"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Machines understand numbers, not text. Feature Encoding is the process of converting categorical variables into numerical values. Any dataset contains 2 types of data – numerical and categorical data. Machines have no problem in understanding numerical data. But some of the machine learning algorithms have a problem in understanding categorical data and they have to be converted into numerical data before passing onto the algorithm.
While many know UK company Ocado as an online grocery retailer, it's really one of the most innovative tech companies in the world. Ocado was founded in 2000 as an entirely online experience and therefore never had a brick-and-mortar store to serve its customers, who number 580,000 each day. Its technology expertise came about out of necessity as it began to build the software and hardware it needed to be efficient, productive, and competitive. Today, Ocado uses artificial intelligence (AI) and machine learning in many ways throughout its business. Since 2000, Ocado tried to piece together the technology they needed to succeed by purchasing products off the shelf.
Kuzushiji, a cursive writing style, had been used in Japan for over a thousand years starting from the eighth century. Over 3 million books on a diverse array of topics, such as literature, science, mathematics and even cooking are preserved. However, following a change to the Japanese writing system in 1900, Kuzushiji has not been included in regular school curricula. Therefore, most Japanese natives nowadays cannot read books written or printed just 150 years ago. Museums and libraries have invested a great deal of effort into creating digital copies of these historical documents as a safeguard against fires, earthquakes and tsunamis.
As an engineering director leading research projects into the application of machine learning (ML) and deep learning (DL) to computational software for electronic design automation (EDA), I believe I have a unique perspective on the future of the electronic and electronic design industries. The next leap in design productivity for semiconductor chips and the systems built around them will come from the fusion of fully integrated EDA computational software tool flows, the application of distributed and multi-core computing on a broader scale and ML/DL. The current wave of artificial intelligence (AI) and ML innovation began with improved GPU computing capacity and the smart engineers who figured out how to harness it to accelerate deep neural network training. AI/ML will play a key role in the design of next-generation platforms, enabling the proliferation of today's technology drivers including 5G, hyperscale computing and others. In my role, the fun comes from the numerous non-deterministic polynomial (NP)-hard and NP-complete problems that exist at every stage of the design and verification process.
We are near the end of the hype cycle for artificial intelligence (AI). The human champion of the game of Go decided to retire, saying AI cannot be beaten after AlphaGo defeated him. Domain-specific chatbots are engaging with customers and providing them with the answers they need. AI is about to revolutionize our broken health-care system. Is your company ready for AI? Anyone with deep data claims to be using AI.
Some would say that being an AI expert is the job of the century. I think that point of view puts a lot of pressure on the candidates who plan to take a shot at the artificial intelligence industry. It might create the impression that only the most brilliant students who have had straight A's since high school are good enough for an AI role. Learning and practicing AI is not a cakewalk. You need to put in the hours and struggle for solutions.
Dabl library has been created by Andreas Mueller, one of the core developers and maintainers of the scikit-learn machine learning library. The idea behind dabl is to make supervised machine learning more accessible to beginners and reduce boilerplate for common tasks. Dabl takes inspirations from scikit-learn and auto-sklearn. The library is being developed actively and hence isn't recommended for production use. Dabl can be used for automated preprocessing of the dataset, quick EDA as well as initial model building as part of a typical machine learning pipeline.
A decision tree is one of the supervised machine learning algorithms. This algorithm can be used for regression and classification problems -- yet, is mostly used for classification problems. A decision tree follows a set of if-else conditions to visualize the data and classify it according to the conditions. Before we dive deep into the working principle of the decision tree's algorithm you need to know a few keywords related to it. Attribute Subset Selection Measure is a technique used in the data mining process for data reduction.
I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Ensuring fairness and safety in artificial intelligence(AI) applications is considered by many the biggest challenge in the space. As AI systems match or surpass human intelligence in many areas, it is essential that we establish a guideline to align this new form of intelligence with human values.