Before, when describing the simple perceptron, I said that a result is calculated in a neuron, e.g. by summing up all the incoming data multiplied by weights. However, this has one big disadvantage: such an approach would only enable our neural net to learn linearrelationships between data. In order to be able to learn (you can also say approximate) any mathematical problem – no matter how complex – we use activation functions. Activation functions normalize the output of a neuron, e.g. to values between -1 and 1, (Tanh), 0 and 1 (Sigmoid) or by setting negative values to 0 (Rectified Linear Units, ReLU). In H2O we can choose between Tanh, Tanh with Dropout, Rectifier (default), Rectifier with Dropout, Maxout and Maxout with Dropout. Let’s choose Rectifier with Dropout. Dropout is used to improve the generalizability of neural nets by randomly setting a given proportion of nodes to 0. The dropout rate in H2O is specified with two arguments:
hidden_dropout_ratios, which per default sets 50% of hidden (more on that in a minute) nodes to 0. Here, I want to reduce that proportion to 20% but let’s talk about hidden layers and hidden nodes first. In addition to hidden dropout, H2O let’s us specify a dropout for the input layer with
input_dropout_ratio. This argument is deactivated by default and this is how we will leave it.
Read the whole thing and, if you understand German, check out the video as well.
With the Execute R Script module you can immediately use more than 650 R packages which come preinstalled in the Azure ML Studio environment. You can also use other R packages (including packages not on CRAN) and source in R scripts you develop elsewhere (as shown above), although this does require the time to install them in the Studio environment. You can even create custom ML Studio models encapsulating R code for others to use in the drag-and-drop environment.
If you’re new to Azure ML Studio, check out the Quickstart Tutorial for R to learn how use the Execute R Script module, and to check out what’s new in the latest update follow the link below.
Click through for more details.
For small-scale server-side deployments both frameworks are easy to wrap in e.g. a Flask web server.
For mobile and embedded deployments, TensorFlow works really well. This is more than what can be said of most other deep learning frameworks including PyTorch.
Deploying to Android or iOS does require a non-trivial amount of work in TensorFlow.
You don’t have to rewrite the entire inference portion of your model in Java or C++.
Other than performance, one of the noticeable features of TensorFlow Serving is that models can be hot-swapped easily without bringing the service down.
Read on for the full comparison.
You need to create a model in Azure ML Studio and create a web service for it.
The traditional example in Predict a passenger on Titanic ship is going to survived or not?
we have a dataset about passengers like their age, gender, and passenger class, then we are going to predict whether they are going to survive or not
Open Azure ML Studio and follow the steps to create a model for predicting this. Navigate to Azure ML Studio.
Then download the dataset for titanic from here
Click through for the step-by-step instructions.
For each of these images, I am running the
predict()function of Keras with the VGG16 model. Because I excluded the last layers of the model, this function will not actually return any class predictions as it would normally do; instead we will get the output of the last layer:
These, we can use as learned features (or abstractions) of the images. Running this part of the code takes several minutes, so I save the output to a RData file (because I samples randomly, the classes you see below might not be the same as in the
Read the whole thing.
So, when I read What’s new in SQL Server 2019, I came across a lot of interesting “stuff”, but one thing that stood out was Java language programmability extensions. In essence, it allows us to execute Java code in SQL Server by using a pre-built Java language extension! The way it works is as with R and Python; the code executes outside of the SQL Server engine, and you use
sp_execute_external_scriptas the entry-point.
I haven’t had time to execute any Java code as of yet, but in the coming days, I definitely will drill into this. Something I noticed is that the architecture for SQL Server Machine Learning Services has changed (or had additions to it).
That Java support is for Spark, I’d imagine. And I hope they allow for Scala.
SQL Server 2019 big data clusters provide a complete AI platform. Data can be easily ingested via Spark Streaming or traditional SQL inserts and stored in HDFS, relational tables, graph, or JSON/XML. Data can be prepared by using either Spark jobs or Transact-SQL (T-SQL) queries and fed into machine learning model training routines in either Spark or the SQL Server master instance using a variety of programming languages, including Java, Python, R, and Scala. The resulting models can then be operationalized in batch scoring jobs in Spark, in T-SQL stored procedures for real-time scoring, or encapsulated in REST API containers hosted in the big data cluster.
SQL Server big data clusters provide all the tools and systems to ingest, store, and prepare data for analysis as well as to train the machine learning models, store the models, and operationalize them.
Data can be ingested using Spark Streaming, by inserting data directly to HDFS through the HDFS API, or by inserting data into SQL Server through standard T-SQL insert queries. The data can be stored in files in HDFS, or partitioned and stored in data pools, or stored in the SQL Server master instance in tables, graph, or JSON/XML. Either T-SQL or Spark can be used to prepare data by running batch jobs to transform the data, aggregate it, or perform other data wrangling tasks.
Data scientists can choose either to use SQL Server Machine Learning Services in the master instance to run R, Python, or Java model training scripts or to use Spark. In either case, the full library of open-source machine learning libraries, such as TensorFlow or Caffe, can be used to train models.
Lastly, once the models are trained, they can be operationalized in the SQL Server master instance using real-time, native scoring via the PREDICT function in a stored procedure in the SQL Server master instance; or you can use batch scoring over the data in HDFS with Spark. Alternatively, using tools provided with the big data cluster, data engineers can easily wrap the model in a REST API and provision the API + model as a container on the big data cluster as a scoring microservice for easy integration into any application.
I’ve wanted Spark integration ever since 2016 and we’re going to get it.
One of the key points in Deep Learning is to understand the dimensions of the vector, matrices and/or arrays that the model needs. I found that these are the types supported by Keras.
In Python’s words, it is the shape of the array.
To do a binary classification task, we are going to create a one-hot vector. It works the same way for more than 2 classes.
- The value
1will be the vector
- The value
0will be the vector
Keras provides the
to_categoricalfunction to achieve this goal.
This example doesn’t include using CUDA, but the data sizes are small enough that it doesn’t matter much. H/T R-Bloggers
The term logP(w), which represents our prior, acts as a regularization term. Choosing a Gaussian distribution with mean 0 as the prior, you’ll get the mathematical equivalence of L2 regularization.
Now that we start thinking about neural networks as probabilistic creatures, we can let the fun begin. For start, who says we have to output one set of weights at the end of the training process? What if instead of learning the model’s weights, we learn a distribution over the weights? This will allow us to estimate uncertainty over the weights. So how do we do that?
It’s an interesting approach to the problem.
A great benefit of Confluent MQTT Proxy is simplicity for realizing IoT scenarios without the need for a MQTT Broker. You can forward messages directly from the MQTT devices to Kafka via the MQTT Proxy. This reduces efforts and costs significantly. This is a perfect solution if you “just” want to communicate between Kafka and MQTT devices.
If you want to see the other part of the story (integration with sink applications like Elasticsearch / Grafana), please take a look at the Github project “KSQL for streaming IoT data“. This realizes the integration with ElasticSearch and Grafana via Kafka Connect and the Elastic connector.
Check it out and then take a gander at Kai’s GitHub repo.