Where I express personal thoughts in words
In those moments of boredom when you're playing with Snapchat's filters - sticking your tongue out, ghoulifying your features, and working out how to get the flower crown to fit exactly on your head - surely you've had a moment where you've wondered what's going on, on a technical level - how Snapchat manages to match your face to the animations?
Data Scientists at Work displays how some of the world’s top data scientists work across a dizzyingly wide variety of industries and applications — each leveraging her own blend of domain expertise, statistics, and computer science to create tremendous value and impact.
Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective where manual programming is not. As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer sincere and other fields. However, developing successful machine learning applications requires a substantial amount of “black art” that is hard to find in textbooks.
Today, deep neural networks and deep learning achieve outstanding performance on many important problems in computer vision, speech recognition, and natural language processing. They’re being deployed on a large scale by companies such as Google, Microsoft, and Facebook.I hope that this post helps you learn the core concepts of neural networks, including modern techniques for deep learning.
Deep Learning is strongly technique-focused. There are not much concrete explanations for each of the new ideas. Most new ideas came out with experimental results attached to prove that they work. Deep Learning is like playing LEGO. Mastering LEGO is as challenging as any other arts, but getting into it is easier.
It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data.
Data scientists live at the intersection of coding, statistics, and critical thinking. As Josh Wills put it, “data scientist is a person who is better at statistics than any programmer and better at programming than any statistician.” I personally know too many software engineers looking to transition into data scientist and blindly utilizing machine learning frameworks such as TensorFlow or Apache Spark to their data without a thorough understanding of statistical theories behind them. So comes the study of statistical learning, a theoretical framework for machine learning drawing from the fields of statistics and functional analysis.
