DATA SCIENCE,MACHINE LEARNING,NEURAL NETWORKS

Maxim Orlovsky, PhD, MD

CloudBusinessCity, Mentor (cloudbusinesscity.com)GRPIIIQ, CEO (qoderoom.com, banqsystems.com)

BICA Labs, Head (bicalabs.org)

INTRODUCTION

#CloudBusinessCity #MSRoadShowDataScience

ComputerScienceData

Science

Machine Learning

Cognitive Science

Artificial Intelligence

BIG DATA• Volume• Velocity• Variety

• Variability• Veracity

• analysis• capture• data curation• search• sharing• storage

• transfer• visualization• querying

• updating • information privacy

DATA MINING

computational process of

discovering patterns in large data

sets involving methods at the

intersection of artificial intelligence,

machine learning, statistics, and

database systems

Pre-Data Science Buzzword J

DATA SCIENCE• Part of Computer Science• Interdisciplinary field• Data -> Knowledge

• Predictive analytics

CLOUD COMPUTINGshared computer processing resources and data to computers and other devices on demand

Cloud computing reduces cost of Data Science research and lowers entering threshold for startups

ComputerScienceData

Science

Machine Learning

Cognitive Science

“

”MACHINE LEARNINGGIVES COMPUTERS THE ABILITY TO LEARN WITHOUT BEING EXPLICITLY PROGRAMMED

Arthur Samuel, 1959

THE DIFFERENCE BETWEEN ML AND PROGRAMMING

Programming Machine Learning

Result of program Deterministic Non-deterministic

Program Code Architecture

Data storage External Embedded

Changeability By human By machine

MACHINE LEARNING IS MORE THEN AI

• Clustering• Regression• Dimensionality reduction• Decision trees• Genetic and evolutionary algorithms

Machine learning is when computer updates it’s own algorithm depending on the data or its result

TYPES OF MACHINE LEARNING

• Supervised: when you know what’s right and wrong (i.e. have labelled training sets)

• Non-supervised:when you don’t know right answers/there is no labelled training sets

• Reinforced:combination of supervised and unsupervised learning;similar to human’s learning

K-METHODS

• k-Means Clustering: partition nobservations into k clusters

• k-Nearest Neighbors: assign class according to the environment

GENETIC AND EVOLUTIONARY ALGORITHMS

Classical Algorithm Genetic Algorithm

Generates a single point at each iteration. The sequence of points approaches an optimal solution.

Generates a population of points at each iteration. The best point in the population approaches an optimal solution.

Selects the next point in the sequence by a deterministic computation.

Selects the next population by computation which uses random number generators.

ComputerScienceData

Science

Machine Learning

Cognitive Science

COGNITIVE SCIENCEexamines the nature, the tasks, and the functions of cognition

• language• perception• memory

• attention• reasoning• emotion

ComputerScienceData

Science

Machine Learning

Cognitive Science

Artificial Intelligence

AI: TYPES

• Specialized: performs only one task or subset of tasks, usually better then humans(compare to dogs, that smell better then we do)

• Generic (human level and super-human)

MACHINE POWER: MOOR’S LAW

WHEN GENERIC AI WILL APPEAR?

WHEN GENERIC AI WILL APPEAR?

BRAIN VS AI

Brain• Massive parallelism:

100 000 000 000 “cores”• Extreme “bandwidth”:

700 000 000 000 000 connections between “cores”

• ~10^18 “transistors”• Asynchronous• Adaptive hardware: neuroplasticity• “Analog”, but suitable for differential

and integral computations

Present day computer

• Non-parallel architecture

• Low bandwidth

• ~10^9 transistors

• Synchronous (clock rate)

• Static hardware

• Digital, but linear computations

SPECIALIZED AI CREATES MORE RISKS THEN GENERIC

NEURAL NETWORKS

UNDERSTANDING NEURAL NETWORKS

UNDERSTANDING NEURAL NETWORKS #1

UNDERSTANDING NEURAL NETWORKS

Neural network as a graph of gateways

* +w

b

UNDERSTANDING NEURAL NETWORKS: HERE COMES TENSORS

WEIGHTS AND BIASES: HOW DOES THIS WORK

HOW NN CLASSIFIES

AI DISRUPTION 2016: KEY FACTORS

1. Machine Power and Cloud Computing2. Big Data and its availability3. Frameworks and ready-to-go cloud APIs

STARTUP TODO

Design a product with USP and then1. Look for the source of data2. Find what you can personalize3. Use cloud computing power4. Use ready-to-go APIs when available5. Don’t be afraid of creating and training own neural nets6. Always use a proper ready-to-go framework for that purpose

STARTUP TODO

Product

Data

Added value

AI dev/trainig

NEURAL NETWORKS OVERVIEW

ARCHITECTURES

• Linear / recurrent• Non-deep / deep• Deterministic / probability• Supervised / unsupervised / reinforced

APPLICATIONS

• Computer vision• NLP• Translation• Text-to-speech and vice verse• Generative methods• Personalization and adaptive methods• Complex solutions implementing different types of AI to obtain a cohesive

result

GENERATIVE METHODS AND PRODUCTS

GENERATIVE METHODS AND PRODUCTS

THE NEXT REMBRANDThttps://www.nextrembrandt.com

“We now had a digital file true to Rembrandt’s style in content, shapes, and lighting. But paintings aren’t just 2D — they have a remarkable three-dimensionality that comes from brushstrokes and layers of paint. To recreate this texture, we had to study 3D scans of Rembrandt’s paintings and analyze the intricate layers on top of the canvas.”

CONTACTS

Maxim OrlovskyAbout.me profile (all social networks):

BICA LabsScientific enquiries:

QoderoomBusiness enquiries:

CTO PART

MODERN NEURAL NETWORK ARCHITECTURES AND HOW THEY WORK

NN MECHANICS

CONVOLUTION FILTER (LAPLASSIAN)

HOW CONVOLUTION HAPPENS INSIDE NEURAL NETWORK

CONVOLUTION LAYER

MICROSOFT MSRA PROJECT

GENERATING HOUSE NUMBERS WITH RNN

Credits: Fei-Fei Li & Andrej Karpathy & Justin Johnson, Stanford University

LONG SHORT-TERM MEMORY (LSTM): BEST RECURRENT ARCHITECTURE

LSTM: SIMPLIFICATION

“Memory”

New data

Previous result Output

“Forget andremember”

Correct by“recalling”

DESIGNING NEURAL NET WITH YOUR DATA

1. Find a way to embed data2. Understand what you’d like to receive from network3. Design proper network architecture:

1. Use recurrent networks for time-based data2. Use LSTM networks if time intervals between the data are large or non-even3. Select number of layers according to data dimensionality4. Has training set? Use supervised learning. Otherwise – reinforced.

4. Visualize and re-iterate hundreds of times5. PROFIT!

FRAMEWORKS

• TensorFlow• Teano• Torch• CNTK• Caffe

CAFFE

• http://caffe.berkeleyvision.org

• From Berkley University• Written in C++• Create networks in Protocol Buffers: no

need to write code• Has Python and MATLAB bindings • Good for feedforward networks • Good for finetuning existing networks • Not good for recurrent networks

layer {

name: "ip1"

type: "InnerProduct"

param { lr_mult: 1 }

param { lr_mult: 2 }

inner_product_param {

num_output: 500

weight_filler {

type: "xavier"

}

bias_filler {

type: "constant"

}

}

bottom: "pool2"

top: "ip1"

}

Credits: Fei-Fei Li & Andrej Karpathy & Justin Johnson, Stanford University

TORCH• http://torch.ch

• From New York University

• Written in C and Lua• Used a lot a Facebook, DeepMind

• Create networks in Lua• You usually write your own training code • Lots of modular pieces that are easy to combine • Less plug-and-play than Caffe

• Easy to write your own layer types and run on GPU

• Not good for recurrent networks

Credits: Fei-Fei Li & Andrej Karpathy & Justin Johnson, Stanford University

LUA

• High level scripting language, easy to interface with C • Similar to Javascript:

• One data structure: table == JS object

• Prototypical inheritance metatable == JS prototype

• First-class functions • Downsides:

• 1-indexed – bad for tensors =( • Variables global by default =( • Small standard library

Credits: Fei-Fei Li & Andrej Karpathy & Justin Johnson, Stanford University

TEANO• http://deeplearning.net/software/theano

• From University of Montreal

• Python + numpy• Embracing computation graphs, symbolic

computation

• RNNs fit nicely in computational graph • Raw Theano is somewhat low-level • High level wrappers (Keras, Lasagne) ease the

pain

• Large models can have long compile times • Much “fatter” than Torch; more magic

Credits: Fei-Fei Li & Andrej Karpathy & Justin Johnson, Stanford University

CNTK – THE MICROSOFT COGNITIVE TOOLKIT

• https://www.cntk.ai

• From Microsoft

• Written in C++• Programmed in Python and C++• BrainScript: powerful abstraction

• Good for both recurrent and convolution nets

TENSORFLOW• https://www.tensorflow.org

• From Google

• Python + numpy• Computational graph abstraction, like Theano;

great for RNNs

• Easy visualizations (TensorBoard)Multi-GPU and mzlti-node training

• Data AND model parallelism; best of all frameworks

• Slower than other frameworks right now • Much “fatter” than Torch; more magic

Credits: Fei-Fei Li & Andrej Karpathy & Justin Johnson, Stanford University

OVERVIEW

Credits: Fei-Fei Li & Andrej Karpathy & Justin Johnson, Stanford University

DATA SCIENCE IN NEURAL NETWORKSDimensionality reduction

T-DISTRIBUTED STOCHASTIC NEIGHBOR EMBEDDING

T-DISTRIBUTED STOCHASTIC NEIGHBOR EMBEDDING

T-DISTRIBUTED STOCHASTIC NEIGHBOR EMBEDDING

COMPUTER TRANSLATION

DATA SCIENCE IN NEURAL NETWORKSInceptionism

INCEPTIONISM

INCEPTIONISM: GENERATING

INCEPTIONISM: ENHANCING

INCEPTIONISM: ITERATIONS

MICROSOFT AZURE MACHINE LEARNING

EXPLORING AZURE COGNITIVE SERVICESDemo

USING AZURE ML TEXT ANALYTICS APIDemo

USING AZURE DEEP LEARNING INSTANCESDemo

FURTHER READING

Christopher OlahEx Google Brain project member

Andrej KarpathyDeepMind, Open AI, Stanford

Stanford CS231nNeural networks & computer vision

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