will solve real-world problems such as face detection, handwriting recognition, and more.

The Explainable Machine Learning Challenge is a collaboration between Google, FICO and academics at Berkeley, Oxford, Imperial, UC Irvine and MIT, to generate new research in the area of algorithmic explainability. Teams will be challenged to create machine learning models with both high accuracy and explainability; they will use a real-world financial dataset provided by FICO. Designers and end users of machine learning algorithms will both benefit from more interpretable and explainable algorithms. Machine learning model designers will benefit from Model explanations, written explanations describing the functioning of a trained model. These might include information about which variables or examples are particularly important, they might explain the logic used by an algorithm, and/or characterize input/output relationships between variables and predictions.

This is the new book by Andrew Ng, still in progress. Andrew Yan-Tak Ng is a computer scientist and entrepreneur. He is one of the most influential minds in Artificial Intelligence and Deep Learning. Ng founded and led Google Brain and was a former VP & Chief Scientist at Baidu, building the company's Artificial Intelligence Group into several thousand people. He is an adjunct professor (formerly associate professor and Director of the AI Lab) at Stanford University.

Maker Faire Rome is the event in which the digital revolution that is changing the way in which we produce and the way in which we live can be experienced. It is the ideal place for companies and innovators which use the new digital culture as a tool to challenge the market. Over a period of a few years, the event has become a vital reference point for start uppers, digital artisans, business people and other and can boast figures that are constantly increasing. THE MAIN TOPICS OF THE SIXTH EDITION The main topics of the 2018 edition are numerous, current and involving: Iot and Electronic Industry, Artificial Intelligence and Big Data, Smart Robotics and Smart Manufacturing, Intelligent Mobility, Design, Coding and Education. All calls are open for the sixth edition of the "Maker Faire Rome – The European Edition".

Update on April 18th, 2018: (a) New coding exercise has been added to Collections Framework chapter to test Lists & Queues!, (b) New assignment has been added in Section 3 "This is by far the best advanced as well as beginner course I have ever read/seen since Andre LaMothe quit writing." This one should be the best seller of all the other ... " Brady Adams "This is THE best course on Java on Udemy - Period! Dheeru is not only passionate about what he is coaching but also OBSESSIVE and covers every minute detail of the subject ... Most lessons have demos which Dheeru makes sure that they do work without any glitches. He is a genius coder ... Plus, he bases the course on the best practices from the book "Effective Java" which is great. You get to cover most of this book if you study this course! If you want to learn Java right from installing, configuring and all the way to mastering its advanced topics - look no further - you are at the right place THIS - IS - IT!!!" Richard Reddy "This is a wonderful course.

This is the second Udemy class on Matlab I've taken. Already, a couple important concepts have been discussed that weren't discussed in the previous course. I'm glad the instructor is comparing Matlab to Excel, which is the tool I've been using and have been frustrated with. This course is a little more advanced than the previous course I took. As an engineer, I'm delighted it covers complex numbers, derivatives, and integrals.