Deep learning is a fascinating field of study and the techniques are achieving world class results in a range of challenging machine learning problems.
It can be hardto get started in deep learning.
Which library should you use and which techniques should you focus on?
In this post you will discover a 14-part crash course into deep learning in python with the easy to use and powerful Keras library.
This mini-course is intended for python machine learning practitioners that are already comfortable with scikit-learn on the SciPy ecosystem for machine learning.
Let’s get started.
( Tip : you might want to print or bookmark this page so that you can refer back to it later. )
Applied Deep Learning in Python Mini-Course
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Who Is This Mini-Course For?Before we get started, let’s make sure you are in the right place. The list below provides some general guidelines as to who this course was designed for.
Don’t panic if you don’t match these points exactly, you might just need to brush up in one area or another to keep up.
Developers that know how to write a little code . This means that it is not a big deal for you to get things done with Python and know how to setup the SciPy ecosystem on your workstation (a prerequisite). It does not mean your a wizard coder, but it does mean you’re not afraid to install packages and writescripts. Developers that know a little machine learning . This means you know about the basics of machine learning like cross validation, some algorithms and the bias-variance trade-off. It does not mean that you are a machine learning PhD, just that you know the landmarks or know where to look them up.This mini-course is not a textbook on Deep Learning.
It will take you from a developer that knows a little machine learning in Python to a developer who can get results and bring the power of Deep Learning to your own projects.
Mini-Course Overview (what to expect)This mini-course is divided into 14 parts.
Each lesson was designed to take the average developer about 30 minutes. You might finish some much sooner and other you may choose to go deeper and spend more time.
You can can complete each part as quickly or as slowly as you like. A comfortable schedule may be to complete one lesson per day over a two week period. Highly recommended.
The topics you will cover over the next 14 lessons are as follows:
Lesson 01 : Introduction to Theano. Lesson 02 : Introduction to TensorFlow. Lesson 03 : Introduction to Keras. Lesson 04 : Crash Course in Multi-Layer Perceptrons. Lesson 05 : Develop Your First Neural Network in Keras. Lesson 06 : Use Keras Models With Scikit-Learn. Lesson 07 : Plot Model Training History. Lesson 08 : Save Your Best Model During Training With Checkpointing. Lesson 09 : Reduce Overfitting With Dropout Regularization. Lesson 10 : Lift Performance With Learning Rate Schedules. Lesson 11 : Crash Course in Convolutional Neural Networks. Lesson 12 : Handwritten Digit Recognition. Lesson 13 : Object Recognition in Small Photographs. Lesson 14 : Improve Generalization With Data Augmentation.This is going to be a lot of fun.
You’re going to have to do some work though, a little reading, a little research and a little programming. You want to learn deep learning right?
( Tip : All of the answers these lessons can be found on this blog, use the search feature )
Any questions at all, please post in the comments below.
Share your results in the comments.
Hang in there, don’t give up!
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Lesson 01: Introduction to TheanoTheano is a Python library for fast numerical computation to aid in the development of deep learning models.
At it’s heart Theano is a compiler for mathematical expressions in Python. It knows how to take your structures and turn them into very efficient code that uses NumPy and efficient native libraries to run as fast as possible on CPUs or GPUs.
The actual syntax of Theano expressions is symbolic, which can be off-putting to beginners used to normal software development. Specifically, expression are defined in the abstract sense, compiled and later actually used to make calculations.
In this lesson your goal is to install Theano and write a small example that demonstrates the symbolic nature of Theano programs.
For example, you can install Theano using pip as follows:
sudo pip install Theano
A small example of a Theano program that you can use as a starting point is listed below:
importtheano fromtheanoimporttensor # declare two symbolic floating-point scalars a = tensor.dscalar() b = tensor.dscalar() # create a simple expression c = a + b # convert the expression into a callable object that takes (a,b) # values as input and computes a value for c f = theano.function([a,b], c) # bind 1.5 to 'a', 2.5 to 'b', and evaluate 'c' result = f(1.5, 2.5) print(result)Learn more about Theano on the Theano homepage .
Lesson 02: Introduction to TensorFlowTensorFlow is a Python library for fast numerical computing created and released by Google. Like Theano, TensorFlow is intended to be used to develop deep learning models.
With the backing of Google, perhaps used in some of it’s production systems and used by the Google DeepMind research group, it is a platform that we cannot ignore.
Unlike Theano, TensorFlow does have more of a production focus with a capability to run on CPUs, GPUs and even very large clusters.
In this lesson your goal is to install TensorFlow become familiar with the syntax of the symbolic expressions used in TensorFlow programs.
For example, you can install TensorFlow using pip:
sudo pip install TensorFlow
A small example of a TensorFlow program that you can use as a starting point is listed below:
importtensorflowas tf # declare two symbolic floating-point scalars a = tf.placeholder(tf.float32) b = tf.placeholder(tf.float32) # create a simple symbolic expression using the add function add = tf.add(a, b) # bind 1.5 to ' a ' , 2.5 to ' b ' , and evaluate ' c ' sess = tf.Session() binding = {a: 1.5, b: 2.5} c = sess.run