Create Your Own Neural Networks With Microsoft Cognitive ToolKit
Do you want to learn the cognitive computing? Then don’t worry because Microsoft has come up with Microsoft Cognitive Toolkit 2.0 version. This toolkit helps in creating, training and evaluating the neural networks, which further helps in building a professional AI. Furthermore, these neural networks can scale efficiently along multiple machines on massive data sets as well as along the multiple GPU’s.
In simple words, a cognitive toolkit which is also known as CNTK is a method to help deep machine learning in image and speech recognition. This toolkit began simplifying the tasks of the scientists in beta on October 2016, since then it is taking care of the production workloads. Furthermore, Microsoft has made it an open source toolkit and a lot of changes, features and improvements have been made to the latest version.
Let us all find out the new features in the latest version of the Microsoft Cognitive Toolkit 2.0.
Java Bindings And Spark Support(Microsoft Cognitive Toolkit)
Now you can evaluate the Cognitive Toolkit models with a new Java API. While training models with either Python or Brian script, Cognitive Toolkit always provide ways to evaluate those models in either Python or BrainScript. But with Java API it becomes easy for the user to integrate deep learning models into their Java-based applications. Furthermore, the platforms like Spark also provide an evaluation at scale.
Keras Support(Microsoft Cognitive Toolkit)
The Keras API is best for the users who want to develop AI applications and it gives best user experience. It reduces the number of user actions to the minimum for common use cases. Furthermore, it reduces cognitive load in the best possible way and gives consistent and simple API’s. Moreover, it gives transparent feedback on the user error. Keras is giving an opportunity to lots of users to learn deep learning without any prior machine learning experience. This improvement in the Keras is enabling many Keras users to benefit from the existing performance of the toolkit without changing their existing Keras version. Microsoft is continuously improving its Keras features.
Model Compression (Microsoft Cognitive Toolkit)
Cognitive Toolkit GA includes a lot of extensions that allow large size implementations of several FP operations. These are quite fast in comparison to their counterparts. It is difficult to evaluate a lower end CPU which are commonly found in mobile products. Furthermore, it is even true for models that are trained for image learning on a video from any kind of camera. The speedup has enough power to enable evaluating Cognitive Toolkit models with very little loss in evaluating with accuracy even on lower power embedded devices.
Built-in Components( Microsoft Cognitive Toolkit)
The Built-in components can take care of the multidimensional data from Python, C++ and BrainScript. Moreover, Generative adversarial networks enable learning, supervised and unsupervised learning. Furthermore, it can add new core components on the GPU from Python. Its hyperparameter tuning is automatic.
Efficiency In Resource Usage (Microsoft Cognitive Computing)
It can work with accuracy on multiple GPU via 1-bit SGD. Memory sharing to even fit in the largest models in GPU memory.
Express Your Networks With All The Ease
It evaluates models with Python, C++, C# and BrainScript. Both high-level and low-level API’s are available for learners, readers and evaluation from Python, C++ and BrainScript. Furthermore, it gives an automatic shape inference based on the data.
Microsoft is using the cognitive toolkit in many of its products. Furthermore, a lot of other companies and many students across the globe are also incorporating the toolkit in their work. Moreover, it gives latest algorithms and techniques to the data scientists and developers.