Intro to Deep Learning with Keras

PyData Bristol is the Bristol chapter of the international PyData community.PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. The PyData Code of Conduct (http://pydata.org/code-of-conduct.html) governs this meetup. To discuss any issues or concerns relating to the code of conduct or the behavior of anyone at a PyData meetup, please contact NumFOCUS Executive Director Leah Silen (+1 512-222-5449; leah@numfocus.org) or the group organiser.Twitter: @PyDataBristol

PyData Bristol

Deep learning is becoming increasingly powerful in its applications, from speech recognition to <A HREF="https://www.kaggle.com/c/dog-breed-identification" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">dog recognition</A>, from <A HREF="https://deepmind.com/research/alphago/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">AlphaGo</A> to self-driving cars. It's also becoming easier and easier to jump in and use deep neural networks, with frameworks like <A HREF="https://keras.io/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Keras</A> designed to facilitate fast and easy prototyping whilst remaining user friendly. 
Takeaways This workshop will be a (very) brief introduction to deep neural networks with a focus on using convolutional neural networks for image classification and object recognition. No prior knowledge is assumed, and we'll be starting our journey in the mid-1950s with the concept of artificial neurons, moving swiftly onto perceptrons, multi-layer perceptrons, and finally into the recent advances in deep learning. The workshop will be hands-on throughout with code examples and exercises provided to apply what you've just learned. Pre-requisites & preparation 
<UL>
<LI>Students should have familiarity with Python. Background in linear algebra is preferred but not required. For those wishing to dig a little deeper, brilliant.org has a great free course on Artificial Neural Networks, and the "<A HREF="https://brilliant.org/courses/artificial-neural-networks/math-for-neural-networks/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Math for Neural Networks</A>" module is especially recommended.</LI>
<LI>Install Anaconda. We will be using the Anaconda distribution of Python 3.6. The download comes pre-installed with a lot of the libraries required for machine learning. You can get this from <A HREF="https://www.anaconda.com/download/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">https://www.anaconda.com/download/</A></LI>
<LI>Install TensorFlow. Installing TensorFlow can be as simple as conda install tensorflow, although Google recommends installing the officially supported/tested version from the Python Package Index (PyPI) using pip install tensorflow. Regardless, there are full and up-to-date installation instructions here: <A HREF="https://www.tensorflow.org/install/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">https://www.tensorflow.org/install/</A>. If you have GPUs on your machine, then feel free to install the TensorFlow with GPU support (e.g. pip install tensorflow-gpu). Note that there are a number of pre-requisites, such as CUDA Toolkit 9.0, the associated NVIDIA drivers, cuDNN v7.0 and a GPU card with CUDA Compute Capability 3.5 or higher (this is all detailed on TensorFlow's installation guide). You do not need to enable GPU support for this workshop.</LI>
<LI>Install Keras. This should be as simple as pip install keras from within your Anaconda environment. For more information please see: <A HREF="https://keras.io/#installation" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">https://keras.io/#installation</A> </LI>
</UL>
PyData Bristol #4: Pre-Meetup Workshop
This workshop will be the first in a series of pre-meetup workshops running before each PyData Bristol meetup. <A HREF="https://www.meetup.com/PyData-Bristol/events/253608445/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">PyData Bristol #4</A> will feature talks on "Controlling Aircraft with Deep Reinforcement Learning" and "Hand pose classification in VR using TensorFlow" plus lightning talks. 
Instructor
John Sandall is the Founder and Principal Data Scientist at Coefficient, a data consultancy offering data science, engineering, machine learning and other AI-related services as well as bespoke training courses. In April 2017 he created SixFifty.org.uk in order to predict the UK General Election using open data and advanced modelling techniques and is currently a Fellow of Newspeak House. For more information please visit <A HREF="http://artofinference.com" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">artofinference.com</A> or follow him on Twitter <A HREF="https://twitter.com/john_sandall" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">@john_sandall</A>

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Intro to Deep Learning with Keras

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Intro to Deep Learning with Keras

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