#PAWFINDAY1

1. An Introduction to Time Series Modeling
using Deep Learning with TensorFlow
and Keras
Aaron Goldenberg, QuantRiskTrading, LLC

3. Machine Learning vs Deep Learning

4. Playground
http://playground.tensorflow.org/

5. 2007 - 50x speed CPU!

6. AWS

7. Google’s TensorFlow - 2015

8. TensorFlow
TensorFlow is an open source library for fast numerical computing. It was created
and is maintained by Google and released under the Apache 2.0 open source
license. The API is nominally for the Python programming language, although
there is access to the underlying C++ API. Unlike other numerical libraries
intended for use in Deep Learning like Theano, TensorFlow was designed for use
both in research and development and in production systems, not least RankBrain
in Google search1 and the fun DeepDream project2. It can run on single CPU
systems, GPUs as well as mobile devices and large scale distributed systems of
hundreds of machines.

9. TensorFlow
In just its first year, TensorFlow has helped researchers, engineers, artists,
students, and many others make progress with everything from language
translation to early detection of skin cancer and preventing blindness in diabetics.
We're excited to see people using TensorFlow in over 6000 open-source
repositories online.

10. Painting like Van Gogh with Convolution Neural Networks
http://www.subsubroutine.com/sub-subroutine/2016/11/12/painting-like-van-gogh-
with-convolutional-neural-networks

11. TensorFlow Identifying Skin Cancer
https://news.stanford.edu/2017/01/25/artificial-intelligence-used-identify-skin-canc
er/

12. What is TensorFlow?
TensorFlow™ is an open source software library for numerical computation using
data flow graphs. Nodes in the graph represent mathematical operations, while the
graph edges represent the multidimensional data arrays (tensors) communicated
between them. The flexible architecture allows you to deploy computation to one
or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
TensorFlow was originally developed by researchers and engineers working on
the Google Brain Team within Google's Machine Intelligence research
organization for the purposes of conducting machine learning and deep neural
networks research, but the system is general enough to be applicable in a wide
variety of other domains as well.

13. Neural Networks

14. Convolution Neural Networks and MNIST
https://www.tensorflow.org/tutorials/layers

15. Convolutional Neural Networks
Convolutional Neural Networks expect and preserve the spatial relationship between pixels by learning internal feature
representations using small squares of input data. Features are learned and used across the whole image, allowing for the
objects in the images to be shifted or translated in the scene and still detectable by the network. It is this reason why the
network is so useful for object recognition in photographs, picking out digits, faces, objects and so on with varying
orientation. In summary, below are some of the benefits of using convolutional neural networks:

16. Keras - 2015
User friendliness. Keras is an API designed for human beings, not machines. It puts user experience front and center.
Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user
actions required for common use cases, and it provides clear and actionable feedback upon user error.
Modularity. A model is understood as a sequence or a graph of standalone, fully-configurable modules that can be plugged
together with as little restrictions as possible. In particular, neural layers, cost functions, optimizers, initialization schemes,
activation functions, regularization schemes are all standalone modules that you can combine to create new models.
Easy extensibility. New modules are simple to add (as new classes and functions), and existing modules provide ample
examples. To be able to easily create new modules allows for total expressiveness, making Keras suitable for advanced
research.
Work with Python. No separate models configuration files in a declarative format. Models are described in Python code,
which is compact, easier to debug, and allows for ease of extensibility.

17. Keras
(1) Define your model. Create a Sequential model and add configured layers.
(2) Compile your model. Specify loss function and optimizers and call the compile()
(3) Fit your model. Train the model on a sample of data by calling the fit() function on the model.
(4) Make predictions. Use the model to generate predictions on new data by calling functions such as evaluate() or predict()
on the model.

18. Getting started: 30 seconds to Keras
https://keras.io/#getting-started-30-seconds-to-keras

20. Time Series Modeling
Regression with Pandas

21. Recurrent Neural Networks
https://www.tensorflow.org/tutorials/recurrent
NLP - Probabilistic modelling for predicting sentences

22. Recurrent Neural Networks

23. Understanding LSTM Models
http://colah.github.io/posts/2015-08-Understanding-LSTMs/

24. RNN

25. LSTM

26. Keras
>>> from keras.klayers import Embedding,LSTM
>>> model3.add(Embedding(20000,128))
>>> model3.add(LSTM(128,dropout=0.2,recurrent_dropout=0.2))
>>> model3.add(Dense(1,activation='sigmoid'))