While deep learning racks up the likes among the big data crowd, a potentially bigger phenomenon is the emergence of extremely simple machine learning models that do not require sophisticated technical and mathematical skills, or what machine learning expert Ted Dunning calls "cheesy and cheap machine learning," or simply "cheap learning."
This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.
The recent explosion of data set size, in number of records and attributes, has triggered the development of a number of big data platforms as well as parallel data analytics algorithms. At the same time though, it has pushed for usage of data dimensionality reduction procedures. Indeed, more is not always better. Large amounts of data might sometimes produce worse performances in data analytics applications.
Artificial Intelligence (AI) has become the latest buzzword in the IT industry. Everything from the dishwasher and fridge to TVs and cars has become connected due to the Internet of Things (IoT), and with AI, many think that these products are going to think like human beings. Computers have certainly become more intelligent. In 2016, Google's AI software called AlphaGo finally beat one of the top Go players in the world, a feat that had been thought to be impossible as Go is very complex.