<jupyter_start><jupyter_text>Deep Learning from Scratch > This directory contains Jupyter's notebook-based documentation for the **Deep Learning from Scractch** course.Deep learning is one of the fastest growing areas of machine learning and a hot topic in both academia and industry. This course will cover the basics of deep learning by using a hands-on approach. ApproachWe will illustrate all contents with Jupyter notebooks, a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Target AudienceThis course is targeted for developers, data scientists and researchers that have a basic knowledge of machine learning. PrerequisitesMinimal experience on Python programming, basic knowledge of calculus, linear algebra, and probability theory. Attendees are expected to bring their own laptops for the hands-on practical work. WhoThis course is organized by the DataScience@UB Group (http://datascience.barcelona/)INSTRUCTORS: <NAME>, Associate Professor at UB. <NAME>, Lecturer at UB. J<NAME>. Full Professor at UB. WhyBy the end of this course, you will be able to:+ Describe how a neural network works and combine different types of layers and activation functions.+ Describe how these models can be applied in computer vision, text analytics, etc.+ Develop your own models in Tensorflow. Topics* Basic Concepts * Tensorflow & Keras* Convolutional Neural Networks* Recurrent Neural Networks* Unsupervised Learning* Advanced Applications<jupyter_code>import keras
import tensorflow
print('keras:', keras.__version__)
print('tensorflow: ', tensorflow.__version__)<jupyter_output>keras: 2.1.3
tensorflow:  1.5.0
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