Hands-on Data Sciences Workshop
Advanced Deep Learning with Python, Keras
& Tensorflow
“Improve
your deep learning skills building complex models with Python, Keras and
Tensorflow”
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Curriculum
The workshop is meant to expand
your skills in deep learning by exposing you to the Tensorflow and to real
world case studies. You will learn:
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Review
of fundamental deep learning architectures (Fully Connected, Convolutional,
Recurrent)
Ø
Build
and train a model with pure Tensorflow
Ø
Custom
architectures and loss functions
Ø
Setting up a
machine for deep learning / serving a model
Activities
The workshop is conceived to
maximize the learning experience for everyone and includes 30% theory and 70%
hands-on practice.
Are there any prerequisites?
Previous experience programming in
Python or in other languages is advised to make best use of the workshop.
Additionally, familiarity with machine learning and deep learning is necessary.
Why python?
In the last 2 years Python has
become a de-facto standard in data science and is widely adopted by most major
companies. Reasons for this success include:
Ø
large
set of mature data science libraries => most needs covered
Ø
worldwide
community of enthusiasts => get help when you need it
Ø
easy
to learn, read and write => start contributing immediately
Ø
supports
both functional and object oriented coding => versatile and powerful
Ø
full
stack programming language => easier interaction between data scientists and
software engineers
Why Tensorflow?
There are many open source Deep
Learning libraries. Tensorflow is backed by Google and is quickly becoming one
of the most used libraries in the fields. It has a large and growing community
of users and it is versatile and easy to learn. Highlights include
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largest
community of developers
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state
of the art models and nodes
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high
scalability, can be distributed on many GPUs
Ø
production
performance and deployment tools
Ø
very
versatile and powerful for distributed high performance computing beyond neural
networks
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Agenda
v What Is Machine Learning ?
v Limitations of Machine Learning
v Deep Learning To The Rescue
v What Is Deep Learning ?
v Deep Learning Applications
v Difference Between Machine Learning and Deep
Learning
v What is TensorFlow?
v TensorFlow Data Structures
v TensorFlow Use-Case
v How Deep Learning Works?
v Single Layer Perceptron (Early Deep Learning Models)
v Single Layer Perceptron Examples
v Limitations Of Single Layer Perceptron
v Multi Layer Perceptron
v Multi Layer Perceptron Examples
v Demo – Keras
v Why Not /Feedforward Networks?
v What Is Recurrent Neural Network?
v Issues With Recurrent Neural Networks
v Vanishing And Exploding Gradient
v How To Overcome These Challenges?
v Long Short Term Memory Units
v LSTM Use-Case
