Learn Full Course: ☞ http://on.codek.tv/NydkB38zZ
Deep Learning: Convolutional Neural Networks in Python ☞ http://on.codek.tv/412LB3UGZ
Data Science: Practical Deep Learning in Theano + TensorFlow ☞ http://on.codek.tv/VyYdr3UGZ
A guide for writing your own neural network in Python and Numpy, and how to do it in Google’s #TensorFlow.
This course will get you started in building your FIRST artificial neural network. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.
We extend the previous binary classification model to K classes using the softmax function, and we derive the very important training method called “backpropagation” using first principles. I show you how to code backpropagation in Numpy, first “the slow way”, and then “the fast way” using Numpy features.
Next, we implement a neural network using Google’s new TensorFlow library.
You should take this course if you are interested in any of the topics I mentioned about, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features.
After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks - slightly modified architectures and what they are used for.
If you already know about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPU-optimization, check out my follow-up course on this topic, Data Science: Practical Deep Learning Concepts in Theano and TensorFlow.