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Deep Learning through Deep Cognition

Deep Learning is fun and amazing. Deep Learning (DL) is a subarea of Machine Learning which deals with the Deep Neural Networks (with emphasis on learning through successive “hidden layers”) and significant algorithms for the preprocessing of data and model regularization. DL has proved to be an amazing field that helps create great solutions and solve problems in the data science world with computer vision and NLP problems. It allows complex computational models, composed of multiple processing layers, to learn patterns in data with multiple levels of abstraction just like a black box. The Deep Neural Network represents a learned neural network, represented as layers stacked one after the other. DL basically uses artificial neural network (ANN), from AI, an artificial network inspired by biological neurons, which are used for function estimation that depend on a large number of unknown inputs.

DL gained prominence in the early 2010s and hence, it has achieved many breakthroughs, namely Near-human level image classification and speech recognition, Improved machine translation, Personal Digital Assistants such as Amazon Alexa or Echo, etc. With the start of this decade, there has been a significant rise in the development of many simple but important DL algorithms, advances in hardware (mostly inventions with GPUs), and the exponential generation and gathering of structured and unstructured data.

Nowadays it is very easy to do DL, as there are many available frameworks and libraries, platforms and much more. But the one creating a ripple here is Deep Cognition. With its feature-oriented APIs, Deep Cognition is the new front which has significantly made its place into several organizations and is helping them adopt DL and AI. Normally we do DL programming, and learn to use APIs, some with complex usability features and some easy and expressive like Keras. But Deep Cognition, a feature-oriented visual API to build and deploy DL solutions, promises processing just a click away. The Deep Cognition platform was founded to boost AI development. With the help of these APIs, it is now possible to develop and run small DL models on personal desktops or in the cloud. It shows new ways in which a user can interact with the computer to do DL. Deep Cognition is a single-user solution for creating and deploying AI. It is available both as Cloud Solution and Desktop Solution where the software will run on the local machine or Enterprise Cloud, allowing people to use their own computers with GPU without any charges or a pay-as-you-go subscription for cloud GPU tenants.

The reason behind developing such an interactive software is that NNs are difficult to configure and there are a lot of parameters that need to be set, a lot of features to be aligned with the model design, etc. With Deep Cognition, Hyperparameter tuning and Feature importance configuration can be done in a very flexible way, wherein the optimal hyperparameter can be chosen from several Loss functions and Optimizers and the important features can be refined accordingly. It allows different types of training instances (with CPU and GPU) support to do this and also helps in monitoring epochs and create a Loss and Accuracy graph for each training iteration through a small gif of the training process. The results for each training can be viewed and can be used to alter/cross validate the testing and validation set and check the precision and recall of the model. Pre-trained models such as VGG16, ResNet and Inception V3, as well as built-in assistive features can be used to accelerate the model development process. Deep Cognition delivers a simple but powerful GUI with existing NNs that can be dragged and dropped and also supports DL model creation with AutoML. It also caters to a developer’s need with a full autonomous IDE to code and interact with required libraries. The tool also supports model code import and edit with the visual interfaces. The platform saves each model result after an epoch and hyperparameters, for tuning features, to improve model performance. The performance can be compared across different training versions to find the optimal design.

So, a lot of the complexity of modeling for DL and coding has been simplified with this great platform. Of course, alternatives to this exist too, such as Azure ML Studio, Salesforce Eintien, Google Machine Learning Engine, etc. But Deep Cognition will enhance expertise when building the model as the code that produces the predictions, is written in Keras, a high-level NN API, written in Python, that supports running on top of any framework such as TensorFlow, CNTK, or Theano. It is also possible to upload code snippets and test it with the in-built notebook that the system provides.

GUIs are most likely to take over coding platforms in the near future to get things done with DL. DL algorithms are now packed inside complex code snippets, and so having a GUI, that is using a visual layout to organize the system, is a boon toward enabling non-data scientists to become the architects of DL models as the technology begins to spread its roots in the industry. Its simplified orchestration aspect is easier to understand. Greater accessibility for non-data scientists also means that DL modelers or developers without specialized training can directly take advantage of building an AI technology as the society grapples with the darker side of pervasive DL algorithms.

Comments
  • vartika April 14, 2019 7:18 am

    Nice Blog

    Reply
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