In this blog post I will show how to use a low-code app in MATLAB, the Deep Network Designer, for two different tasks and design paradigms: creating a network from scratch vs. using transfer learning. The process of building deep learning (DL) solutions follows a standard workflow that starts from the problem definition and continues with the steps of collecting and preparing the data, selecting a suitable neural network architecture for the job, training and fine-tuning the network, and eventually deploying the model (Figure 1). The selection of a suitable neural network architecture usually follows the best practices for the application at hand, e.g., the use convolutional neural networks (CNNs or ConvNets) for image classification or recurrent neural networks (RNNs) with long short-term memory (LSTM) cells for text and sequence data types of applications. Transfer learning is an incredibly easy, quick, and popular method for building DL solutions in some domains, such as image classification using neural network architectures pretrained on ImageNet (a large dataset of more than 1 million images in more than 1,000 categories). Essentially, it consists of using a deep neural network that has been pre-trained in a large dataset of similar nature to the problem you are trying to solve. This is usually accomplished by retraining some of its layers (while freezing the others).

The purpose of Computer Vision (CV) is to allow machines to obtain valuable information from their surroundings, by analyzing visual data that can be provided by different sources such as digital images and videos. The nature of such information depends on the final goal of the machine. Think, for example, of self-driving cars. A CV module that is capable of detecting in real-time objects that appear in front of the car is essential to avoid accidents. On the other hand, a robot that has to give directions to people inside a railway station can change the way of speaking based on whether the listener is a child or an adult.

It is imperative for next generation applications to have AI at the core. With almost all major tech players offering AI enabled solutions, we see it as a default feature in any upcoming software products. Many financial institutions have already started their innovation programs by automating existing rule sets with machine learning models to automate/augment existing processes. These models can make automated decisions across vast quantities of data. Even then, organizations are somewhat apprehensive in deploying these systems into the core process, since AI solutions carry a, probably justified, reputation for being'black boxes' characterized by poor transparency.

In this blog post I will show how to use a low-code app in MATLAB, the Deep Network Designer, for two different tasks and design paradigms: creating a network from scratch vs. using transfer learning. The process of building deep learning (DL) solutions follows a standard workflow that starts from the problem definition and continues with the steps of collecting and preparing the data, selecting a suitable neural network architecture for the job, training and fine-tuning the network, and eventually deploying the model (Figure 1). The selection of a suitable neural network architecture usually follows the best practices for the application at hand, e.g., the use convolutional neural networks (CNNs or ConvNets) for image classification or recurrent neural networks (RNNs) with long short-term memory (LSTM) cells for text and sequence data types of applications. Transfer learning is an incredibly easy, quick, and popular method for building DL solutions in some domains, such as image classification – using neural network architectures pretrained on ImageNet (a large dataset of more than 1 million images in more than 1,000 categories). Essentially, it consists of using a deep neural network that has been pre-trained in a large dataset of similar nature to the problem you are trying to solve. This is usually accomplished by retraining some of its layers (while freezing the others).

Learn how HPE, NVIDIA, WekaIO, and Mellanox have designed a deep learning architecture that accelerates AI insights. Deep learning (DL) architectures offer organizations a way to accelerate AI insights, enabling them to process hundreds of millions of data points and generate AI-based analytics--without slowing down their systems. At HPE, we're offering our technology and expertise to data scientists, solution builders, and IT personnel who recognize the need to successfully implement AI projects. We understand the unique needs of organizations that might hesitate to build the complex IT infrastructure needed to deliver AI insights--which is why we've designed solutions that make it easy for them. To build a storage solution that could accelerate AI training and inferencing, we've collaborated with our partners to develop a scalable, shared storage solution that runs on a neural network.