Deep Learning is an advanced branch of artificial intelligence that enables machines to “learn” and “think” like humans. From facial recognition systems to self-driving cars and medical image analysis—these are all applications of Deep Learning. But what makes this technology so special? And are Vietnamese businesses ready to harness its full potential?

What is Deep Learning?

Deep Learning is a method within artificial intelligence (AI) where computers learn from data using models known as artificial neural networks. It mimics the way the human brain works—learning from experience, recognizing patterns, and improving over time with each iteration.

How is Deep Learning different from Machine Learning?

• Machine Learning requires human intervention to manually select data features.

• Deep Learning automatically learns features from raw data without needing explicit programming.

Example: In image recognition, Machine Learning requires you to define key features (like eyes, nose, mouth), while Deep Learning can learn to identify these elements on its own.

How Does Deep Learning Work?

Deep Learning is based on an architecture called a deep neural network, which consists of multiple layers that mimic how the human brain processes information.

How Do Layers in a Neural Network Function?

A typical deep learning model consists of three main types of layers:

1. Input Layer

This layer receives raw data such as images, audio, or text.
For example, if the input is a 28×28 pixel image, the input layer will have 784 neurons—each representing a pixel value.

2. Hidden Layers

These are the core of the network where all the “magic” happens. Each hidden layer:

• Receives input from the previous layer

• Applies weights and biases

• Uses an activation function (like ReLU or Sigmoid) to introduce non-linearity

• Passes the result to the next layer

3. Output Layer

This layer produces the final output—whether it’s a classification (e.g., cat or dog), a prediction (e.g., house price), or generating new content (e.g., text).

Each connection between neurons is like a “pathway of information,” and the weight determines the importance of that path. The network learns by adjusting these weights to minimize errors.

The Learning Process: From Data to Intelligence

1. Forward Propagation

Data flows from input to output through each layer. At each step, a linear operation (matrix × vector) is combined with a non-linear activation function.

2. Loss Calculation

The model’s prediction is compared with the actual result.
For example, if the image is a “cat” but the model predicts “dog,” the error is high.

3. Backward Propagation

Using derivatives and the chain rule, the network calculates how much each weight contributed to the error.

4. Weight Update (Learning)

An optimization algorithm (like Gradient Descent) is used to adjust the weights, reducing the overall error over time.

This cycle repeats thousands or even millions of times until the model becomes sufficiently accurate.

Example Analogy

It’s like teaching a child to distinguish between a cat and a dog. At first, the child makes mistakes. But with feedback and repeated examples, the child gradually learns to make correct distinctions.
Neural networks learn in a similar way—through continuous feedback and adjustment.

 

Why Has Deep Learning Grown So Rapidly in Recent Years?

The rapid rise of deep learning can be attributed to three key factors:

1. Big Data – Companies now generate massive amounts of data every day.

2. Advanced Computing Power – GPUs and AI chips drastically accelerate training.

3. Open-Source Frameworks – Tools like TensorFlow and PyTorch make deep learning accessible to all.

1. Big Data

Today, organizations collect data at an unprecedented scale—from surveillance videos, IoT sensors, to customer transactions—resulting in an abundance of rich, raw data.
Unlike traditional algorithms, deep learning automatically extracts hidden patterns from raw inputs without manual feature engineering.
The more data it learns from, the more accurate and intelligent the model becomes.

2. Powerful Computational Resources

GPUs (Graphics Processing Units) are no longer just for gaming. They act as turbochargers for AI by handling thousands of parallel computations efficiently.
This makes it dozens to hundreds of times faster to train deep learning models compared to traditional CPUs, enabling large-scale projects like image recognition or natural language processing (NLP) to become viable for enterprises.

3. Modern Open-Source Frameworks

Frameworks such as TensorFlow, PyTorch, and MXNet are not only free but also optimized for building and training deep neural networks across multiple GPUs or machines.
These tools empower everyone—from startups to research institutions—to experiment, develop, and deploy deep learning solutions without needing to take shortcuts or build from scratch.

Real-World Impact

According to McKinsey, companies that have adopted AI—including deep learning—have improved performance by 20% to 30% in areas like operations, marketing, and decision-making.
For example, AI helps optimize inventory, forecast demand, reduce costs, and boost productivity significantly.
(Source: McKinsey & Company)

Applications of Deep Learning in Vietnam

1. Banking

Facial recognition for authentication & attendance tracking
As of early 2024, the State Bank of Vietnam has mandated biometric verification (facial recognition) for all digital transactions valued at over 10 million VND (~ $390).
(Source: Vietnam Government Portal)

Several domestic banks, including Techcombank and Vietcombank, have developed facial recognition systems designed by Vietnamese engineers to strengthen security and enhance customer experience.

Fraud detection through behavioral and transactional analysis
Organizations like Feedzai are partnering with Vietnamese banks to deploy biometric authentication combined with transaction behavior analysis to detect deepfakes, spoofing attempts, or compromised accounts.
(Source: Feedzai)

Result: Significant reduction in fraud risk, lower monitoring costs, and improved customer trust.

2. Healthcare

Medical image analysis: X-rays and CT scans
VinBigData (a subsidiary of Vingroup) has developed VinDr – a deep learning system capable of diagnosing breast cancer, pneumonia, and bronchitis from X-ray and CT images. It has been piloted at major hospitals including Military Hospital 108, Vinmec, and Hanoi Medical University.
(Source: National Library of Medicine)

AI-assisted diagnosis at scale
VinBrain, through its DrAid platform, has been deployed in 63 healthcare facilities across Vietnam. Powered by NVIDIA DGX A100 GPUs, the platform helps doctors reduce diagnostic time by up to 80%.

AI-powered tuberculosis (TB) detection
A collaboration between the Central Lung Hospital and Vietnamese tech institutes has created a CNN-based model trained on local chest X-rays, achieving over 90% sensitivity and specificity. VinBrain has also integrated TB diagnostics into DrAid.
(Source: AuntMinnieEurope)

3. Logistics & Warehousing

Automated monitoring & congestion forecasting
According to the International Journal of Scientific Research & Management, deep learning has helped Vietnamese logistics firms improve demand forecasting, optimize deliveries, and reduce costs.
(Source: ResearchGate)

By deploying AI-powered cameras in warehouses, companies can automatically detect damaged packages, monitor inventory, and predict congestion in real time to optimize shipping routes.

4. Education

Facial recognition and student behavior analysis
Many schools in Hanoi and Ho Chi Minh City have piloted AI camera-based attendance systems.
Students who are late or absent are automatically recorded in a digital attendance log. IT teams can also monitor behaviors such as copying or lack of interaction to proactively support students in need.

A 2025 study published by MDPI compared credit scoring effectiveness using machine learning versus deep learning in Vietnamese educational institutions – with deep learning showing clearly superior performance.
(Source: mdpi.com)

The Future of Deep Learning in Vietnam

Deep learning is no longer a distant or experimental concept — it’s becoming an essential technology in modern business operations. Across Vietnam, companies are making a strong shift toward automation and data-driven decision-making.

According to TopDev 2024, demand for AI engineers in Vietnam is growing at 45% annually. And it’s not just the tech sector — industries such as banking, manufacturing, and education are all actively seeking AI talent.

VietnamWorks reports that deep learning engineers now earn an average salary of over $2,000 USD per month, highlighting a clear trend of increased AI investment from domestic enterprises.

Three strong indicators that Deep Learning is becoming the standard:

1. Users expect fast, personalized, and seamless digital experiences.

2. Vietnam’s government aims to place the country in the top 4 ASEAN nations in AI by 2030.

3. Companies that fail to adopt AI risk inefficiencies, higher operating costs, and being left behind.

When Deep Learning Becomes the New Standard

Deep learning is shifting from a “competitive edge” to a business necessity across industries — just like websites became mandatory after 2010, or CRM systems became the standard after 2020.

By 2027, AI and deep learning will likely be default infrastructure for any forward-thinking business. Without it, companies may fall behind in speed, efficiency, and innovation.

Now Is the Time to Act

You don’t need a full in-house technical team to adopt deep learning. Many solutions are now available as plug-and-play modules or cloud-based services that can integrate into existing systems.

Even small and medium-sized businesses can start with a phased approach — and by acting early, you gain a valuable lead in your digital transformation journey.

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