What is machine learning / ai ? How to lean machine learning in practice ? What are recurrent neural networks ( rnn ) , what are long short term neural networks ( lstm ) and how do the work?
machine learning / ai (artificial intelligence) is the hottest topic in this century - for good reasons.
Neural Networks (often referred to as deep learning) in their differnt forms are particular interesting. But there are a few questions.
To answer these questions and give beginners a guide to really understand them, I created two interesting crash courses. The first course " A crash course in neural networks for beginners " already covers the multilayer perceptron and convolutional neural networks. This second course extends the knowledge you have already acquired. Together we explore recurrent neural networks or rnn in depth and implement them in code. In additon to that we also learn to understand long short term memory neural networks or lstm which play a major role in creating chatbots.
Normally there is a lot of math involved which might discourage beginners. I agree that statistics, calculus and linear algebra are not everyone's favourite topics.
That's why this crash course in neural networks emphasizes the real understanding of how a neural network works without overloading you with math. Nonetheless math is involved of course because it's necessary to understand the process. But there is no need to be a math expert I promise!
Do you want to take your chance and expose yourself to this interesting topic which will change the world forever? Then join me and other students to dive deeper into neural networks right now. What are you waiting for?
See you in the first lecture
Daniel is a 28 year old entrepreneur ,data scientist and web analyst consultant. He holds a master degree as well as other major certificates from Google and others.
He is committed to support other people by offering them educational services to help them accomplishing their goals and becomming the best in their profession.
"In order to do the impossible you need to see the invisible"