{Deep Learning

Machine Perception and Its Applications

Adam Gibson // deeplearning4j.org // skymind.io // zipfian

Deep Learning = subset of Machine Learning

Machine Learning = subset of AI AI = Algorithms that repeatedly

optimize themselves. Deep learning = pattern recognition Machines classify data and improve

over time.

DL, a Subset of AI

Why Is DL Hard?

We see this… Machines see this… (Where’s the cat?)

(Hat tip to Andrew Ng)

Anything digitized Raw media: MP3’s, JPEG’s, text, video Sensor output: temperature, pressure,

motion and chemical composition Time-series data: Prices and their

movement; e.g. the stock market, real estate, weather and economic indicators

It’s setting new accuracy records everywhere

What Can It Handle?

Recommendation engines: Anticipate what you will buy or click.

Anomaly detection: Bad outcomes signal themselves in advance: fraud in e-commerce; tumors in X-rays; loans likely to default.

Signal processing: Deep learning can estimate customer lifetime value, necessary inventory or an approaching market crash.

Facial and image recognition

What’s It Good For?

Facial recognition & featurehierarchy

(Hat tip to Andrew Ng)

DL4J Facial Reconstructions

Nets need training data. You know what training sets contain. Nets learn training-set faces by

repeated reconstruction. Reconstruction = finding which facial

features are indicative of larger forms.

When a net can rebuild the training set, it is ready to work with unsupervised data.

How Did It Do That?

Nets measure the difference between their results and a benchmark = loss function

They minimize differences with an optimization function.

They optimize by altering their parameters and testing how changes affect results.

Gradient descent, Conjugate gradient, L-BFGS

Technical Explanation

Learning looks like this.

Note the local minima…

Through pre-training, nets learn to locate signal in a world of noise

Generic priors initiate weights Reconstructions = representations Feature hierarchies intuition about

complex, abstract features

Representation Learning

Facebook engages us more. (95-97% accuracy)

Government agencies identify persons of interest.

Video game makers build more realistic (and stickier) worlds.

Stores identify customers and track behavior, prevent churn and encourage spending.

Facial Recognition’s Uses

Sentiment analysis ~ NLP Software classifies sentences by

emotional tone, bias and intensity Positive or negative - object-specific… Rank movies, books, consumer goods,

politicians, celebrities Predict social unrest, gauge

reputations, PR…

Sentiment Analysis & Text

Restricted Boltzmann Machine (RBMs)

A stack of RBMs. 1st RBM’s hidden layer -> 2nd RBM’s

input layer Feature hierarchy A DBN classifies data. Buckets images: e.g. sunset, elephant,

flower. Useful in search.

Deep-Belief Net (DBN)

Two DBNs. The first DBN encodes data into

vector of 10-30 numbers. The second DBN decodes data back

to original state. Reduce any document/image to

highly compact vector. QA and information retrieval: Watson

Deep Autoencoder

Some Results

Image Search Results

Good with images. ConvNets learn data like images in

patches. Each piece learned is then woven

together in the whole. Yann LeCun’s baby, now at

Facebook.

Convolutional Net

Top-down, hierarchical nets rather than feed-forward like DBNs.

Sequence-based classification, windows of several events, entire scenes rather than images.

Features = vectors. A tensor = multi-dimensional matrix,

or multiple matrices of the same size.

Recursive Neural Tensor Net

RNTNs & Scene Composition

RNTNs & Sentence Parsing

t-SNE for Data Visualizations

DL4J + MNIST + t-SNE