AI Fundamentals

Deep Learning

Deep learning is a subset of machine learning that uses neural networks with many layers (hence "deep") to recognize complex patterns in large datasets. It powers the most significant AI breakthroughs of the last decade — from speech recognition and image classification to Large Language Models.

Why does this matter?

Deep learning enables AI applications that were unthinkable a decade ago: automated quality control through image recognition, predictive maintenance in manufacturing, natural language processing in customer service. For mid-sized businesses, deep learning becomes accessible through pre-trained models — without a dedicated data science team.

How IJONIS uses this

We use deep learning models from Hugging Face, PyTorch, and TensorFlow for specialized tasks like document classification, anomaly detection, and language processing. Instead of training models from scratch, we adapt pre-trained architectures to your specific use case — faster and more cost-efficient.

Frequently Asked Questions

What distinguishes deep learning from machine learning?

Machine learning is the umbrella term, deep learning is a specific method within it. Classical ML uses handcrafted features; deep learning automatically learns relevant features from raw data. This makes deep learning more powerful with complex data like images, text, and audio.

Does deep learning always require GPUs?

For training large models, yes — but not for deployment (inference). Many pre-trained deep learning models run efficiently on CPUs or even edge devices. For businesses, this means: deployment costs are often significantly lower than training costs.

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