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Deep Learning Made Easy with Deep Cognition


So normally we do Deep Learning programming, and learning new APIs, some harder than others, some are really easy an expressive like Keras, but how about a visual API to create and deploy Deep Learning solutions with the click of a button? This is the promise of Deep Cognition.



Deep Cognition

 
So normally we do Deep Learning programming, and learning new APIs, some harder than others, some are really easy an expressive like Keras, but how about a visual API to create and deploy Deep Learning solutions with the click of a button?

This is the promise of Deep Cognition.

As they say The Deep Cognition platform was founded to “democratize AI”.

Artificial intelligence is already creating significant value for the world economy. There is a (big) shortage of AI expertise though that creates a significant barrier for organizations ready to adopt AI. And this is what they are solving.

Their platform, Deep Learning Studio is available as cloud solution, Desktop Solution (deepcognition.ai/desktop/) where software will run on your machine or Enterprise Solution (Private Cloud or On Premise solution).

The Desktop version allows people to use their own computers with GPU without hourly fee.

For this we will be using the Cloud version of the Deep Learning Studio. This is a single-user solution for creating and deploying AI. The simple drag & drop interface helps you design deep learning models with ease.

Pre-trained models as well as use built-in assistive features simplify and accelerate the model development process. You can import model code and edit the model with the visual interface. The platform automatically saves each model version as you iterate and tune hyperparameters to improve performance. You can compare performance across versions to find your optimal design.

MNIST with Deep Cognition and AutoML

Deep Learning Studio can automagically design a deep learning model for your custom dataset thanks to our advance AutoML feature. You will have good performing model up and running in minutes.

And yes AutoML is what you think, automatic Machine Learning, here applied specifically to Deep Learning, and it will create for you a whole pipeline to go from raw data into predictions.

As a small tutorial / try, of the Deep Learning Studio let’s study the classical MNIST.

MNIST is a simple computer vision dataset. It consists of images of handwritten digits like these:

It also includes labels for each image, telling us which digit it is.

Let’s train a model to look at images and predict what digits they are using Deep Cognition Cloud Studio and AutoML.

When you have an account you just need to enter in the http://deepcognition.ai webpage and click on Launch Cloud App.

Now this will take you to the UI, you’ll see that you can choose from some sample projects:

Or create a new project that is what we are going to do now:

This will take you to a page where you can choose the training-validation-test ratio, load a dataset or used an already uploaded one, specify the types of your data and more.

The Model tab will allow you to create your own models using advance Deep Learning features and different types of layers and neural networks, but we will use the AutoML feature so Deep Cognition take care of all of the modeling:

We choose Image because this is the type of data that we are trying to predict.

After you click Design you will have your first DL model available to customize and analyze:

The model looks like this:

So you can see that all the complexity of modeling for Deep Learning and coding has been simplified a LOT with this great platform.

If you want you can also code in a Jupyter Notevook inside the platform, with all the necessary installations already done for you:

The reason is that neural networks are notoriously difficult to configure and there are a lot of parameters that need to be set. Hyperparameter tuning is the hardest in neural network in comparison to any other machine learning algorithm.

But with Deep Cognition this can be done really easy and in a very flexible way, in the HyperParameters tab you can choose from several Loss functions and Optimizers to tune your parameters.

Now the funny part. Training your model. In the Training tab you can choose from different types of instances (with CPU and GPU) support to to this. It will also help you monitor your traning process and create a Loss and Accuracy graph for you:

Above there is an small gif of the training process.

The results your traninig can be found in the Results tab. You will have there all your runs.

And finally you can use this model you have trained for the testing and validation set (or other you can upload) and see how well it performs when predicting the digit from an image.

 

The blackbox problem

 
Something that will come yo your mind is: ok I’m doing deep learning but I have no idea how.

Because of that you can actually download the code that produced the predictions, and as you will see it is written in Keras. You can then upload the code and test it with the notebook that the system provides.

The AutoML features have the best of Keras and other DL frameworks in a simple click, and the good thing about it is that it chooses the best practices for DL for you, and if you are not completely happy with the choices you can change them really easy in the UI or interact with the notebook.

This system is built with the premise of making AI easy for everyone, you don’t have to be an expert when creating this complex models, but my recommendation is that is good that you have an idea of what you are doing, read some of the TensorFlow or Keras documentation, watch some videos and be informed. If you are an expert in the subject great! This will make your life much easier and you can still apply your expertise when building the models.

Remember checking the references for more information about Deep Learning and AI.

 
References

 

Bio: Favio Vázquez is a physicist and computer engineer. He holds a Master’s Degree in Physical Sciences from UNAM. He works in Big Data, Data Science, Machine Learning and Computational Cosmology. Since 2015, he has been part of the collaboration of Apache Spark in the Core and MLlib library. He’s Chief Data Scientist at Iron performing distributed processing, data analysis, machine learning and directing data projects for the company. In addition, he works at BBVA Data & Analytics as a data scientist performing machine learning, doing data analysis, maintaining the life cycles of the projects and models with Apache Spark.

Original. Reposted with permission.

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