Python for probability statistics and machine learning pdf started with deep learning today. Get your copy of Deep Learning With Python.

Deep learning is the most interesting and powerful machine learning technique right now. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this mega Ebook is written in the friendly Machine Learning Mastery style that you’re used to, learn exactly how to get started and apply deep learning to your own machine learning projects. The best source so far I found that shows how to use deep learning in Python. Very well explained material with a lot of examples.

Highly recommend this book if you want to apply deep learning in practice. Why Are Deep Learning Models So Powerful? Deep learning techniques are so powerful because they learn the best way to represent the problem while learning how to solve the problem. This is called representation learning. Representation learning is perhaps the biggest differentiation between deep learning models and classical machine learning algorithm.

It is the power of representation learning that is spurring such great creativity in the way the techniques are being used. Deep learning models are being used for very difficult problems and making progress, like colorizing image and videos based on the context in the scene. Deep learning models are being used in bold new ways, such as cutting the head off a network trained on one problem and tuning it for a completely different problem, and getting impressive results. Combinations of deep learning models are being used to both identify objects in photographs and then generate textual descriptions of those objects, a complex multi-media problem that was previously thought to require large artificial intelligence systems. Deep learning is hot, it is delivering results and now is the time to get involved. But where do you start? So How Do Regular People Get Started?

Where do you even begin in deep learning? Deep learning looks like a hard field to get started in. And in many ways it is hard to get started. Hard enough that many people try and quickly give up.

Because they are told that they must already be masters in a laundry list of academic disciplines. Develop a strong grounding in statistics, probability, linear algebra, multivariate statistics and calculus. Develop a deep knowledge of modern machine learning algorithms and techniques. Study and become one with the mathematical theory of each deep learning algorithm and a bunch of related techniques for using them.

Oh and if there is time find a library and start applying deep learning to your problem. It could take a decade or more to follow this advice and that would be a decade delay that you cannot afford. I would never have started developing software as a profession. There is a much easier path that is just right for you. Deep learning is a tool that you can use on your machine learning projects. It does not have to be a theoretical academic pursuit that you study in gritty detail. You can get started in deep learning by selecting one of the best-of-breed deep learning libraries and start developing models.