Learning in Python

Study Data Science and Machine Learning including Modern Neural Networks produced in Python, Theano, and TensorFlow
While learning is a complex subject, it is not any more difficult to learn than any other machine learning algorithm. I wrote this book to introduce you to the basics of neural networks. You will get along fine with undergraduate-level math and programming skill.
All the materials in this book can be downloaded and installed for free. We will use the Python programming language, along with the numerical computing library Numpy. I will also show you in the later chapters how to build a deep network using Theano and TensorFlow, which are libraries built specifically for deep learning and can accelerate computation by taking advantage of the GPU.
Unlike other machine learning algorithms, deep learning is particularly powerful because it automatically learns features. That means you don't need to spend your time trying to come up with and test "kernels" or "interaction effects" - something only statisticians love to do. Instead, we will let the neural network learn these things for us. Each layer of the neural network learns a different abstraction than the previous layers. For example, in image classification, the first layer might learn different strokes, and in the next layer put the strokes together to learn shapes, and in the next layer put the shapes together to form facial features, and in the next layer have a high-level representation of faces.
On top of all this, deep learning is known for winning its fair share of Kaggle contests. These are machine learning contests that are open to anyone in the world who is allowed to use any machine learning technique they want. Deep learning is powerful.
Do you want a gentle introduction to this "dark art", with practical code examples that you can try right away and apply to your own data
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