Get your team access to Udemy's top 2,500 courses anytime, anywhere. You make a great decision to join. Artificial intelligence (AI) is the hottest topic currently out there - no doubt about that. Neural networks in particular have seen a lot of attention and they will be used everywhere -self driving cars, predictions in finance and sales forecasts - everywhere and across all industries. To be successful in the working world of tomorrow we have to expose ourselves to this interesting topic - and from my personal experience - coding your own neural network is the best way to understand how they work.
This course is about deep learning fundamentals and convolutional neural networks. Convolutional neural networks are one of the most successful deep learning approaches: self-driving cars rely heavily on this algorithm. First you will learn about densly connected neural networks and its problems. The next chapter are about convolutional neural networks: theory as well as implementation in Java with the deeplearning4j library. The last chapters are about recurrent neural networks and the applications!
About this course: Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well. This course contains the same content presented on Coursera beginning in 2013. It is not a continuation or update of the original course. It has been adapted for the new platform. Please be advised that the course is suited for an intermediate level learner - comfortable with calculus and with experience programming (Python).
In his 10-week course Ng takes a an engineering-oriented approach to Machine Learning that concentrates on statistical models. If you are looking for an alternative Coursera also has Neural Networks for Machine Learning, a class taught by University of Toronto professor, Geoffry Hinton who is a leading proponent in the field from a cognitive science perspective. His eight-week course sets out to teach students artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion. Its prerequisites are programming proficiency in Matlab, Octave or Python, plus knowledge of calculus, linear algebra and probability theory.