I met some people that where very pro
HTML5 seeing this as the future. I met some people that where very pro XAML and
see this as the future. After the silent demise of Silverlight it’s a bit
difficult to know where the future is.
On the HTML5 front I intend to catch up
on subjects such as Bootstrap (MVC5), TypeScript, Reactive Binding (MDV ECMA),
ShadowOn, Angular, Bower, Command.js, Node/Grat, Handlebars.js...
I heard a rumor that out of 10 projects
MS started in HTML5 7 have been rewritten in XAML. The pragmatic approach of
hybrid solutions is the way to go. Use HTML5 when it makes sense but be aware
there is a cost associated with its use. WPF is a more elegant solution because
it uses OO and properly separates concerns. But when you need customer reach
then HTML5 is the way to go but be careful that the target customers have
browsers capable of handling html5,
A friend of mine will be working
together with xamarin to provide a VS template with MVVM Light for ios native
apps with portable c# libraries
Here’s a session that I can highly
recommend
4-554 Building
Big: Lessons Learned from Windows Azure Customers
It’s not likely that I will be writing software that
needs to scale in quite the same way as described in this session. The real
life examples where very interesting and definitely worth watching.
Another session that is definitely worth watching is
AZ-18
Securing Windows Store Applications and REST services with Active Directory
The talk was arrange around a story and
was very good:
- The
story started around an isolated corporate network that had users,
resources and access control that could be administered easily.
- Then
along comes an external resource that needs to be accessed by domain users
and the administrator looks a little less happy
- Next
external users need to access domain resources which really upsets the
administrator
- Finally
BYOD need to access domain resources (Vittorio then drew the picture of
the screem)
REST OAuth2
- A user
enters a code on an authorization endpoint
- The
user reçoives a code
- The
user sends this code to a token endpoint and receives am Authorization
Token
- This
Token can then be used to access external resources
- There
is a Reentry token that allows the authorization token to be cached for a
limited period of time
Windows Azure Active Directory
- This
can be stand alone or a synchronized part of an on premise AD directory
- Supports
OAuth2, SAML-P, WS-Federation and has metadata end points
- There
is a OneClient preview in the Azure portal that is used to maintain the Azure
AD
- Windows
Azure Authentication Library (AAL)See presentation for links how to use this
Essential AAL Ussage
- Authenticate
the user to get a token:AuthenticationContext aCtx= new AuthenticationContext(AuthenticationResult = result = await authorizationContext.AquireTokenAsync(http://host.com/shipmentservice, clientId);
- Use the
token to invoke a REST serviceHttpClient httpClient = new HttpClient();httpClient.DefaultRequestHeaders.Authorization = new AuthorizationHeaderValue("Bearer", result.AccessToken);
Although I don’t have a direct application of Neuron
networks this talk was really well presented:
2-401
Developing Neural Networks Using Visual Studio
Agenda
- What
types of problems does a neural network solve
- What
exactly is a neural network
- How
does a neural network actual work
- Understanding
activation functions
- Alternatives
to neural networks
- Understanding
neural networks training
- Neural
network over-fitting
- Developing
with Visual Studio
- Summary
and resources
What types of problems does a neural
network solve
Tabular information where you have some
inputs (independent variables) to produce an output (the thing you want to
predict). The idea is that you have some training data that is used to fit
internal variables of the neural network after which you have a system that can
predict an output from a given set of inputs
What exactly is a neural network
The inputs are normalized, Boolean
variables are converted to -1 and +1, enumerations to a set of individual
inputs that are set to 0 or 1. There are then used in the input nodes of the
neural network. Then a to be determined number of hidden nodes evaluate a
function based on all these inputs to produce a set of output nodes.
Activation Functions
- Logistic sigmoid output between [0,1] y=1.0/(1.0 +exp(x))
- Hyperbolic tangent output value between [-1, +1] y = tanh(x) = (ex - e-x)/(ex + e-x)
- Heaviside step output value between [0,1] if (x<0 else="" if="" then="" x="" y="0">=0) then y=1
- Softmax output between [0,1] and sum to 1.0 y=(e-x)/Sum(e-xj)
The ability to customize these functions means that it is often better top
write your own neural network
Alternatives to neural networks
- Linear
regression y = a x1 + bx2 + .....
- Logistic
regression y = 1.o/(1,0+e-(ax1 + bx2 + ... + k))
- Naive
Bayes: assumes input data are all independent and output is binary
- Decision
trees
- Support
vector machines: extremely complex implementation, assumes binary output
Neural networks pros and cons
- Pro:
can model any underlying math equation!
- Pro:
can handle multinomial output without resorting to tricks.
- Con:
moderate complexity, requires lots of training data.
- Con:
must pick number hidden nodes, activation functions, input/output
encoding, error definition.
- Con:
must pick training method, training “free parameters,” (and over-fitting defense strategy).
Training
Back-propagation
Fastest technique.
Does not work with Heaviside activation.
Requires “learning rate” and “momentum.”
Genetic algorithm
Slowest technique.
Generally most effective.
Requires “population size,” “mutation rate,” “max generations,” “selection
probability.”
Particle swarm optimization
Good compromise.
Requires “number particles,” “max iterations,” “cognitive weight,” “social
weight.”
Avoiding Over-fitting
What is it?
Symptom: Model is great on predicting existing data, but fails miserably on new
data.
Roulette example: red, red, black, red, red, black, red, red, black, red, red,
??
A serious problem for all classification/prediction techniques, not just neural
networks.
Five most common techniques
Use lots of training data.
Train-Validate-Test (early stop when error on validate set begins to increase).
K-fold cross validation.
Repeated sub-sampling validation.
Jittering: deliberately adding noise data to make over-fitting impossible.
Quite a few exotic techniques also available (weight penalties, Bayesian
learning, etc.).
Summary
Existing neural network tools are
difficult or impossible to integrate into a software system.
Commercial and Open Source API libraries work well for some machine learning
tasks but are extremely limited for neural networks.
To develop neural networks using Visual Studio you must understand seven core
concepts: feed-forward, activation, data encoding, error, training, free
parameters, and over-fitting.
Once the concepts are mastered, implementation with Visual Studio
is not difficult (but not easy either).
