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5 tips to improve data quality before training a neural network from Jaydevs Co-Founder and Managing Partner, Alexander Sivov 👇

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First, the data should cover as much of what we want to train the neural network as possible. For example, if we are solving the problem of recognizing chairs, then the data should include all types of chairs: office, bar, stools, etc.

In addition to this, the chairs should be presented in different turns/ projections, with different lighting.

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It is also crucial that there is a relatively even distribution of data in the dataset to provide no gaps.

These gaps are extremely bad: when the network starts working on the real data and the data that was in these gaps comes across, you can get completely inadequate results.

To fix this, synthetic data is sometimes created. This is not the best case, and also leads to other issues, but better than having gaps anyway.

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When we're preparing the data, we need to form 3 datasets. One dataset we'll use to train the neural network. The second one is for testing it. And the third is the validation one. All three datasets must not overlap with each other.

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When a neural network is trained it recognizes the data as a set of features. So it’s essential to identify which set of features the network will learn from.

Thus the data must be prepared (labeled) to show a machine learning model the target features. This is done by labelers.

Once the data is labeled, it is important to make sure it is consistent, and everything is labeled according to the same rules.

A Data Scientist is usually in charge of determining what data is needed for input, preparing rules, preparing sample datasets, and quality control.

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The last thing that matters is the feedback of the ML engineer who works with the data while training the network. For example, the dataset was prepared according to some rules, but during training it something goes wrong; perhaps some critical factors were not considered.

The engineer can suggest what can be changed and how. That is, the learning process must be iterative.

4 steps to minimize the risks and negative impact when switching software development vendors in our new article 👇

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- How to evaluate factors before making a decision to switch vendors;
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4 Steps IT Project Transition Plan from One Vendor to Another - JayDevs Does software development project transition scare you? Here you will find step-by-step project transition plan and a IT project transition checklist.

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Enjoy reading: https://jaydevs.com/dedicated-software-development-teams/

Dedicated Software Development Teams: The Four W's - JayDevs All you should know about dedicated software development teams. What? Why? When? Who? Read our article and find out answer on this four W's ✔

3 steps to reduce risks while estimating software project requirements, according to Jaydevs CEO Alex Valadzko 👇

“ 01. First if you envision issues with requirements you should ideally involve a business analyst who will ask the right questions and elaborate all the requirements. At the initial project stage, it also makes sense to ask QA engineers for assistance and create detailed test cases. Oftentimes it helps to reveal bottlenecks and unclear moments.

02. Secondly, you should consider frequent product delivery. Agile methodology being really popular nowadays can help you do this. This approach reduces risks due to the ability of seeing the results at an early stage and costs of changes are less.

In general, the customer should be involved in the process of development, not necessarily full time but good enough to evaluate intermediate results and provide feedback. Client's supervision and understanding if everything meets the expectations is the ultimate goal.

The manager from the production side should in a timely manner observe that the client is informed about any requirements misunderstanding resulting in project estimate and costs change. This may not necessarily affect the final project budget, but will surely help you address changes in a more elegant way.

03. The third important aspect to consider is availability of a professional with expertise in the specific domain, having real experience developing and estimating similar applications. People without relevant experience as a rule, estimate based on guesses and assumptions being the main reason for future problems. “

Find more tips on product development, team management and hiring on our blog - https://jaydevs.com/blog/

How do you reduce risks while estimating a software project? Share your thoughts in the comments.