Deep Learning

Deep learning is an important and fascinating subfield of artificial intelligence (AI) that is becoming increasingly significant. This technology enables various industries to evolve: in healthcare, it allows for more accurate diagnoses, while in the financial sector, it improves efficiency. This guide explains in simple terms what deep learning is. In addition to the definition of deep learning, you will learn how the method works, which use cases exist, and how machine learning and deep learning compare.

• Deep learning is a subfield of artificial intelligence (AI) that uses neural networks to learn from large datasets and identify complex patterns

• Artificial neural networks with multiple layers process data step by step to extract increasingly complex features and make decisions

• Deep learning is highly popular due to its accuracy and efficiency in areas such as image recognition and speech processing, as well as in industries like healthcare and financial technology (FinTech)

• Advancements in hardware and algorithms will continue to enhance deep learning capabilities, ensuring it plays a central role in the future development of AI

Definition: What is deep learning in simple terms?

As a subfield of machine learning and artificial intelligence (AI), deep learning involves training machines to learn from large datasets. Unlike traditional algorithms, deep learning uses artificial neural networks that function similarly to the human brain. These networks consist of multiple layers that process and analyse data step by step. The term “deep” in deep learning refers to these layers, with “deep” indicating the many levels involved.

How does deep learning work?

Deep learning enables machines to acquire new skills and improve without human intervention. It works by using artificial neural networks composed of several layers to process data gradually. From the available datasets, deep learning extracts new models and recognises complex patterns, linking them to other data and placing them in the appropriate context.

Deep learning and artificial neural networks

Artificial neural networks are at the core of deep learning. Each layer in a neural network plays a specific role in processing input data.

The first layer, the input layer, captures raw data, which is then passed through several subsequent layers. Each layer identifies increasingly complex features, from simple lines to intricate object recognition. The learning process occurs by adjusting the connections between neurons, similar to how the human brain functions, using an algorithm called backpropagation. This algorithm reduces errors between predicted and actual results by adjusting connection weights. The output layer ultimately delivers the result, such as detecting an object in an image.

There are various types of neural networks, including convolutional neural networks (CNNs) for image data and recurrent neural networks (RNNs) for sequential data. The effectiveness of these networks relies on large datasets and powerful hardware, such as graphics processing units (GPUs).

Why is deep learning so popular?

Deep learning has gained immense popularity in recent years, and with good reason. This AI method offers numerous benefits that make it indispensable for many applications:

• High accuracy
  Deep learning models can achieve very high accuracy, especially in areas like image recognition, speech processing, and natural language processing. Their ability to detect complex patterns and relationships in large datasets often surpasses traditional machine learning methods.

• Automatic feature extraction
  Another reason for deep learning’s popularity is its automatic feature extraction. Unlike traditional algorithms that require manual feature engineering, deep learning models derive relevant features directly from the data. This saves time and increases efficiency.

• Scalability
  Deep learning is highly scalable and can handle vast amounts of data. With the availability of powerful hardware like GPUs and tensor processing units (TPUs), these models can be trained faster and more efficiently, making practical application easier.

• Versatile applications
  Deep learning’s versatility also contributes to its widespread use. It is applied in various sectors, including healthcare, financial technology (FinTech), autonomous driving, customer service, and marketing. Its ability to make accurate predictions and support decision-making improves processes and outcomes across many industries.

• Continuous improvement
  Deep learning systems can continuously improve. By training with more data and refining network structures, these models can consistently enhance their performance, leading to better results and greater acceptance in real-world applications.

Combining high accuracy, efficiency, and versatility, deep learning has become a preferred method in the field of artificial intelligence.

Deep learning vs machine learning – the differences

Deep learning is a subfield of machine learning that uses multilayered artificial neural networks to identify complex patterns in data. In contrast, machine learning encompasses broader techniques that often require manual feature extraction. Deep learning enables automatic feature engineering and achieves higher accuracy when working with large datasets.

Use cases of deep learning

Deep learning has significantly improved many areas of application. With its ability to recognise complex patterns in large datasets, the method is now used across various industries. From finance and healthcare to autonomous vehicles, deep learning, as a subfield of artificial intelligence (AI), offers a wide range of applications.

Law enforcement

Deep learning supports law enforcement agencies in monitoring and analysing video footage, facial recognition, and predicting crime patterns. This enhances the efficiency and accuracy of crime investigation and prevention.

Customer service

Deep learning models are also used in customer service to power chatbots that understand natural language and respond to customer enquiries. This allows teams to handle requests quickly and efficiently, improving customer satisfaction.

Healthcare

In healthcare, deep learning is used to diagnose diseases, analyse medical images, and predict patient outcomes. This technology helps doctors make more accurate diagnoses and develop personalised treatment plans.

Marketing

Deep learning can assist in marketing by analysing customer data and personalising advertising campaigns. By identifying patterns in customer behaviour, companies can develop targeted marketing strategies and increase customer loyalty.

Sales

In sales, deep learning optimises pricing strategies, inventory management, and customer recommendations. Accurate analysis of sales data and trends enables businesses to adjust their sales strategies, potentially boosting revenue.

Human resources

Deep learning is frequently used in human resources to analyse applications, predict employee performance, and improve hiring decisions. This helps companies identify top talent and develop effective HR strategies.

Autonomous driving

In autonomous vehicles, deep learning improves environmental awareness, allowing cars to make safer decisions. Self-driving vehicles can recognise objects more quickly, read traffic signs, and respond to changing traffic conditions.

Financial sector (FinTech)

In the financial sector, deep learning is used for fraud detection, risk analysis, and algorithmic trading. Its ability to identify patterns in financial data helps detect suspicious transactions early and make informed investment decisions.

What are the hardware requirements for deep learning?

An AI and deep learning workstation requires powerful GPUs, large memory capacities, and high computing power to efficiently handle intensive calculations and large datasets.

These hardware components for deep learning must be carefully selected and coordinated to achieve the best results:

• GPUs (graphics processing units): Essential for parallel processing of large datasets, crucial for tasks like segmentation

• Memory (RAM): Large memory capacities, with at least 16 GB of RAM, are necessary to process extensive data volumes

• Storage: SSDs (solid-state drives) provide fast read and write speeds for efficient data storage and segmentation

• CPUs (central processing units): High-performance CPUs support data preparation and system management

• Network connections: Fast network connections are required for distributed training and quick data transfer

• Cooling and power supply: Effective cooling systems and stable power supplies prevent overheating and performance loss

Frameworks for deep learning

Frameworks are essential tools and libraries in AI and deep learning that enable the development and implementation of complex models. They simplify working with neural networks and significantly speed up the development process.

• TensorFlow:
  Developed by Google, TensorFlow is one of the most well-known frameworks, ideal for large, complex models and distributed training

• PyTorch:
  Created by Facebook, PyTorch offers a dynamic architecture that simplifies model development and is especially popular among researchers

• Keras:
  Built on frameworks like TensorFlow, Keras is known for its user-friendliness, enabling quick model development and serving as a great entry point for beginners in deep learning

• Caffe:
  Developed by BVLC, Caffe focuses on speed and modularity, making it particularly suitable for image-based applications and convolutional neural networks (CNNs)

• MXNet:
  Developed by Apache, MXNet is flexible and scalable, supports multiple programming languages, and is suitable for production-ready applications

• Microsoft Cognitive Toolkit (CNTK):
  CNTK provides a powerful platform for training deep learning models, known for high scalability and efficiency

Conclusion: Deep learning remains important for the future

Deep learning is a key area of machine learning. The word “deep” describes the method’s ability to analyse and process large datasets through multiple layers in depth. This enables the recognition of patterns and relationships that would be difficult for humans to detect.

Deep learning will continue to play a crucial role in the development of artificial intelligence. Its capacity for complex pattern recognition makes it indispensable for many applications. For example, autonomous vehicles could make more accurate decisions in the future by analysing traffic signs in real time and quickly detecting pedestrians and other vehicles, potentially reducing accidents. Additionally, advancements in hardware technology and the development of new frameworks and algorithms will further enhance deep learning capabilities. It remains a long-term, essential component of AI, driving innovation across various industries.