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).

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).

Imagine you were interested in solving a certain problem using Reinforcement learning. You have coded in your environment and you compile a laundry list of Reinforcement Learning (RL) algorithms to try. Self-implementing the algorithms from scratch is tricky and time-consuming because it requires a lot of trials and contains a lot of implementational tricks. The best answer is to use an RL framework. RL framework contains near-optimal implementations of RL algorithms.

MathWorks has introduced the new Release 2020a with expanded AI capabilities for deep learning. The release introduces an enhanced Deep Learning Toolbox that helps users manage multiple deep learning experiments, keep track of training parameters, analyse and compare results and code with the new Experiment Manager app. It can also interactively train a network for image classification, generate MATLAB code for training, and access pretrained models with Deep Network Designer app. Engineers can now train neural networks in the updated Deep Network Designer app, manage multiple deep learning experiments in a new Experiment Manager app, and choose from more network options to generate deep learning code. R2020a introduces new capabilities specifically for automotive and wireless engineers in addition to hundreds of new and updated features for all users of MATLAB and Simulink.