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5 Essential Steps For Every Deep Learning Model!

Understanding the 5 significant stages to construct every and any deep learning model

Photo by Fotis Fotopoulos on Unsplash
Photo by Fotis Fotopoulos on Unsplash

The hype of Deep Learning is on a consistent rise and is constantly peaking with the overall continuous progressions, developments, advancements, and improvements.

Deep learning is a sub-field of machine learning which works on concepts of artificial neural networks to perform specific tasks. Artificial neural networks withdraw inspiration from the human brain.

However, it is paramount to note that they do not function theoretically like our brains, not even close! They are named as artificial neural networks as they can complete precise tasks while achieving a desirable accuracy without being explicitly programmed with any specific rules.

The main reason for the failure of AI a few decades ago was due to the lack of data and computation power. However, this has changed significantly over the past few years. The abundance of data is surging every day because big tech giants and multi-national companies are investing in this data. The computational power is also no longer such a big issue due to powerful graphics processing units (GPUs).

In this article, we will discuss in a detailed manner about these five essential stages of deep learning models and how we can encounter these steps to tackle various deep learning projects. Let us get started by analyzing each of these.


1. Defining Your Architecture –

Deep learning is one of the most preferable methods to solve complex tasks like image classification or segmentation, face recognition, object detection, chatbots, and so much more. But, with each of these projects, every deep learning model goes through five fixed stages to accomplish the task at hand.

The first and most significant step of building your deep learning model is to define the network and architecture successfully. Depending on the type of tasks that are being performed, we prefer to use certain types of architecture.

Usually, Convolutional Neural Networks (CNNs) or ConvNets are preferred for computer vision tasks such as image segmentation, image classification, facial recognition, and other similar projects.

While Recurrent Neural Networks (RNNs) and Long Short Term Memory (LSTMs) are preferable for natural language processing and problems related to text data.

In this step, you can also decide the type of model building structure of the entire deep learning architecture. The three main steps for performing this are the Sequential Models, Functional API, or a custom architecture defined by the user. We will discuss each of these methods in further detail in a future article.

2. Compiling Your Model –

With the preferred architecture constructed, we will move on to the next step of the compilation of the built model. The compile step is usually a single line of code in the TensorFlow deep learning framework and can be achieved with the model.compile() function.

The requirement of the compilation step in deep learning is to configure the model for the fitting/training process to be successfully completed. It is during the compile step that some of the critical components of the training procedure is defined for the evaluation procedure.

To mention a few of the necessary parameters, we have the loss, optimizer, and the metrics to be assigned in the following step. The kind of loss is determined by the type of problem we are encountering and what we need to solve. The optimizers are usually adam, RMSprop, or similar optimizers for computations, and the metrics can be accuracy or any other user-defined metrics for analysis.

3. Fit The Model –

After defining the overall architecture and compiling your model, the next logical step is to fit the model on the training dataset. The fit function trains the model for a fixed number of epochs (iterations on a dataset).

The important parameters like the number of epochs for training, the input and output data, validation data, and many others are decided with the help of this function. It is used for computing and calculating these essential features.

The fitting step must be continuously evaluated while the training procedure is underway. It is essential to make sure the model being trained is performing well with improving accuracies as well as a reduction in the overall loss.

It is equally important to consider that the model is not being overfitted in any manner. For this, continuous evaluation with a tool like Tensorboard must be used for analyzing the various graphs and understanding if these models are being overfitted by any chance.

Once the training is completed and analyzed for a fixed number of epochs, we can move ahead to the next step of the evaluation and making predictions with the trained model.

4. Evaluating And Making Predictions –

Evaluation of deep learning models is an extremely significant step to check out if your article is working out as desired. There are chances that the deep learning model that you built might not perform well in real-world applications. Therefore, evaluation of your deep learning models becomes critical.

One main method of evaluating your deep learning models is to ensure that the predictions made by your model on the test data that is split at the beginning of the pre-processing step are considered for the purposes of validating the effectiveness of the trained model.

Apart from the test data, the model must be tested with variable data and random tests as well to see its effectiveness on un-trained data and if its efficiency of performance matches the required commitments.

To explain with a simple example – let us assume that we built a simple face recognition model. Consider the model you have trained with the images and try to evaluate these images with various faces both on the test data and a real-time video reel as well to make sure the trained model is performing well.

5. Deploying The Model –

The deployment stage is the final stage of any model constructed.

Once you have successfully completed building your model, this is an optional step if you want to keep it with yourself or deploy it so that you can target a wider audience.

The methods of deployment vary from deploying it as an application that can be transferred across, or by using the AWS cloud platform provided by amazon for deployment, or by making use of an embedded system.

If you want to deploy something like a security camera, then you can consider using an embedded device like the raspberry pi alongside a camera module for performing this function. Embedded systems with AI are common ways of deploying your IoT projects.

You can also deploy these deep learning models on your website after it is built with either flask, Django, or any other similar framework. Another way to effectively deploy your models is by developing an android or iOS app for smartphone users to reach a wider range of audiences.


Conclusion:

Photo by Tincho Franco on Unsplash
Photo by Tincho Franco on Unsplash

These are the five most essential steps for building every deep learning model. Most of these steps are followed for any deep learning model and will have at least four of the five previously mentioned steps. The deployment step is an optional step for a novice or beginner-level projects, but it is an extremely significant step in industries and companies.

However, an important matter to note is the data preprocessing, exploratory data analysis, and other mandatory cleansing steps must be taken into consideration before building and working with the deep learning models. These steps must be implemented exactly after all the pre-processing of the data is completed.

Feel free to check out some of my other articles from the links provided below.

Top 5 AI Trends That Will Change The Landscape Of The Future!

Top 5 Qualities of Successful Data Scientists!

Beginners Roadmap To Master Data Science

Solutions To Interview Questions On Pattern Programming!

Understanding ReLU: The Most Popular Activation Function in 5 Minutes!

Thank you all for sticking on till the end, and have a wonderful day!


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