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 hard to 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.

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


Applied Deep Learning in Python Mini-Course