The lecture illustrates how machine learning systems adding elements of intelligence to your own application. Machine learning is basically taking data running it through an algorithm to yield some sort of insight. However, it needs a place to store the data and run the algorithm, which means infrastructure is an integral part of a machine learning systems. Machine learning systems have solved many new problems. As matter of fact, Google used the machine learning technique with solving problems. As an example google translate build in a machine learning called machine translator.
The developers can do a lot with machine learning.
The spectrum of the machine learning, Transonflow is for deep research to make innovative deep learning research. On the other hand, the spectrum includes the cloud machine learning, which can analyze data and classification tasks. Helps with predicting data to get a valuable information. Moreover, part of the machine learning is the machine learning APIs. Under the API systems are Google cloud speech API which works with audio, helps with returns a transcript of the audio data. Also, the Google cloud vision API which works with images and process this kind of data to give a useful information.
One of the companies used the machine learning systems that was mentioned in the lecture is Wex. Wex started to lunch new machine learning technology to improve many aspects that the company aiming. One of them is to learn and know more about their users. Through the machine learning, the company starts to improve.
There are many Apps using the machine learning, one of the Application is Oval money. it takes a different approach. The app uses machine learning to help save you money. By looking at your spending habits and collective knowledge from all users, Oval creates a money-saving strategy that’s smart and easy for you to follow.
Another example, The snapchat application start to use the machine learning. Looksery’s clever facial tracking algorithm to search out your face in your snaps and add things like glasses, hats and doggy ears. Recognizing a face is simple for humans, however tough for computers. explicitly programming a computer to recognize a face is nearly not possible. Instead, Snapchat has its algorithm to verify thousands of faces to slowly learn what a face sounds like. every image has all facial expression like eyes and nose marked by humans.