Machine learning and deep learning models, like those in Keras, require all input and output variables to be numeric. This means that if your data contains categorical data, you must encode it to numbers before you can fit and evaluate a model. The two most popular techniques are an integer encoding and a one hot encoding, although a newer technique called learned embedding may provide a useful middle ground between these two methods. In this tutorial, you will discover how to encode categorical data when developing neural network models in Keras. How to Encode Categorical Data for Deep Learning in Keras Photo by Ken Dixon, some rights reserved. A categorical variable is a variable whose values take on the value of labels.

Machine learning technology has the capacity to autonomously identify malignant tumors, pilot Teslas and subtitle videos in real time. The term "autonomous" is tricky here, because machine learning still requires a lot of human ingenuity to get these jobs done. It works like this: An algorithm scans a massive dataset. Engineers don't tell it exactly what to look for in this initial dataset, which could consist of images, audio clips, emails and more. Instead, the algorithm conducts a freeform analysis.

Proper use of time series and location data in prediction and optimization can considerably boost the yield of data science and AI initiatives. While location and time data have been available for business use, using them in AI requires scaling them as spatiotemporal functions that can be processed with high performance. This has been a major industry challenge, due to the fact that key geospatial functions are locked away in database silos or fragmented everywhere. Just as we are automating AI lifecycle management with AutoAI and promoting AI model trust and transparency with Watson OpenScale, IBM Research has been tasked to solve this additional, demanding challenge. Spatiotemporal functions implemented as part of Analytic Engines in Watson Studio are now coming to Cloud Pak for Data.

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed! By just putting in a few hours a week for the next few weeks, this is what you'll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more.

Every recruitment leader I am working with right now is asking me about AI (artificial intelligence) and machine learning in recruitment technology. Equally, the recruitment marketers I mentor and coach are thinking about AI in marketing and how it can help them improve job adverts, and brand and sales. This blog is a speedy 3-minute read with 2 key takeaways for recruitment leaders and their marketers who want to understand how AI will affect recruitment. Recruitment and marketing need humans!" But I am extremely aware that AI and machine learning is starting to disrupt recruitment - at least it is disrupting the conversations, which often leads to a disruption of the job and the product. If it's disrupting my marketing mentoring and recruitment training sessions, it will be disrupting the calls that recruiters are making, and the meetings that marketers are having with their recruiters. I have been listening to Brad Geddes and his Podcast on Humans Vs Machines. Brad has been in paid search / for as long as you can pay for searches (1998) and has some great insight into AI and machine learning, and the role of humans in business. This podcast was a speedy listen, and made me think about 2 key takeaways for my recruitment and marketing. Is AI / machine learning going to replace humans (and in this I mean recruiters and recruitment marketers)? Brad makes a great point that computers are great at numbers and data crunching. I feel that the average recruiter and marketer have very little time to do this job – so, yay to computers! Humans are great at strategy, while computers are not going to nail this (yet!). But again, the marketers I mentor and the recruiters my team train often have little time for marketing strategy and recruitment strategy – both roles (at the moment) are fraught with a JFDI approach. BUT Brad makes a great point: when you link the two – when you link humans and computers... BINGO! "How can I link AI and my humans together?

Like a lot of working parents, when I'm walking my daughters to school or listening to them recount their days at the dinner table, one question is often on my mind: What should I be doing to prepare them for the world they'll enter as adults? When my daughters and their peers enter the workforce in 10 years, the global economy will be even more competitive, automated and technology-driven than it is today. Computing will be faster and cheaper. Artificial intelligence will be even more powerful, complemented by sensors everywhere in our environments--making it impossible to distinguish between "online" and "offline." Our greatest challenges, from climate change to economic inequality to privacy, will be even more acute.

This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow 2 framework in a way that is easy to understand.We'll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0's official API) to quickly and easily build models. In this course we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more! This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way! Learn to use TensorFlow 2.0 for Deep Learning Leverage the Keras API to quickly build models that run on Tensorflow 2 Perform Image Classification with Convolutional Neural Networks Use Deep Learning for medical imaging Forecast Time Series data with Recurrent Neural Networks Use Generative Adversarial Networks (GANs) to generate images Use deep learning for style transfer Generate text with RNNs and Natural Language Processing Serve Tensorflow Models through an API Use GPUs for accelerated deep learning This course will guide you through how to use Google's latest TensorFlow 2 framework to create artificial neural networks for deep learning!

"(AI) will rank among our greatest technological achievements, and everyone deserves to play a role in shaping it." Encompassing all facets of AI, the General Secretariat of MONTREAL.AI introduces, with authority and insider knowledge: "Artificial Intelligence 101: The First World-Class Overview of AI for the General Public". AI opens up a world of new possibilities. This AI 101 tutorial harnesses the fundamentals of artificial intelligence for the purpose of providing participants with powerful AI tools to learn, deploy and scale AI. Theoretical Physics in 1 (one) year, followed by a Master's degree in Government Policy Analysis (1998) and a Master's degree in Aerospace Engineering (Space Technology) (2000).

Every commercial organization wants to grow continuously and incrementally. Through its unlimited processing power Artificial Intelligence (AI) has the power to make such growth a reality. However, established workflows and processes often hamper the application of true AI, so how do organizations get started on digital transformation without disrupting their existing operations? Intelligent content holds the key to digital transformation. But what do we mean by intelligent content? How does it aid digital transformation?

To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate intelligence in a way that enables comparisons between two systems, as well as comparisons with humans. Over the past hundred years, there has been an abundance of attempts to define and measure intelligence, across both the fields of psychology and AI. We summarize and critically assess these definitions and evaluation approaches, while making apparent the two historical conceptions of intelligence that have implicitly guided them. We note that in practice, the contemporary AI community still gravitates towards benchmarking intelligence by comparing the skill exhibited by AIs and humans at specific tasks such as board games and video games. We argue that solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience: unlimited priors or unlimited training data allow experimenters to "buy" arbitrary levels of skills for a system, in a way that masks the system's own generalization power. We then articulate a new formal definition of intelligence based on Algorithmic Information Theory, describing intelligence as skill-acquisition efficiency and highlighting the concepts of scope, generalization difficulty, priors, and experience. Using this definition, we propose a set of guidelines for what a general AI benchmark should look like. Finally, we present a benchmark closely following these guidelines, the Abstraction and Reasoning Corpus (ARC), built upon an explicit set of priors designed to be as close as possible to innate human priors. We argue that ARC can be used to measure a human-like form of general fluid intelligence and that it enables fair general intelligence comparisons between AI systems and humans.