What is deep learning
Deep Learning is a subset of machine learning, which is a branch of artificial intelligence that enables computers to learn from data and perform tasks that typically require human intelligence. Deep learning uses artificial neural networks, an algorithm inspired by the structure and function of the human brain, which can learn and make predictions or classifications from large amounts of data.
The working principle of deep learning
Deep learning works by creating multiple layers of neurons in a neural network, where each layer can perform some calculations on input data and pass it on to the next layer.
The first layer is called the input layer, which receives raw data such as images, text, or sound.
The middle layer is called a hidden layer, which can extract features or patterns from the data and transform them into higher-level representations.
The last layer is called the output layer, which produces the final result, such as labels or scores.
The connections between neurons are related to weights, which determine the degree of influence each neuron has on another neuron. The weights are initially random and adjusted during training using a process called backpropagation, which involves comparing the network’s output with the desired output (basic facts) and calculating the error metric (loss function). The error is then propagated back through the network and used to update weights based on rules (optimization algorithms).
The training process of deep learning requires a large amount of labeled data, which means that each input example has an associated output value. For example, if we want to train a neural network to recognize handwritten digits, we need thousands of digital images with corresponding labels (0-9). The internet learns by searching for patterns and correlations in data, which help minimize errors and improve accuracy.
The Differences between Deep Learning and Machine Learning
Although deep learning is a subset of machine learning, there are some differences in data requirements, computational power, feature extraction, and performance.
Data requirements: Machine learning algorithms typically use structured data, which means that each input example has a fixed number of predefined features that are organized into tables. For example, if we want to classify flowers based on their characteristics, we need to measure features such as petal length, petal width, sepal length, and sepal width. Deep learning algorithms can process unstructured data without predefined features, such as images, text, or sound, and can vary in size and format. Deep learning algorithms can automatically extract features from raw data and learn hierarchical representations.
Computational power: Machine learning algorithms can run on standard CPUs without requiring too much memory or storage space. Deep learning algorithms require high-performance GPUs or specialized hardware to handle large amounts of data and complex calculations, as well as more memory and storage space to store the results and parameters in the process.
Feature extraction: Machine learning algorithms rely on technicians to define and select relevant features for each problem domain, which is both time-consuming and subjective, and may not capture all aspects of the data. Deep learning algorithms, on the other hand, automatically extract features from raw data using multi-layer neurons, eliminating some manual work, reducing human intervention and bias, and allowing for more generalization and adaptation.
Performance: Machine learning algorithms can achieve good results on many problems, but they may be difficult to handle complex tasks involving high-dimensional inputs, nonlinear relationships, or noisy data. Deep learning algorithms can achieve state-of-the-art results for many challenging problems, such as computer vision, natural language processing, speech recognition, machine translation, etc., sometimes surpassing human level performance. They can also handle noisy data better than machine learning algorithms because they can learn robust representations from a large amount of data.
Popular deep learning development frameworks
Deep learning frameworks are software libraries or tools that help data scientists and developers build and deploy deep learning models more easily and efficiently. They can abstract low-level details of underlying algorithms and hardware, and provide advanced APIs and functionalities for creating, training, testing, and deploying various types of neural networks. Some of the most popular deep learning frameworks today are:
TensorFlow: an open-source platform developed by Google that supports multiple languages such as Python, C++, Java, Go, and can run on CPUs, GPUs, TPUs, and mobile devices. It provides a flexible and scalable architecture for distributed processing and production environments.
PyTorch: An open-source framework developed by Facebook, based on Torch, a scientific computing library for Lua. It supports Python as the primary language and can run on both CPU and GPU. It provides a dynamic computational graph that has greater flexibility and interactivity than TensorFlow’s static graph.
Keras: Advanced API that can run on TensorFlow, Theano, or CNTK. It supports Python as the primary language and can run on both CPU and GPU. It provides a simple and user-friendly interface for building common types of neural networks, such as Convolutional Neural Networks (CNN) or Recursive Neural Networks (RNN).
SciKit Learn: A popular Python machine learning library that also supports deep learning features such as neural network models, feature extraction, dimensionality reduction, etc., and can only run on the CPU.
Apache MXNet: An open-source framework that supports multiple languages such as Python, R, Scala, Julia, and more. It can run on CPUs and GPUs across multiple devices. It provides a declarative programming style that allows for easy parallelization and optimization.
Other deep learning frameworks include Caffe (computer vision application framework), Theano (Python symbolic mathematics library), Deeplearning4j (Java framework), MATLAB (numerical computing environment), Sonnet (library based on TensorFlow) and PaddlePaddle PaddlePaddle launched by Baidu.
Application scenarios of deep learning
In various tasks such as image recognition, natural language processing, and speech recognition, deep learning can achieve high accuracy, sometimes even surpassing human performance. The following are some examples of how deep learning can change various industries and fields with its ability to learn and perform complex tasks from data:
Computer vision: Deep learning can be used to automatically detect objects, faces, scenes, and activities in images and videos. For example, deep learning provides power for autonomous vehicles that can recognize traffic signs, pedestrians, and other vehicles.
Natural language processing: Deep learning can be used to analyze text and speech data, for tasks such as sentiment analysis, machine translation, text summarization, question answering, and chatbots.
Medical health: Deep learning can be used for diagnosing diseases, discovering new drugs, analyzing medical images, and personalized treatment. For example, deep learning can help detect cancer cells from microscopic images.
Finance: Deep learning can be used to predict market trends, detect fraud, optimize investment portfolios, and provide customer service. For example, deep learning can help analyze credit card transactions and mark suspicious activities.
Agriculture: Deep learning can be used to monitor crops, optimize yield, and detect pests and diseases. For example, deep learning can help identify weeds from aerial images.
Network security: Deep learning can be used to detect malicious software attacks. For example, deep learning can help identify malicious files or network intrusions.
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