AI investment has become a must in the business world, and companies from all over the globe are embracing this trend. Nearly 90% of organizations plan to put more money into AI by 2025.
One of the main areas of investment is deep learning. The World Economic Forum approves of this initiative, as the cutting-edge technology can boost productivity, optimize cybersecurity, and enhance decision-making.
Knowing that deep learning is making waves is great, but it doesn’t mean much if you don’t understand the basics. Read on for deep learning applications and the most common examples.
Artificial Neural Networks
Once you scratch the surface of deep learning, you’ll see that it’s underpinned by artificial neural networks. That’s why many people refer to deep learning as deep neural networking and deep neural learning.
There are different types of artificial neural networks.
Perceptrons are the most basic form of neural networks. These artificial neurons were originally used for calculating business intelligence or input data capabilities. Nowadays, it’s a linear algorithm that supervises the learning of binary classifiers.
Convolutional Neural Networks
Convolutional neural network machine learning is another common type of deep learning network. It combines input data with learned features before allowing this architecture to analyze images or other 2D data.
The most significant benefit of convolutional neural networks is that they automate feature extraction. As a result, you don’t have to recognize features on your own when classifying pictures or other visuals – the networks extract them directly from the source.
Recurrent Neural Networks
Recurrent neural networks use time series or sequential information. You can find them in many areas, such as natural language processing, image captioning, and language translation. Google Translate, Siri, and many other applications have adopted this technology.
Generative Adversarial Networks
Generative adversarial networks are architecture with two sub-types. The generator model produces new examples, whereas the discriminated model determines if the examples generated are real or fake.
These networks work like so-called game theory scenarios, where generator networks come face-to-face with their adversaries. They generate examples directly, while the adversary (discriminator) tries to tell the difference between these examples and those obtained from training information.
Deep Learning Applications
Deep learning helps take a multitude of technologies to a whole new level.
The feature that allows computers to obtain useful data from videos and pictures is known as computer vision. An already sophisticated process, deep learning can enhance the technology further.
For instance, you can utilize deep learning to enable machines to understand visuals like humans. They can be trained to automatically filter adult content to make it child-friendly. Likewise, deep learning can enable computers to recognize critical image information, such as logos and food brands.
Natural Language Processing
Artificial intelligence deep learning algorithms spearhead the development and optimization of natural language processing. They automate various processes and platforms, including virtual agents, the analysis of business documents, key phrase indexing, and article summarization.
Human speech differs greatly in language, accent, tone, and other key characteristics. This doesn’t stop deep learning from polishing speech recognition software. For instance, Siri is a deep learning-based virtual assistant that can automatically make and recognize calls. Other deep learning programs can transcribe meeting recordings and translate movies to reach wider audiences.
Robots are invented to simplify certain tasks (i.e., reduce human input). Deep learning models are perfect for this purpose, as they help manufacturers build advanced robots that replicate human activity. These machines receive timely updates to plan their movements and overcome any obstacles on their way. That’s why they’re common in warehouses, healthcare centers, and manufacturing facilities.
Some of the most famous deep learning-enabled robots are those produced by Boston Dynamics. For example, their robot Atlas is highly agile due to its deep learning architecture. It can move seamlessly and perform dynamic interactions that are common in people.
Self-driving cars are all the rage these days. The autonomous driving industry is expected to generate over $300 billion in revenue by 2035, and most of the credits will go to deep learning.
The producers of these vehicles use deep learning to train cars to respond to real-life traffic scenarios and improve safety. They incorporate different technologies that allow cars to calculate the distance to the nearest objects and navigate crowded streets. The vehicles come with ultra-sensitive cameras and sensors, all of which are powered by deep learning.
Passengers aren’t the only group who will benefit from deep learning-supported self-driving cars. The technology is expected to revolutionize emergency and food delivery services as well.
Deep Learning Algorithms
Numerous deep learning algorithms power the above technologies. Here are the four most common examples.
Backpropagation is commonly used in neural network training. It starts from so-called “forward propagation,” analyzing its error rate. It feeds the error backward through various network layers, allowing you to optimize the weights (parameters that transform input data within hidden layers).
Stochastic Gradient Descent
The primary purpose of the stochastic gradient descent algorithm is to locate the parameters that allow other machine learning algorithms to operate at their peak efficiency. It’s generally combined with other algorithms, such as backpropagation, to enhance neural network training.
The reinforcement learning algorithm is trained to resolve multi-layer problems. It experiments with different solutions until it finds the right one. This method draws its decisions from real-life situations.
The reason it’s called reinforcement learning is that it operates on a reward/penalty basis. It aims to maximize rewards to reinforce further training.
Transfer learning boils down to recycling pre-configured models to solve new issues. The algorithm uses previously obtained knowledge to make generalizations when facing another problem.
For instance, many deep learning experts use transfer learning to train the system to recognize images. A classifier can use this algorithm to identify pictures of trucks if it’s already analyzed car photos.
Deep Learning Tools
Deep learning tools are platforms that enable you to develop software that lets machines mimic human activity by processing information carefully before making a decision. You can choose from a wide range of such tools.
Developed in CUDA and C++, TensorFlow is a highly advanced deep learning tool. Google launched this open-source solution to facilitate various deep learning platforms.
Despite being advanced, it can also be used by beginners due to its relatively straightforward interface. It’s perfect for creating cloud, desktop, and mobile machine learning models.
The Keras API is a Python-based tool with several features for solving machine learning issues. It works with TensorFlow, Thenao, and other tools to optimize your deep learning environment and create robust models.
In most cases, prototyping with Keras is fast and scalable. The API is compatible with convolutional and recurrent networks.
PyTorch is another Python-based tool. It’s also a machine learning library and scripting language that allows you to create neural networks through sophisticated algorithms. You can use the tool on virtually any cloud software, and it delivers distributed training to speed up peer-to-peer updates.
Caffe’s framework was launched by Berkeley as an open-source platform. It features an expressive design, which is perfect for propagating cutting-edge applications. Startups, academic institutions, and industries are just some environments where this tool is common.
Python makes yet another appearance in deep learning tools. Here, it powers Theano, enabling the tool to assess complex mathematical tasks. The software can solve issues that require tremendous computing power and vast quantities of information.
Deep Learning Examples
Deep learning is the go-to solution for creating and maintaining the following technologies.
Image recognition programs are systems that can recognize specific items, people, or activities in digital photos. Deep learning is the method that enables this functionality. The most well-known example of the use of deep learning for image recognition is in healthcare settings. Radiologists and other professionals can rely on it to analyze and evaluate large numbers of images faster.
There are several subtypes of natural language processing, including text generation. Underpinned by deep learning, it leverages AI to produce different text forms. Examples include machine translations and automatic summarizations.
As previously mentioned, deep learning is largely responsible for the development of self-driving cars. AutoX might be the most renowned manufacturer of these vehicles.
The Future Lies in Deep Learning
Many up-and-coming technologies will be based on deep learning AI. It’s no surprise, therefore, that nearly 50% of enterprises already use deep learning as the driving force of their products and services. If you want to expand your knowledge about this topic, consider taking a deep learning course. You’ll improve your employment opportunities and further demystify the concept.
Soon, we will be launching four new Degrees for AY24-25 at OPIT – Open Institute of Technology
I want to offer a behind-the-scenes look at the Product Definition process that has shaped these upcoming programs.
🚀 Phase 1: Discovery (Late May – End of July)
Our journey began with intensive brainstorming sessions with OPIT’s Academic Board (Francesco Profumo, Lorenzo Livi, Alexiei Dingli, Andrea Pescino, Rosario Maccarrone) . We also conducted 50+ interviews with tech and digital entrepreneurs (both from startups and established firms), academics and students. Finally, we deep-dived into the “Future of Jobs 2023” report by the World Economic Forum and other valuable research.
🔍 Phase 2: Selection – Crafting Our Roadmap (July – August)
Our focus? Introducing new degrees addressing critical workforce shortages and upskilling/reskilling needs for the next 5-10 years, promising significant societal impact and a broad market reach.
Our decision? To channel our energies on full BScs and MScs, and steer away from shorter courses or corporate-focused offerings. This aligns perfectly with our core mission.
💡 Focus Areas Unveiled!
We’re thrilled to concentrate on pivotal fields like:
- Advanced AI
- Digital Business
- Metaverse & Gaming
- Cloud Computing (less “glamorous”, but market demand is undeniable).
🎓 Phase 3: Definition – Shaping the Degrees (August – November)
With an expert in each of the above fields, and with the strong collaboration of our Academic Director, Prof. Lorenzo Livi , we embarked on a rigorous “drill-down process”. Our goal? To meld modern theoretical knowledge with cutting-edge competencies and skills. This phase included interviewing over 60+ top academics, industry professionals, and students and get valuable, program-specific, insights from our Marketing department.
🌟 Phase 4: Accreditation and Launch – The Final Stretch
We’re currently in the accreditation process, gearing up for the launch. The focus is now shifting towards marketing, working closely with Greta Maiocchi and her Marketing and Admissions team. Together, we’re translating our new academic offering into a compelling value proposition for the market.
Stay tuned for more updates!
Far from being a temporary educational measure that came into its own during the pandemic, online education is providing students from all over the world with new ways to learn. That’s proven by statistics from Oxford Learning College, which point out that over 100 million students are now enrolled in some form of online course.
The demand for these types of courses clearly exists.
In fact, the same organization indicates that educational facilities that introduce online learning see a 42% increase in income – on average – suggesting that the demand is there.
Enter the Open Institute of Technology (OPIT).
Delivering three online courses – a Bachelor’s degree in computer science and two Master’s degrees – with more to come, OPIT is positioning itself as a leader in the online education space. But why is that? After all, many institutions are making the jump to e-learning, so what separates OPIT from the pack?
Here, you’ll discover the answers as you delve into the five reasons why you should trust OPIT for your online education.
Reason 1 – A Practical Approach
OPIT focuses on computer science education – a field in which theory often dominates the educational landscape. The organization’s Rector, Professor Francesco Profumo, makes this clear in a press release from June 2023. He points to a misalignment between what educators are teaching computer science students and what the labor market actually needs from those students as a key problem.
“The starting point is the awareness of the misalignment,” he says when talking about how OPIT structures its online courses. “That so-called mismatch is generated by too much theory and too little practical approach.” In other words, students in many classes spend far too much time learning the “hows” and “whys” behind computerized systems without actually getting their hands dirty with real work that gives them practical experience in using those systems.
OPIT takes a different approach.
It has developed a didactic approach that focuses far more on the practical element than other courses. That approach is delivered through a combination of classroom sessions – such as live lessons and masterclasses – and practical work offered through quizzes and exercises that mimic real-world situations.
An OPIT student doesn’t simply learn how computers work. They put their skills into practice through direct programming and application, equipping them with skills that are extremely attractive to major employers in the tech field and beyond.
Reason 2 – Flexibility Combined With Support
Flexibility in how you study is one of the main benefits of any online course.
You control when you learn and how you do it, creating an environment that’s beneficial to your education rather than being forced into a classroom setting with which you may not feel comfortable. This is hardly new ground. Any online educational platform can claim that it offers “flexibility” simply because it provides courses via the web.
Where OPIT differs is that it combines that flexibility with unparalleled support bolstered by the experiences of teachers employed from all over the world. The founder and director of OPIT, Riccardo Ocleppo, sheds more light on this difference in approach when he says, “We believe that education, even if it takes place physically at a distance, must guarantee closeness on all other aspects.” That closeness starts with the support offered to students throughout their entire study period.
Tutors are accessible to students at all times. Plus, every participant benefits from weekly professor interactions, ensuring they aren’t left feeling stuck on an educational “island” and have to rely solely on themselves for their education. OPIT further counters the potential isolation that comes with online learning with a Student Support team to guide students through any difficulties they may have with their courses.
In this focus on support, OPIT showcases one of its main differences from other online platforms.
You don’t simply receive course material before being told to “get on with it.” You have the flexibility to learn at your own pace while also having a support structure that serves as a foundation for that learning.
Reason 3 – OPIT Can Adapt to Change Quickly
The field of computer science is constantly evolving.
In the 2020s alone, we’ve seen the rise of generative AI – spurred on by the explosive success of services like ChatGPT – and how those new technologies have changed the way that people use computers.
Riccardo Ocleppo has seen the impact that these constant evolutions have had on students. Before founding OPIT, he was an entrepreneur who received first-hand experience of the fact that many traditional educational institutions struggle to adapt to change.
“Traditional educational institutions are very slow to adapt to this wave of new technologies and trends within the educational sector,” he says. He points to computer science as a particular issue, highlighting the example of a board in Italy of which he is a member. That board – packed with some of the country’s most prestigious tech universities – spent three years eventually deciding to add just two modules on new and emerging technologies to their study programs.
That left Ocleppo feeling frustrated.
When he founded OPIT, he did so intending to make it an adaptable institution in which courses were informed by what the industry needs. Every member of its faculty is not only a superb teacher but also somebody with experience working in industry. Speaking of industry, OPIT collaborates with major companies in the tech field to ensure its courses deliver the skills that those organizations expect from new candidates.
This confronts frustration on both sides. For companies, an OPIT graduate is one for which they don’t need to bridge a “skill gap” between what they’ve learned and what the company needs. For you, as a student, it means that you’re developing skills that make you a more desirable prospect once you have your degree.
Reason 4 – OPIT Delivers Tier One Education
Despite their popularity, online courses can still carry a stigma of not being “legitimate” in the face of more traditional degrees. Ocleppo is acutely aware of this fact, which is why he’s quick to point out that OPIT always aims to deliver a Tier One education in the computer science field.
“That means putting together the best professors who create superb learning material, all brought together with a teaching methodology that leverages the advancements made in online teaching,” he says.
OPIT’s degrees are all accredited by the European Union to support this approach, ensuring they carry as much weight as any other European degree. It’s accredited by both the European Qualification Framework (EQF) and the Malta Qualification Framework (MQF), with all of its courses having full legal value throughout Europe.
It’s also here where we see OPIT’s approach to practicality come into play via its course structuring.
Take its Bachelor’s degree in computer science as an example.
Yes, that course starts with a focus on theoretical and foundational knowledge. Building a computer and understanding how the device processes instructions is vital information from a programming perspective. But once those foundations are in place, OPIT delivers on its promises of covering the most current topics in the field.
Machine learning, cloud computing, data science, artificial intelligence, and cybersecurity – all valuable to employers – are taught at the undergraduate level. Students benefit from a broader approach to computer science than most institutions are capable of, rather than bogging them down in theory that serves little practical purpose.
Reason 5 – The Learning Experience
Let’s wrap up by honing in on what it’s actually like for students to learn with OPIT.
After all, as Ocleppo points out, one of the main challenges with online education is that students rarely have defined checkpoints to follow. They can start feeling lost in the process, confronted with a metaphorical ocean of information they need to learn, all in service of one big exam at the end.
Alternatively, some students may feel the temptation to not work through the materials thoroughly, focusing instead on passing a final exam. The result is that those students may pass, but they do so without a full grasp of what they’ve learned – a nightmare for employers who already have skill gaps to handle.
OPIT confronts both challenges by focusing on a continuous learning methodology. Assessments – primarily practical – take place throughout the course, serving as much-needed checkpoints for evaluating progress. When combined with the previously mentioned support that OPIT offers, this approach has led to courses that are created from scratch in service of the student’s actual needs.
Choose OPIT for Your Computer Science Education
At OPIT, the focus lies as much on helping students to achieve their dream careers as it does on teaching them. All courses are built collaboratively. With a dedicated faculty combined with major industry players, such as Google and Microsoft, it delivers materials that bridge the skill gap seen in the computer science field today.
There’s also more to come.
Beyond the three degrees OPIT offers, the institution plans to add more. Game development, data science, and cloud computing, to name a few, will receive dedicated degrees in the coming months, accentuating OPIT’s dedication to adapting to the continuous evolution of the computer science industry. Discover OPIT today – your journey into computing starts with the best online education institution available.