Machine Learning konusuna ilgi duyanlar guzel bir kaynak.
It is a great resource for Machine Learning.
Machine learning gives computers the ability to learn without being explicitly programmed for the task at hand. The learning happens when data is combined with mathematical models, for example by finding suitable values of unknown variables in the model.
The most basic example of learning could be that of fitting a straight line to data, but machine learning usually deals with much more flexible models than straight lines.
The point of doing this is that the result can be used to draw conclusions about new data, that was not used in learning the model.
If we learn a model from a data set of 1000 puppy images, the model might — if it is wisely chosen — be able to tell whether another image
Machine Learning for Everyone makina ogrenmesinin temellerine inen ve konuyu basitçe anlatan güzel bir e-kitap.
Machine Learning for Everyone is a good ebook that goes to the basics of machine learning and simply tells the subject.
Classical machine learning is often divided into two categories – Supervised and Unsupervised Learning.
In the first case, the machine has a “supervisor” or a “teacher” who gives the machine all the answers, like whether it’s a cat in the picture or a dog. The teacher has already divided (labeled) the data into cats and dogs, and the machine is using these examples to learn. One by one. Dog by cat. Unsupervised learning means the machine is left on its own with a pile of animal photos and a task to find out who’s who. Data is not labeled, there’s no teacher, the machine is trying to find any patterns on its own. We’ll talk about these methods below. Clearly, the machine will learn faster with a teacher, so it’s more commonly used in real-life tasks. There are two types of such tasks: classification – an object’s category prediction, and regression – prediction of a specific point on a numeric axis.
Makine ogrenmesi konusunda teknik stratejileri anlatan 100 sayfalik bir kitap…
A 100-page book describing technical strategies for machine learning …
Machine learning is the foundation of countless important applications, including web search, email anti-spam, speech recognition, product recommendations, and more. I assume that you or your team is working on a machine learning application, and that you want to make rapid progress. This book will help you do so.
Example: Building a cat picture startup Say you’re building a startup that will provide an endless stream of cat pictures to cat lovers.
You use a neural network to build a computer vision system for detecting cats in pictures. But tragically, your learning algorithm’s accuracy is not yet good enough. You are under tremendous pressure to improve your cat detector. What do you do? Your team has a lot of ideas, such as: • Get more data: Collect more pictures of cats. • Collect a more diverse training set. For example, pictures of cats in unusual positions; cats with unusual coloration; pictures shot with a variety of camera settings; …. • Train the algorithm longer, by running more gradient descent iterations. • Try a bigger neural network, with more layers/hidden units/parameters. • Try a smaller neural network. • Try adding regularization (such as L2 regularization). • Change the neural network architecture (activation function, number of hidden units, etc.) • … If you choose well among these possible directions, you’ll build the leading cat picture platform, and lead your company to success. If you choose poorly, you might waste months. How do you proceed? This book will tell you how. Most machine learning problems leave clues that tell you what’s useful to try, and what’s not useful