[ Sharing ]  Demystifying Deep Learning: A Beginner's Guide

Demystifying Deep Learning: A Beginner's Guide

  By : Leadtek AI Expert     57
Deep learning is a branch of artificial intelligence (AI) and machine learning (ML) that utilizes multi-layered artificial neural networks to accurately perform tasks such as object detection, speech recognition, language translation, and more.


What is Deep Learning?

Deep learning is a subset of machine learning distinguished by its ability to automatically learn representations from data, such as images, videos, or text, without the need for human domain knowledge.

The term "deep" in deep learning refers to the multiple layers of algorithms or neural networks used to recognize patterns in data. The highly flexible architecture of deep learning allows it to learn directly from raw data, akin to the way the human brain operates. As more data is acquired, the predictive accuracy of the model improves.

Moreover, deep learning serves as a key technology for achieving high precision and accuracy in tasks like speech recognition, language translation, and object detection. It has achieved breakthroughs in AI applications, including Google DeepMind's AlphaGo, autonomous vehicles, and intelligent voice assistants.



Working Principles of Deep Learning

Deep learning employs artificial neural networks (ANN) with multiple "hidden layers" between input and output.

Artificial neural networks transform input data by applying a nonlinear function to the weighted sum of input values. The transformation, called a neural layer, involves units known as neurons.

The intermediate output of a layer, called features, serves as input for the next layer. The neural network learns multi-layered nonlinear features (such as edges and shapes) through repeated transformations, ultimately synthesizing these features in the last layer to generate predictions for more complex objects.

In a process called gradient descent, errors are sent back through the network via backpropagation, adjusting weights to improve the model. The learning process involves changing the network's weights or parameters to minimize the difference between the neural network's predictions and the expected values. This iterative process, known as training, allows the network to learn optimal features from the data, and these features do not need to be predetermined.



GPU: The Key to Deep Learning

In terms of architecture, CPUs consist of a few cores with large cache memory and can process only a few software threads at a time. In contrast, GPUs consist of hundreds of cores and can concurrently handle thousands of threads.

Advanced deep learning neural networks may have millions or even billions of parameters that need adjustment through backpropagation. Additionally, they require substantial training data for higher accuracy, meaning thousands or even millions of input samples must undergo simultaneous forward and backward propagation.

Due to the inherently high parallelism of neural networks constructed with many identical neurons, this parallelism naturally maps to GPUs. This results in significantly faster computation speeds compared to training with only a CPU, making GPUs the preferred platform for training large complex neural network systems. The parallel nature of inference operations also makes it well-suited for execution on GPUs.



Use Cases of Deep Learning

Deep learning is commonly applied in computer vision, conversational AI, recommendation systems, and more. Computer vision applications use deep learning to extract knowledge from digital images and videos. Conversational AI applications enable computers to comprehend and communicate through natural language. Recommendation systems use images, language, and user interests to provide meaningful and relevant search results and services.

Deep learning is being applied to autonomous vehicles, smart personal assistants, and more intelligent network services. Advanced teams and organizations use deep learning applications such as fraud detection and supply chain modernization.

There are various variants of deep learning algorithms, including:

  • Feedforward artificial neural networks transmit information from one layer to the next without feedback. Multilayer perceptrons (MLP) are a type of feedforward ANN composed of at least three layers: input, hidden, and output. MLPs excel in making predictions for classification using labeled input and are versatile networks applicable to various scenarios.
  • Convolutional Neural Networks (CNN) function as image processors for object recognition. In some cases, CNN image recognition surpasses human capabilities, including identifying cats, signs of cancer in blood, and tumors in MRI scan images. CNNs are integral in areas such as autonomous driving, oil exploration, and fusion energy research. In healthcare, they expedite medical imaging to detect diseases and save lives more rapidly.
  • Recurrent Neural Networks (RNN) are mathematical tools for analyzing language patterns and sequence data.
  • These networks are driving a voice-based computing revolution, enabling natural language processing for hearing and speech in Amazon Alexa, Google Assistant, and Apple Siri. They also provide predictive magic for Google's autocomplete feature in search queries.
  • RNN applications extend beyond natural language processing and speech recognition. They can be used for language translation, stock predictions, and algorithmic trading.
  • For detecting financial fraud, RNNs can mark anomalous spending patterns. RNNs excel at predicting changes in a sequence of data. American Express has deployed deep learning models optimized using NVIDIA® TensorRT™ and running on the NVIDIA Triton™ Inference Server for fraud detection.


NVIDIA Deep Learning for Developers

With GPU-accelerated deep learning frameworks from NVIDIA, researchers and data scientists can significantly accelerate the speed of deep learning training. Tasks that previously took days to complete training can now be done in a matter of hours, and training that once required weeks can be completed in a few days. After preparing models for deployment, developers can rely on GPU-accelerated inference platforms for cloud, embedded devices, or autonomous vehicles to achieve high-performance, low-latency inference for compute-intensive deep neural networks.

GPU-accelerated deep learning frameworks provide flexibility for designing and training custom deep neural networks, with programming interfaces available for commonly used programming languages like Python and C/C++. Widely-used deep learning frameworks like MXNet, PyTorch, TensorFlow, and others depend on NVIDIA GPU acceleration libraries to offer high-performance multi-GPU accelerated training.




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