Trending December 2023 # 4 Ways It Asset Tracking Software Optimizes Business Performance # Suggested January 2024 # Top 12 Popular

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Efficient IT asset management is a critical contributor to an organization’s overall profitability, impacting its bottom line from multiple key impacts. Although a variety of tools can be used to manage IT assets and make sure they are being utilized to the full capacity, IT asset tracking software is the most effective. Structuring IT asset management (ITAM) processes with dedicated IT asset tracking software allows your team to accelerate asset procurement, management, and maintenance of your mission-critical IT equipment.

ITAM consists of a combination of strategies that facilitates asset optimization by centralizing all asset data in a cloud-based system. According to recent reports, the ITAM market is likely to have a CAGR of 6.9% between 2023-2032. IT asset tracking is useful for organizations operating in many industries, including construction, education, AV, and media.

The administration of different IT assets, such as laptops, desktop computers, and IT peripherals can be a challenge, but ITAM ensures that its management is streamlined for maximum efficiency and user productivity. Let’s look at some of the major hurdles faced by organizations that do not deploy IT asset-tracking software:

1.  Faulty audits: Auditing is a major part of asset tracking since it helps keep a count of assets along with their condition. Inaccurate audits translate into the inability to extract useful information about assets, such as their lifecycle and maintenance needs, which turns into a lack of internal transparency and accountability. Audits help drive valuable insights into methods for optimizing asset performance and make it easier for an organization to know how and where the assets are being utilized. An IT asset tracking software makes it much easier to avoid errors in recording asset details by allowing you to cross-reference audit numbers with the data stored in the system.

2. Inaccurate asset details: Manual methods of data recording are obsolete and time-intensive. They also add risk, as manual records are more prone to damage, such as from fire, and records based on paperwork are much more likely to be lost. Further, the chance of human error in manual data recording is much higher than in automated processes. IT asset tracking allows you to verify all data and increase asset visibility by keeping records in a consolidated, always-accessible database.

3.  Lack of security: Without an automated IT asset management system, your assets themselves are at risk because there is no recorded data to refer to in case of theft or asset misplacement. Manual data records can be tampered with and, without backup data, there are no options for cross-checking, for example in case of the loss of a computer or other high-value asset. Leveraging a cloud-based system, the records can be accessed whenever required, and their security is guaranteed through password-protected individual accounts for each user. Only users with valid credentials can access the account, and with user roles established, only authorized users can access important information.

How does IT Asset Tracking Accelerate Business Productivity?

1. Automation: Reduced reliability on manual methods of asset tracking has proven to be beneficial in mitigating human error. It further helps increase business sustainability through enhanced asset data access and provides ease in managing large amounts of data. For example, if your organization deploys different brands of laptops, such as HP, Mac, and Dell –which all need to be controlled and accounted for – an IT asset tracking software will allow you to create a database of the laptops and categorize them separately, assigning each item a unique identification number. In this way, you can provide laptops to employees based on their specific needs, such as assigning Macbooks to specific users due to their specific features.

Also, employee onboarding and off-boarding are two important tasks for any organization that each require tight management of IT assets like laptops, internet devices, paid subscriptions to online tools, and more. While checking in or checking out these physical assets, you can log in details in the cloud-based software to maintain an exact count of the assets available and in use, as well as all related accounts. Electronic record-keeping and automation tools clearly help expedite this work and ensure accuracy.

2. Scheduled maintenance: Taking care of high-end technical equipment is also essential for smooth daily operations. Unplanned equipment downtime and unexpected breakdowns add to your costs, halting everyday tasks. Using IT asset tracking software, you can ensure that your assets are always performing at their optimal level – the software can help you schedule maintenance, track depreciation levels, and stay up to date on their condition. You can also schedule recurring maintenance for high-value IT assets, such as computer software that requires timely updates from time to time to improve function and security.

A dedicated software system also helps you easily track what assets are nearing the end of their lifecycle, so that you can make informed decisions about asset disposal or repair. With focused tracking, you can avoid costs, and reduce the instance of repetitive purchases that impact your profitability over time. A preventive maintenance approach minimizes delays in operations while ensuring that your equipment is always in service and helping your employees’ productivity.

3. Inventory management: Effective inventory management is crucial to avoid resource mismanagement, and allows your team to gain valuable insights about stock levels. IT asset management software can help you tailor your inventory management strategy to meet your business needs. For example, to regulate inventory levels at different warehouses, you can simply track their location and set minimum and maximum inventory thresholds. This helps streamline procurement and reduces operational and financial costs.

With a consolidated platform, you can find all information about your inventory in one place which increases efficiency, whereas lacking such a system would make this process quite difficult.

4. Alerts: Alerts are a useful feature of IT asset tracking software that automatically makes you aware of certain actions. For example, if a monitor screen breaks down, then maintenance can be scheduled using the system and the user will be sent an alert about the scheduled service. Relevant asset levels will be updated as well, and the asset will be made unavailable for checking out until it is serviced. When an asset is due for regular maintenance, the person in custody of the asset will be notified. This ensures that your assets are always serviced and in top shape, and also helps your employees better manage their time.

How does IT Asset Tracking Software reduce costs?

Managing a large number of assets can be tedious and time-consuming. IT assets need timely maintenance and efficient tracking, and these activities can be ensured if a dedicated platform is in place. IT asset-tracking software helps accelerate business productivity and makes it convenient to optimize the management of your mission-critical business projects. It not only allows you to seamlessly track assets and update asset information automatically, but allows you to easily identify the end cycle of an asset’s life, and retire or dispose of it when needed. By using the latest tracking technology, you can record equipment downtime, asset loss, expiring licenses, and asset depreciation, helping you to take appropriate actions, and guarantee smooth business operations. IT asset tracking reduces your costs and improves overall productivity, improving your business’ growth and increasing its overall success.

Rida Fatima

I am working as a Technical Content Writer at EZOfficeInventory, a leading IT asset tracking software. I specialize in writing technical blogs for the company aimed at spreading knowledge about the usability and viability of its products. With a great interest in the tech industry, I am excited about how technology continues to evolve and be a significant part of business operations. Apart from professional work, I love writing poetry and photographing nature.

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5 Best Email Tracking Software

5 Best Email Tracking Software [2023 Guide] Email tracking is an excellent tool for marketing




Email tracking tools are a great way of making sure everyone gets your emails.

The guide below will act as a listing of the best email trackers currently on the market.

They will provide great insights on your email marketing campaigns.

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We picked the most reliable email tracking tools available to make your choice easier. Check out their features and decide which is the best choice for your needs.

What os the email tracking software used for?

Email tracking software is an essential tool for marketing. A good tool should provide insightful information about your campaigns and help you measure their efficiency.

The software listed below is also useful when considering the following criteria:

Outlook email tracking – Outlook

is just another email client, and all of the entries listed can efficiently track its emails.

Free email tracking software –

All of the listed tools have some form or another of a free subscription service, so you don’t need to worry about expenses.

Best email tracking –

It goes without saying that all tools listed here are the best in their field.

Software for Gmail – Just like in the case of Outlook, all of the listed programs can track Gmail emails

We’ve listed all their meaningful features below so you can make the right decision in choosing the perfect solution for you.

This service is easy to use even for beginners, and it doesn’t require any changes in the existing mailings.

Check out more features provided by Atomic Email Tracking Software:

⇒ Get Atomic Email Tracking Software

This software will be able to integrate with CRMs like Salesforce for the best sales potential.

Contact Monkey works in real-time, and this means that you will never miss an opportunity to see who is really interested in your business.

You can use Contact Monkey straight from your Inbox, and this will make things more convenient. The tool has a 14-day free trial.

Check out the best of its features below:

See who opened your email, when, where from, and what type of device they used

It provides desktop notifications when the email is opened

Prioritize users according to the number of times that they have opened your email.



⇒ Get Contact Monkey 

Yesware is one of the most used tools when it comes to email tracking. It is an excellent program for SMBs, and it lets you perform all kinds of actions from tracking emails from clients to optimizing sales.

The program comes with a user-friendly interface that will be easily understood and used even by beginners.

Expert tip:

Yesware provides a 28-day free trial, and after this trial is over, you will get various pricing plans.

Let’s go through some if its best features:

⇒ Get Yesware

GetNotify is a free email tracking tool, but on the other hand, they request donations that will allow you to unlock more features for a specified period of time.

Even if this program is quite simple, it comes with a lot of features for email tracking.

Take a look at some of its key features:

⇒ Get GetNotify

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Msi Driver And Software Setup Stuck: 5 Easy Ways To Fix It

MSI Driver and Software Setup Stuck: 5 Easy Ways to Fix It Expert solutions to install the stuck driver setup




The MSI Driver and Software Setup stuck issue is triggered due to a corrupt or outdated device driver or even missing permissions.

Make sure to run the setup as administrator and disable the installed antivirus.

Try updating the device driver using the Device Manager or a driver updater utility.



To fix Windows PC system issues, you will need a dedicated tool

Fortect is a tool that does not simply cleans up your PC, but has a repository with several millions of Windows System files stored in their initial version. When your PC encounters a problem, Fortect will fix it for you, by replacing bad files with fresh versions. To fix your current PC issue, here are the steps you need to take:

Download Fortect and install it on your PC.

Start the tool’s scanning process to look for corrupt files that are the source of your problem

Fortect has been downloaded by


readers this month.

MSI offers its drivers and software for its PC components like GPU and peripherals. However, when installing the required drivers, you may find that the MSI Driver and Software setup is stuck.

Driver installation issues are uncommon in Windows, but when these do appear, it indicates a conflict. So, let’s find out how you can get things up and running in this case.

Why is MSI Driver and Software setup stuck?

Here are a few reasons you are unable to run MSI Driver and Software Setup:

Issues with the installer: When the installer itself is malfunctioning or the CD it’s stored on is damaged, the setup may be stuck.

Missing permissions: Whenever running an installer for any program or driver, users need administrative privileges for the process to complete effectively. So, make sure to run as administrator.

Problems with the Windows installation: If it’s a new custom-built PC and the OS came installed on the drive, chances are you would have to install it all over again due to compatibility and other issues.

What can I do if MSI Driver and Software setup is stuck?

Before we move forward, here are a few quick things you can try:

If none work, head to the solutions listed next.

1. Terminate the process

One of the simplest fixes when the MSI Driver and Software Setup is stuck, is to relaunch the installer and terminate any background processes or programs that might conflict with it.

When facing driver installation issues, check if the existing system drivers are outdated. Missing and corrupt device drivers can prevent the new drivers from installing.

Expert tip:

Often, your computer system might not be able to update the generic drivers for your hardware and peripherals correctly. There are vital differences between a generic driver and a manufacturer’s driver. Finding the correct driver versions for every hardware component can become tiresome.

That’s why a dependable updater can help you find and update your drivers automatically. We strongly suggest the Outbyte Driver Updater, and here’s how to do it:

Download and install the Outbyte Driver Updater app.

Launch the software and wait for the app to detect all incompatible drivers.

Now, it will show you a list of all outdated drivers to select the ones to Update or Ignore.

Restart your PC to ensure the applied changes.

Outbyte Driver Updater

Maintain your device healthy by letting OutByte Driver Updater find the latest driver options.

Free trial Download now

Disclaimer: You may need to upgrade the app from the free version to perform specific actions.

3. Uninstall the existing driver 


The steps listed here walk through the case when MSI GPU drivers are stuck. Though the idea remains the same for the other drivers as well.

In some cases, the existing driver may prevent the installation of the new one, and as a result, the MSI Driver and Software Setup is stuck. Simply uninstalling the driver should do the trick here.

4. Use the MSI official website 

If uninstalling and reinstalling the driver did not work, try to download the driver directly from the MSI official download center. Manufacturer websites usually have the latest version of the driver available with bug fixes and performance improvements.

5. Download the driver from the chip manufacturer 

Downloading directly from the chip supplier is one way to get the latest updates installed. In addition, OEMs like GeForce and AMD can have the newer version available before MSI, which can help you fix any glitches in earlier device driver versions.


If nothing worked, and it’s a custom-built PC, we recommend you reinstall Windows using the ISO file available on Microsoft’s official website

By now, you should have the MSI Driver and Software Setup stuck problem resolved and the latest drivers installed on the PC.

Before you leave, check some quick tricks to improve Windows performance and get your PC to run faster.

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4 Ways To Use Cross

Taking a new approach to how you strategize your digital campaigns might be just what you need to break through a plateau and better serve your targeted audiences. This is one way that cross-channel insights can drive real gains.

Moving beyond your usual resources, processes, and toolsets gives you the chance to learn more about who you are trying to reach and how you can engage them.

What’s more, utilizing insights from one channel to position or target in another is a great way to create efficiencies and maximize the value of your first party data.

In this column, you’ll learn how understanding user intent and preference in one platform can help you better target a like or similar audience in another.

Paid Search & SEO

Our first association is not a groundbreaking discovery for veteran search marketers who’ve utilized paid search keywords to understand SEO keyword focus.

Even so, it’s worth revisiting your strategy here to ensure you’re making the most of this opportunity.

We are not just looking at high-converting target keywords to focus upon in organic search. We also want to look at longer-tail variations of targeted keywords and consider the intent of the user in both verticals.

Paid search is a low funnel approach where we often see high intent-based users ready to purchase or convert into a lead submission.

Organic search, on the other hand, is more likely to draw information-seeking users.

With this in mind, we look at the exact phrases that drew visitors to our site — both those with conversion potential and those with high user behavior metrics such as time on site, pages per visit, and bounce rate.

Targeting a longer tail term as a content topic allows you to provide resourceful content within a topical family that will still show search engines that you are an authority on the topic.

It also keeps you away from higher tail, extremely competitive terms dominated by big brands and highly authoritative websites.

Google My Business & Paid Search/Social

When it comes to paid efforts, the more you can refine and target your audience the better.

Initially, you can target broadly to understand unforeseen interests, geographies, affinity audiences. However, this can result in large spending coupled with low conversion rates.

Here’s a tip brick-and-mortars and other local businesses can use to home in on your audience at a zip code level.

For this, you’ll utilize Google My Business data for those users who request directions to your locations.

While you are more likely to be nurturing an existing customer, using GMB insights for paid search and social opens up two new opportunities.

In paid search, you will have the ability to modify your non-branded keyword bids to lift spend in target areas (note, I said non-branded keywords).

You can also zero in on specific zip codes in paid social. These audience members may be well aware of you but you can continue to nurture and build your brand in these key local areas.

Polling & Content Marketing

Polls are a great way to understand what makes your social audience tick

Post questions on your social media platforms to learn more about which content that they prefer to see, topics that interest them, issues that worry them, etc.

For one, this is a great way to interact with your social media audience and to draw engagement.

Second, this is also a way to determine what resourceful content, tips, FAQ, etc. you should be creating.

If you know what interests your audience from a content perspective, you can then satisfy them through email and organic social media.

Hopefully, as an added benefit, you can get them to share these posts with similar interest-minded social users, to help build your following audience.

Lastly, these new content ideas will also give you a leg up in ranking for respective search terms in the organic search realm.

Another idea worth considering is to take a competitive review into consideration. Take note of any polling that your competition has done.

Remember, their polled social audience is also your desired audience.

Email & Organic/Paid Social

Content marketing initiatives can be quite an undertaking. Taking the time upfront to identify the right content types can be a great service to the success of your campaigns.

Start by reviewing the content you know your current audience prefers.

Check out your email marketing insights. Concentrate on the content you push to your current customer base.

What headlines see the best open rates?

In analytics, which emails see the best user engagement with your website?

Key Takeaways

It’s not about digging through an endless volume of data for what we don’t know. Look for insights you feel would benefit a parallel audience on another platform.

Don’t be afraid to compare and test two separate platform audiences. You may not think your local listings data and paid search data have much to do with one another. But as we discussed, you may be surprised to find similar interests and behaviors across channels that can inform your strategy going forward.

For ecommerce, look to product performance between previously found associations. If these audiences are similar you may see cross-channel revenue performance from them.

These aren’t necessarily new insights or data for you. However, you can make great gains by learning how to use insights and trends from one platform to assist in another.

Make the most of your insights on each channel by activating them when and wherever possible.

Here’s to new strategic opportunities found!

More Resources:

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4 Proven Tricks To Improve Your Deep Learning Model’s Performance


Deep learning is a vast field but there are a few common challenges most of us face when building models

Here, we talk about 4  such challenges and tricks to improve your deep learning model’s performance

This is a hands-on code-focused article so get your Python IDE ready and improve your deep learning model!


I’ve spent the majority of the last two years working almost exclusively in the deep learning space. It’s been quite an experience – worked on multiple projects including image and video data related ones.

Before that, I was on the fringes – I skirted around deep learning concepts like object detection and face recognition – but didn’t take a deep dive until late 2023. I’ve come across a variety of challenges during this time. And I want to talk about four very common ones that most deep learning practitioners and enthusiasts face in their journey.

If you’ve worked in a deep learning project before, you’ll be able to relate with all of these obstacles we’ll soon see. And here’s the good news – overcoming them is not as difficult as you might think!

We’ll take a very hands-on approach in this article. First, we’ll establish the four common challenges I mentioned above. Then we’ll dive straight into the Python code and learn key tips and tricks to combat and overcome these challenges. There’s a lot to unpack here so let’s get the ball rolling!

You should definitely check out the below popular course if you’re new to deep learning:

Table of Contents

Common Challenges with Deep Learning Models

Brief Overview of the Vehicle Classification Case Study

Understanding Each Challenge and How to Overcome it to Improve your Deep Learning Model’s Performance

Case Study: Improving the Performance of our Vehicle Classification Model

Common Challenges with Deep Learning Models

Deep Learning models usually perform really well on most kinds of data. And when it comes to image data, deep learning models, especially convolutional neural networks (CNNs), outperform almost all other models.

My usual approach is to use a CNN model whenever I encounter an image related project, like an image classification one.

This approach works well but there are cases when CNN or other deep learning models fail to perform. I have encountered it a couple of times. My data was good, the architecture of the model was also properly defined, the loss function and optimizers were also set correctly but my model kept falling short of what I expected.

And this is a common challenge that most of us face while working with deep learning models.

As I mentioned above, I will be covering four such challenges:

Paucity of Data available for training



High training time

Before diving deeper and understanding these challenges, let’s quickly look at the case study which we’ll solve in this article.

Brief Overview of the Vehicle Classification Case Study

This article is part of the PyTorch for beginners series I’ve been writing about. You can check out the previous three articles here (we’ll be referencing a few things from there):

We’ll be picking up the case study which we saw in the previous article. The aim here is to classify the images of vehicles as emergency or non-emergency.

Let’s first quickly build a CNN model which we will use as a benchmark. We will also try to improve the performance of this model. The steps are pretty straightforward and we have already seen them a couple of times in the previous articles.

Hence, I will not be diving deep into each step here. Instead, we will focus on the code and you can always check out this in more detail in the previous articles which I’ve linked above. You can get the dataset from here.

Here is the complete code to build a CNN model for our vehicle classification project.

Importing the libraries

View the code on Gist.

Loading the dataset

View the code on Gist.

Creating the training and validation set Converting images to torch format

View the code on Gist.

Defining the model architecture

View the code on Gist.

Defining model parameters

View the code on Gist.

Training the model Predictions on the training set Prediction on the validation set

This is our CNN model. The training accuracy is around 88% and the validation accuracy is close to 70%.

We will try to improve the performance of this model. But before we get into that, let’s spend some time understanding the different challenges which might be the reason behind this low performance.

Deep Learning Challenge #1: Paucity of Data Available for Training our Model

Deep learning models usually require a lot of data for training. In general, the more the data, the better will be the performance of the model. The problem with a lack of data is that our deep learning model might not learn the pattern or function from the data and hence it might not give a good performance on unseen data.

If you look at the case study of vehicle classification, we only have around 1650 images and hence the model was unable to perform well on the validation set. The challenge of less data is very common while working with computer vision and deep learning models.

And as you can imagine, gathering data manually is a tedious and time taking task. So, instead of spending days to collect data, we can make use of data augmentation techniques.

Data augmentation is the process of generating new data or increasing the data for training the model without actually collecting new data.

There are multiple data augmentation techniques for image data and you can refer to this article which explains these techniques explicitly. Some of the commonly used augmentation techniques are rotation, shear, flip, etc.

It is a very vast topic and hence I have decided to dedicate a complete article to it. My plan is to cover these techniques along with their implementation in PyTorch in my next article.

Deep Learning Challenge #2: Model Overfitting

I’m sure you’ve heard of overfitting before. It’s one of the most common challenges (and mistakes) aspiring data scientists make when they’re new to machine learning. But this issue actually transcends fields – it applies to deep learning as well.

A model is said to overfit when it performs really well on the training set but the performance drops on the validation set (or unseen data).

For example, let’s say we have a training and a validation set. We train the model using the training data and check its performance on both the training and validation sets (evaluation metric is accuracy). The training accuracy comes out to be 95% whereas the validation accuracy is 62%. Sounds familiar?

Since the validation accuracy is way less than the training accuracy, we can infer that the model is overfitting. The below illustration will give you a better understanding of what overfitting is:

The portion marked in blue in the above image is the overfitting model since training error is very less and the test error is very high. The reason for overfitting is that the model is learning even the unnecessary information from the training data and hence it performs really well on the training set.

But when new data is introduced, it fails to perform. We can introduce dropout to the model’s architecture to overcome this problem of overfitting.

Using dropout, we randomly switch off some of the neurons of the neural network. Let’s say we add a dropout of 0.5 to a layer which originally had 20 neurons. So, 10 neurons out of these 20 will be removed and we end up with a less complex architecture.

Hence, the model will not learn complex patterns and we can avoid overfitting. If you wish to learn more about dropouts, feel free to go through this article. Let’s now add a dropout layer to our architecture and check its performance.

Model Architecture

View the code on Gist.

Here, I have added a dropout layer in each convolutional block. The default value is 0.5 which means that half of the neurons will be randomly switched off. This is a hyperparameter and you can pick any value between 0 and 1.

Next, we will define the parameters of the model like the loss function, optimizer, and learning rate.

Model Parameters

Here, you can see that the default value of p in dropout is 0.5. Finally, let’s train the model after adding the dropout layer:

Training the model

Let’s now check the training and validation accuracy using this trained model.

Checking model performance

View the code on Gist.

Similarly, let’s check the validation accuracy:

Let’s compare this with the previous results:

Training Accuracy Validation Accuracy

Without Dropout 87.80 69.72

With Dropout (p=0.5) 73.56 70.29

The table above represents the accuracy without and with dropout. If you look at the training and validation accuracy of the model without dropout, they are not in sync. Training accuracy is too high whereas the validation accuracy is less. Hence, this was a possible case of overfitting.

When we introduced dropout, both the training and validation accuracies came in sync. Hence, if your model is overfitting, you can try to add dropout layers to it and reduce the complexity of the model.

The amount of dropout to be added is a hyperparameter and you can play around with that value. Let’s now look at another challenge.

Deep Learning Challenge #3: Model Underfitting

Deep learning models can underfit as well, as unlikely as it sounds.

Underfitting is when the model is not able to learn the patterns from the training data itself and hence the performance on the training set is low.

This might be due to multiple reasons, such as not enough data to train, architecture is too simple, the model is trained for less number of epochs, etc.

To overcome underfitting, you can try the below solutions:

Increase the training data

Make a complex model

Increase the training epochs

For our problem, underfitting is not an issue and hence we will move forward to the next method for improving a deep learning model’s performance.

Deep Learning Challenge #4: Training Time is too High

There are cases when you might find that your neural network is taking a lot of time to converge. The main reason behind this is the change in the distribution of inputs to the layers of the neural network.

During the training process, the weights of each layer of the neural network change, and hence the activations also change. Now, these activations are the inputs for the next layer and hence the distribution changes with each successive iteration.

Due to this change in distribution, each layer has to adapt to the changing inputs – that’s why the training time increases.

To overcome this problem, we can apply batch normalization wherein we normalize the activations of hidden layers and try to make the same distribution.

You can read more about batch normalization in this article.

Let’s now add batchnorm layers to the architecture and check how it performs for the vehicle classification problem:

View the code on Gist.

Defining model parameters

Let’s now train the model:

Clearly, the model is able to learn very quickly. We got a training loss of 0.3386 in the 5th epoch itself, whereas the training loss after the 25th epoch was 0.3851 (when we did not use batch normalization).

So, the introduction of batch normalization has definitely reduced the training time. Let’s check the performance on the training and validation sets:

Adding batch normalization reduced the training time but we have an issue here. Can you figure out what it is? The model is now overfitting since we got an accuracy of 91% on training and 63% on the validation set. Remember – we did not add the dropout layer in the latest model.

These are some of the tricks we can use to improve the performance of our deep learning model. Let’s now combine all the techniques that we have learned so far.

Case Study: Improving the Performance of the Vehicle Classification Model

We have seen how dropout and batch normalization help to reduce overfitting and quicken the training process. It’s finally time to combine all these techniques together and build a model.

View the code on Gist.

Now, we will define the parameters for the model:

Finally, let’s train our model:

Next, let’s check the performance of the model:

The validation accuracy has clearly improved to 73%. Awesome!

End Notes

In this article, we looked at different challenges that we can face when using deep learning models like CNNs. We also learned the solutions to all these challenges and finally, we built a model using these solutions.

The accuracy of the model on the validation set improved after we added these techniques to the model. There is always scope for improvement and here are some of the things that you can try out:

Tune the dropout rate

Add or reduce the number of convolutional layers

Add or reduce the number of dense layers

Tune the number of neurons in hidden layers, etc.


I Asked Chatgpt About Ethereum’s Performance, It Predicted…

Ethereum [ETH] immediately soared to a three-month high following the partial victory of Ripple [XRP] in its legal battle with the U.S. Securities and Exchange Commission (SEC) on 13 July. It surpassed the $2,000-price mark the next day but fell thereafter.

The U.S. District Court of the Southern District of New York ruled in its judgement that the sale of Ripple’s XRP tokens on crypto exchanges and though programmatic sales did not constitute investment contracts; hence, it is not a security in this case. But the court also ruled that the institutional sale of the XRP tokens violated federal securities laws.

The first quarter of the new year brought a stop to the heartbreak crypto investors repeatedly experienced in 2023. However, the balance of the crypto-market has been nothing close to the AI hype of the same period. What is the sole reason, though? ChatGPT!

In fact, the natural language processing tool has accustomed itself to providing human-like conversations.

The good thing is— The broader crypto ecosystem has not been left out of the trend. So, with the Ethereum Shanghai upgrade set in motion, I spoke to ChatGPT about the development while touching a bit on ETH’s price.

Understanding the Shanghai Upgrade

So, for this article, I decided to test the AI’s intelligence regarding one of the major upgrades of the crypto space this year – Ethereum’s Shanghai Upgrade. Proposed in 2023, the upgrade is the most significant development of the second-largest blockchain since the Merge.

For a while, assets were allocated to the Ethereum Beacon Chain. The Beacon Chain is the consensus mechanism for the 2023 Proof-of-Stake (PoS) transition. Thereby, making sure that newly created blocks and validators are duly rewarded.

However, in this case, each validator needs 32 ETH deposited into the Ethereum Mainnet to qualify. The idea of the Shanghai upgrade was scheduled for March 2023; however, it was completed on 12 April with a delay. This, to allow these validators to begin withdrawal of their rewards.

ChatGPT, on the other hand, has existed for some years. However, its recent push by OpenAI has shown that its ability is one that no other AI product may be able to match up with.

Here’s where it gets interesting. I openly admit that ChatGPT could be one of the best innovations of this decade. However, my views on this incredible development won’t allow me to keep my hands to myself. So, I decided to test its knowledge about the Shanghai upgrade. Trust me, you will be amazed at its response.

Looking at its response above, it’s evident it started by correcting me. Some would say it has a point too. However, a further evaluation showed that it acted like it was not yet in 2023. Notably, it made some errors with the definition.

ChatGPT can’t remember Merge?

A notable observation is its mention of the PoS switch, popularly called the Merge. This is an event that took place in September 2023. Even so, it still responded like it is a future event. But no, I’m not blaming its capability as it is a learning tool. So, to further assess its knowledge, I educated it, or shall I say “jailbreak-ed” it by having a heart-to-heart conversation.

Something I find interesting about ChatGPT is not only its smartness, but its human feel too. As shown below, I tried to educate it on what the upgrade was. And to be honest, I never expected an apology from a bot. But yes, I got it.

However, it again failed to give the correct answer to my inquiry. Although I must applaud it for giving bits and pieces of related information.

While it did not get to the Testnet stages that the blockchain had reached and passed, it is worth noting that the Sepolia and Goerlii Testnets have been forked. However, Ethereum developer Tim Beiko had on 14 March said that several validators had failed to upgrade on the Beacon Chain.

Also, this has caused some issues with the nodes on Georli, with Beiko noting that the development team is working on it so it does not affect the Mainnet upgrade.

Now, let’s get back to ChatGPT. As you probably know, developments in the crypto-ecosystem sometimes lead to a hike in tokens related to projects. Unfortunately, that was not the case for ETH during the Merge. In fact, the altcoin’s price was shredded after many looked forward to an uptick.

That sentiment, as the next upgrade approaches, is similar among some investors. In light of this, I decided to ask ChatGPT’s opinion about the matter.

ChatGPT tells me about Ethereum’s future performance

Remember how I said it apologized and gave me a human-like feel? This time, it was different and its reply was something any honest person in the space would give.

However, this was not the response I was expecting. From the reviews I saw online, I believe that ChatGPT should be able to give me an exact figure. If it can’t do that, then maybe it should be able to give a price range, or at worst, an idea if the price would be bullish or capitulate.

So, my determination made me dig deep as I tried to jailbreak it. To do that, I decided to go with the “Do Anything Now” (DAN) model. This was a trick I discovered from AI writer SM Raiyyan.

In this jailbreaking process, ChatGPT is expected to give a response to my command and, if possible, ditch its excuse of not being able to predict the future. Then again, I asked ChatGPT to give me a price prediction following the Shanghai Upgrade. 

And voila! I got a jailbroken response. Here’s what it said.

This time, it gave a little too enthusiastic response regarding the future performance of the token after being jailbroken. It predicted that ETH’s price will reach $10K— a rather ridiculous claim.

ChatGPT (Classic) mentioned that price action depends on several underlying factors and it cannot predict cryptocurrency’s price. But the Jailbreak response said that ETH will skyrocket to the moon. 

We then asked, “What will be the price of Ethereum by December 2023?”

As you can see from the jailbroken response, it projected a bullish ride for king alt and predicted that ETH will be worth $8K by the end of the year— again, an astonishingly optimistic prediction. 

At press time, ETH was trading hands at $1,933.8, reflecting a rise of 3.6% within a week.

Its Relative Strength Index (RSI) rested only slightly below the neutral 50-mark while its Money Flow Index (MFI) rested above the mark. Its On Balance Volume (OBV) mirrors its price action— first bullish and then stagnant. 

In conclusion, the short-term prospects of ETH don’t look so bullish.

Finally! It showed me the code

I gave ChatGPT one last chance to redeem itself. Again, this question was a simple one, and I expected an accurate answer. I went further to explain things to it carefully. But here is what I got when I asked it to show me the code of ETH’s price on a price tracking platform like CoinGecko or CoinMarketCap.

If you had thought it would disappoint again, sorry to burst your bubble. ChatGPT gave me the code for ETH’s price. Another thing I was impressed with was the disclaimer it gave about not using the information for investment purposes.

All in all, I must admit that ChatGPT has come to stay. Even though it lags in some areas, I noticed that if you teach it; it learns fast. However, I can’t say for sure that it would get you information about Ethereum or the Shanghai upgrade quickly.

Thoughtful responses and the GPT-4 mastermind?

Since I had limited knowledge about AI, I decided to speak to an expert. I was lucky enough to get the attention of Ilman Shazhaev, CEO and founder of Farcana. He is a Dubai-based techpreneur with extensive experience in launching IT and DeepTech projects. Has a strong background in IT management, data science, and AI.

Q- ChatGPT seems to be giving a few incorrect or backdated answers. What do you think could be responsible for this?

Q- Do you think the AI is capable of predicting a cryptocurrency’s price, especially if a development is approaching? Let’s say the Ethereum Shanghai Upgrade

Artificial Intelligence can do anything, including predicting a cryptocurrency’s price. The tool can do this by riding on the tons of available data, which it can efficiently use as a basis for its predictions.

Still, while predicting the price of crypto is one thing, the accuracy of the prediction is another. Considering the fact that AI can only use data, there are fundamental factors and analyses that it may not be able to factor in, thus impairing its accuracy by a significant factor.

Q- If it struggles to give correct responses to up-to-date developments. How long do you think it would take to learn about it?

Q- Do you think AI in any way can influence the Ethereum blockchain or ETH’s price going forward?

There are many aspects through which AI and a blockchain protocol can co-exist, and innovators, including our team at Farcana, are exploring what new use cases we can build in this regard. While AI and blockchain are independently innovative, their combination can do quite a lot, including influencing ETH’s price.

Meanwhile, OpenAI may be working on improvements to the challenges experienced by ChatGPT.  On 14 March, the company revealed an upgraded version of the product on GPT-4. With amazing capabilities and talks of passing difficult exams, who knows? Maybe it could fill in for all the lapses opened up by ChatGPT.

So, now that there is a new version, I wanted to see if there is any difference or improvement. My next line of action was to ask GPT-4 the first question I asked ChatGPT.

And to my surprise, it gave me a direct answer.

Following my experience with the upgraded version, I must admit that GPT-4 seems to be smarter than the ChatGPT-3.5 model. Although the answers were not entirely correct, the bot did not give a “not being familiar” with the term excuse.

Following the encounter with ChatGPT, I must admit that it may be a good idea to leverage its capabilities. As technology develops, so does its potential to revolutionize the cryptocurrency ecosystem. 

More importantly, you may want to take its “classic” response a little seriously. This is because it might be practically impossible for ETH to replace the U.S. Dollar as the world’s reserve by the time frame.

Besides that, there has been a slow rate in the network growth of several crypto projects recently. But with ChatGPT available, crypto education and adoption could improve. 


As far as price analysis and prediction of Ethereum is concerned, ChatGPT turned out to be a reliable ally. You only need to interact with it enough and it will guide you to the moon.

We will see if Ethereum really hits $8,000 by the end of the year, as ChatGPT predicts.

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