If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Code for this project can be found on: Github. This article can also be found on my website here. As part of completing the second project of Udacity's Self-Driving Car Engineer online course, I had to implement and train a deep neural network to identify German traffic signs. In total, the dataset used consisted of 51,839 RGB images with dimensions 32x32, and is publicly accessible on this website. A validation set was used to assess how well the model is performing.
We may not have reached the age where we can drive flying cars just yet, but that doesn't mean the age of AI isn't already here. You've probably been encountering AI-driven things more than you realize. Perhaps Netflix has recommended a show or film you've always been meaning to watch. Or your GPS has saved you from sitting in hours of traffic. Or maybe your text messaging app has already predicted what you're going to say next.
Singapore's Ngee Ann Polytechnic (NP) and London-based Centre for Finance, Technology and Entrepreneurship (CFTE) will jointly launch an industry-led AI in Finance (AIF) online course on June 24, 2018. Through this course, both NP and CFTE hope to support and to nurture talent in Fintech and to boost Fintech development in their respective regions and around the world. The course is accredited by SkillsFuture Singapore and is in the process of obtaining accreditation with The Institute of Banking and Finance Singapore. It aims to update finance professionals and technologists on the AI revolution and create an online community of learners and experts in AI to connect and network for future collaborations. Over 20 finance and technology thought leaders and insiders will come together to share key fundamentals and real-life case studies on how AI is reshaping the finance industry worldwide.
Module 3 consists of two lessons: Lessons 5 and 6. In Lesson 5, we discuss mining sequential patterns. We will learn several popular and efficient sequential pattern mining methods, including an Apriori-based sequential pattern mining method, GSP; a vertical data format-based sequential pattern method, SPADE; and a pattern-growth-based sequential pattern mining method, PrefixSpan. We will also learn how to directly mine closed sequential patterns. In Lesson 6, we will study concepts and methods for mining spatiotemporal and trajectory patterns as one kind of pattern mining applications.
About this course: If you're a software developer and new to blockchain, this is the course for you. Several experienced IBM blockchain developer advocates will lead you through a series of videos that describe high-level concepts, components, and strategies on building blockchain business networks. You'll also get hands-on experience modeling and building blockchain networks as well as create your first blockchain application. The first part of this course covers basic concepts of blockchain, and no programming skills are required. However, to complete three of the four labs, you must understand basic software object-oriented programming and how to use the command line.
About this course: In this course, we will learn all the core techniques needed to make effective use of H2O. Even if you have no prior experience of machine learning, even if your math is weak, by the end of this course you will be able to make machine learning models using a variety of algorithms. We will be using linear models, random forest, GBMs and of course deep learning, as well as some unsupervised learning algorithms. You will also be able to evaluate your models and choose the best model to suit not just your data but the other business restraints you may be under.
Welcome to this course: Learn TensorFlow Slim(TF-Slim) From Scratch. TensorFlow-Slim is a light-weight library for defining, training, and evaluating complex models in TensorFlow. With the TensorFlow-Slim library, we can build, train, and evaluate the model easier by providing lots of high-level layers, variables, and regularizers. At the end of this course, you will be geared up to take on any challenges of implementing TensorFlow-Slim in your machine learning environment.
When faced with the choice of saving the life of the passenger or the pedestrian passing by, how should an autonomous car be programed to act? In many scenarios, the most logical decision isn't always the most ethical one, which is why objective decision-making alone in algorithms is not enough. As machine learning and advanced artificial intelligence algorithms become more prevalent in our daily lives, it becomes increasingly important to address the inherent biases and ethical blind spots built into these systems. In fact, when we don't, we risk unleashing systems that may have far-reaching and disastrous consequences across many areas of society, from medical diagnoses to judicial decisions. Join Nathana Sharma, as she talks about the importance of designing AI algorithms that are capable of making decisions that are not just rationally correct, but also ethically right.
Welcome to this e-learning course developed and produced by Dr Neil Thompson and hosted by Simpliv. Neil is a well-published author in the people professions field, an international conference speaker and sought-after consultant.The overall aim of this course is to help you broaden and deepen your understanding of what is involved in learning, what can prevent it from happening and what you can do to maximize your learning. Learning is part of everyday life and something we are very familiar with. But, that does not mean that we are making the most of the learning opportunities we encounter. Indeed, it is fair to say that, despite the emphasis on the importance of learning, relatively few people achieve optimal learning.
Being able to perform machine learning in C will make you a very desirable hiring target. Not that you wouldn't be if you used any other language but, the truth is that machine learning in C is a great combination that is likely to give you access to very interesting positions! In this course, we focus on the practical part of machine learning--employing different C libraries. Several popular machine learning libraries currently exist--we'll review them and you'll become familiar with four of them. We use examples of standard machine learning algorithms implemented through the libraries.