From the outside, data science is often thought to consist wholly of advanced statistical and machine learning techniques. However, there is another key component to any data science endeavor that is often undervalued or forgotten: exploratory data analysis (EDA). At a high level, EDA is the practice of using visual and quantitative methods to understand and summarize a dataset without making any assumptions about its contents. It is a crucial step to take before diving into machine learning or statistical modeling because it provides the context needed to develop an appropriate model for the problem at hand and to correctly interpret its results.
One of the major issues with artificial neural networks is that the models are quite complicated. For example, let's consider a neural network that's pulling data from an image from the MNIST database (28 by 28 pixels), feeds into two hidden layers with 30 neurons, and finally reaches a soft-max layer of 10 neurons. The total number of parameters in the network is nearly 25,000. This can be quite problematic, and to understand why, let's take a look at the example data in the figure below.
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.
BlackRock Inc., the $5 trillion money manager, announced last month that it would be overhauling its actively managed equities business, increasingly betting on computers rather than humans to make investment decisions. This move sent shudders through the financial services industry that has long relied on people to help others with their asset allocation decisions. For all industry observers, it is the next nail in the coffin of actively managed accounts, as technology disrupts the age old financial services business model. The question that corporate consultants must now ask is: "Are we next?
I always enjoy these industry-spanning infographics. They sometimes point me to companies I want to understand in greater depth. The inclusion of SAS for example as a BI enterprise system and the total absence of IBM SPSS from the data science category are huge red flags. These two companies alone control what is at least 1/3rd of the data science platform market among the global 8,000 companies with more than $1 Billion in revenue.