Deep Learning is an implementation of Machine Learning that uses neural networks to solve difficult problems such as image recognition, sentiment analysis and recommendations. Neural networks simulate the functions of the brain where artificial neurons work in concert to detect patterns in data. This allows deep learning algorithms to classify, predict and recommend with an increasing degree of accuracy as more data is trained in the network. This workshop will walk you through the basics of Deep Learning, introduce you to a very powerful open-source Deep Learning framework called Apache MXNet and guide you in training a neural network using Apache MXNet on AWS.

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Osemeke Isibor,
Solutions Architect.
iosemeke@amazon.com
22nd November 2017
What is Deep Learning, uses and
benefits

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Objectives
• Help you understand what Deep Learning is.
• Encourage you to start thinking where you can apply Deep
Learning in your organization or business.
• Master how to create deep learning algorithms and training
models
• Motivate you to acquire more knowledge and get started
with Deep Learning.

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Agenda
• Introduction
• The basics of Deep Learning
• Deep Learning case studies
• Conclusion

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Introduction
Artificial Intelligence
Machine Learning
Deep Learning

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Types of Machine Learning
Supervised learning
Human intervention and validation required
e.g. Photo classification and tagging
Unsupervised learning
No human intervention required
e.g. Auto-classification of documents based on context

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Supervised Machine Learning Process
• Input feature selection –
• what are my predictions going to be based on.
• Target –
• what you want to predict.
• Prediction function –
• regression, classification, dimensionality reduction e.t.c

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Model Performance Measurement
All Labeled Dataset
Training Data
70% 30%
Training
Test
Data
Evaluation
Result
Trial
Model
Accuracy

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Machine Learning – When to Use It
You need ML if:
•Simple classification rules are inadequate
•Scalability is an issue with large number of datasets
You do not need ML if:
•You can predict the answers by using simple rules and computations
•You can program predetermined steps without needing any data driven learning

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Going Beyond Machine Learning

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Deep Learning – Advanced Machine Learning

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What is Deep Learning?
Deep learning is a class of machine learning algorithms that:
•Use a cascade of many layers of nonlinear processing units for feature
extraction and transformation.
•Each successive layer uses the output from the previous layer as input. The
algorithms may be supervised or unsupervised and applications include
pattern analysis (unsupervised) and classification (supervised).
•Learn multiple levels of representations that correspond to different levels of
abstraction; the levels form a hierarchy of concepts.

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Performance of Deep Learning Vs Machine Learning
Data
Performance
Traditional Machine Learning
Algorithms
Deep Learning Algorithms

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The Secret Sauce – Neural Networks
A collection of simple, trainable mathematical units that collectively learn complex functions
ü Output
Neural network
Input
Hidden layers

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Human Brain Neuron
Inputs Output

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Deep Learning Training – Image Classification
X Seal
ü Grizzly Bear
ü Polar Bear
ü Dog
ü Fox
Feedback / Back Propagation
Neural network

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Deep Learning – Data Representation
Hierarchy of representations
• Image – vectors of pixel, motif, part, contour, edge, etc.
• Videos – Image frames, pixels per frame, deltas per frame, etc.
• Text – characters, words, clauses, sentences, etc.
• Speech – audio, band, frequency, wavelengths, modulations, phonetics,
etc.

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Deep Learning – Advantages
• Features automatically deduced and optimally tuned for the desired
outcome
• Robustness to variations automatically learned
• Reusability – same neural network approach can be used for many
applications and data types
• Massively parallel computations through use of GPUs – scalable for
large volumes of data

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Some application of Deep Learning using AWS

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And Many more use cases
ASR/NLU Language Translation Self Driving Cars
Playing Go Financial Risk Medical Diagnosis

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Conclusion: Deep Learning is very very Powerful
• Tons of applications and use cases but NOT for ALL cases.
• Many algorithms, frameworks and tools.
• Easy and cost effective to start and experiment on AWS using latest
generation GPU instances.

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Get Started
https://aws.amazon.com/deep-learning/

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Thank You
Osemeke Isibor,
Solutions Architect
iosemeke@amazon.com