Deep learning

Introduction to Deep Learning

Deep learning is a branch of machine learning with a major aspect of artificial intelligence and uses neural networks found in the human brain. Artificial intelligence is used to allow actions and enhance decision-making. Machine learning is powered by algorithms and enhances the learning experience. Deep learning is an advanced subset of machine learning (Oppermann, 2023). In this technique neural networks are involved, while it might sound futuristic, the model is a key driver for modern AI technologies. Unlike traditional machine learning that depends on structured data and manual feature extraction, the model automatically identifies the features and patterns within layers and predicts the outcomes. Moreover, the technique learns from unstructured data and raw data.

Benefits of Deep Learning

No need for manual feature engineering

In addition to machine learning techniques, some experts pick out the important data features manually and give the insights. The technique helps on its own automatically and finds the relationships in the data to enhance learning, and puts a lot of effort into speeding up the process.

Makes the most of unstructured data

A large portion of company data, such as emails, social media content, audio, and images, is unstructured. The traditional learning algorithms involve humans. The techniques handle the formats and analyze the reports by predicting future outcomes.

Reduces the cost of errors

Mistakes in the products, especially in pharmaceuticals or manufacturing industries, are costly to maintain. The techniques help in identifying the defects that might be missed by humans. The technique adapts to various changes and makes inspections a partner and reliable.

less dependence on labeled data

The labelling data, such as tagging of various images manually is an expensive and time-consuming process (Najafabadi, et al., 2015). The models help learn the data without labels. Unlike traditional learning methods, deep learning figures out the patterns with minimum supervision and saves money and time.

Benefits of deep learning

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Deep Learning Applications

Virtual assistants

The virtual assistants such as Google, Siri, Alexa, and Cortana that use learning techniques to understand and complete tasks. Each of them learns from experience and enhances them better answers.

Chatbots

The chatbots help gather the information through text or voice, answering the question with the help of deep learning algorithms. They are also used in customer support facilities and help them to understand human life conversations.

Healthcare system

Generally, most healthcare systems use deep learning techniques to detect diseases and analyze medical images. Also, the technique helps in diagnosing conditions such as eye diseases from scans and identifying cancer symptoms.

Entertainment

Few platforms use deep learning techniques, such as Spotify, Netflix, and YouTube, to show recommendations. The music-based searches that use learning techniques to identify music. Also, the technique adds features like generation of subtitles.

Robotics

Robotics uses learning techniques to perform human tasks, adjust the path, and avoid obstacles (Biswal, 2025). Generally, they are used in various factories, warehouses, and hospitals to enhance the processes.

Visual recognition

The techniques are useful to recognize and organize images based on photos and events. The technique is mainly helpful in maintaining the attached images and understanding the image pattern.

Fraud detection

Deep learning is also used to detect fraud, especially in payment applications and banks. For example, PayPal uses artificial intelligence to identify patterns and user behavior.

Challenges of deep learning

Data dependency

The models specifically requires a lot of labeled data for training. In this case, gathering much data is time-consuming and expensive. Thus, it becomes impossible, especially in the availability of data.

Computationally intensive

The implementation of deep learning models is demanding and needs a lot of computational resources, such as GPUs and TPUs. The models are particularly helpful with multiple layers that are complicated and require a lot of memory consumption and computational power.

Vulnerability to attacks

Some malicious actors are vulnerable to attacks. Inaccurate misclassifications might come from the attacks (PULIGADDA & CHAPALA, 2021). So, implement good defense mechanisms to fight against the threats.

Lack of interpretability

Basically, the deep learning models are hard to understand. The technique becomes difficult to learn neural networks that include both linkages and changes. The lack of interpretability becomes problematic in the field of healthcare systems.

Resource-demanding technology

A lot of demand involves for the resources. More usage of GPUs requires high volumes of storage and data. Furthermore, when it is compared to machine learning techniques, it requires advanced training and enhances unstructured analytics.

Challenges of Big Data

Cyber security

The major concern of big data is to keep the data secure. As the businesses hold major financial losses and consequently lead to damage to the reputation of the company. Therefore, implement cybersecurity practices and track the results. The use of AI security tools helps to monitor unusual activity and automatically identify threats.

Data quality

Having a large amount of data is a major opportunity, but only if the data is reliable. Big data processes missing pieces, errors, and entries are made duplicates. Consequently leads to complex decisions in the business.

Data storage

Generally, storing a large amount of data is expensive. As the amount of data changes can’t update the systems rapidly. By switching to cloud storage, it scales up the applications and makes it easier to manage the data.

Challenges of big data

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Conclusion

Deep learning is revolutionized as a powerful tool for the maintenance of complex data sets. The technique has a major ability to automate the extraction feature and promote accurate results for various industries such as robotics, finance, and healthcare. However, it is a greater solution for many industries. Certain challenges for big data, such as storage limitations, data security, and quality issues. Businesses need to understand and manage the huge extraction of data sets. To overcome the challenges, organizations put effort into investing in current infrastructure that is suitable for the organization. In conclusion, both deep learning and big data are a powerful combination towards success and enhance opportunities.

References

Biswal, A. (2025, Apr 13). Top 25 Deep Learning Applications Used Across Industries. Retrieved from Simplilearn: https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-applications

Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 02(01), 1-21. Retrieved from https://www.researchgate.net/publication/273063318_Deep_learning_applications_and_challenges_in_big_data_analytics

Oppermann, A. (2023, Dec 12). What Is Deep Learning and How Does It Work? Retrieved from Builtin: https://builtin.com/machine-learning/deep-learning

PULIGADDA, M., & CHAPALA, H. M. (2021). DEEP LEARNING CHALLENGES IN BIG DATA ANALYTICS. Dogo Rangsang Research Journal, 08(14), 588-594. Retrieved from https://www.journal-dogorangsang.in/no_2_Book_21/76.pdf

Keywords

Neural networks, Unstructured data, Virtual Assistants, Fraud detection, Feature extraction

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