What is Deep Learning?

Deep Learning is machine learning a subset of , 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, algorithms inspired by the structure and function of the human brain, to learn from large amounts of data and make predictions or classifications.

How deep learning works

Deep learning works by creating multiple layers of neurons in a neural network, where each layer can perform some calculations on the input data and pass it on to the next layer.

1. The first layer is called the input layer, which receives raw data such as images, text, or sounds.
2. The layers in between, called hidden layers, can extract features or patterns from the data and convert them into higher-level representations.
3. The last layer is called the output layer, which produces the final result, such as a label or score.

The connections between neurons are related to weights, which determine how much influence each neuron has on another neuron. The weights are initially randomized and adjusted during training using a process called backpropagation, which involves comparing the network’s output to the desired output (the ground truth) and calculating an error measure (the loss function), and then The error is propagated backward through the network and used to update the weights according to the rules (optimization algorithm).

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 wanted to train a neural network to recognize handwritten digits, we would need thousands of images of digits with corresponding labels (0-9). Networks learn by finding patterns and correlations in data that help minimize errors and increase accuracy.

The difference between deep learning and machine learning

Although deep learning is a subset of machine learning, they have some differences in data requirements, computing power, feature extraction, and performance.

1. Data requirements: Machine learning algorithms typically work with structured data, meaning each input example has a fixed number of features that are predefined and organized into tables. For example, if we want to classify flowers based on their characteristics, we need to measure characteristics such as petal length, petal width, sepal length, sepal width, etc. While deep learning algorithms can process unstructured data that has no predefined features, such as images, text, or sounds, and can vary in size and format, deep learning algorithms can automatically extract features from raw data and learn hierarchical representations.
2. Computing power: Machine learning algorithms can run on standard CPUs and do not require much memory or storage space. Deep learning algorithms require high-performance GPUs or specialized hardware to process large amounts of data and complex calculations, as well as more memory and storage space to store the results and parameters in the process.
3. Feature extraction: Machine learning algorithms rely on technical staff to define and select relevant features for each problem domain, a process that is time-consuming and subjective and may not capture all aspects of the data. Deep learning algorithms eliminate some of the manual work by automatically extracting features from raw data using multiple layers of neurons, which reduces human intervention and bias and allows for more generalization and adaptation.
4. Performance: Machine learning algorithms can achieve great results on many problems, but they can struggle with 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 large amounts of data.

Popular deep learning development framework

A deep learning framework is a software library or tool that helps data scientists and developers build and deploy deep learning models more easily and efficiently. It abstracts away the low-level details of the underlying algorithms and hardware and provides tools for creating, training, testing, and deploying them. High-level APIs and functions for 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 Python, C++, Java, Go and other languages, and can run on CPU, GPU, TPU and mobile devices. It provides a flexible and scalable architecture for distributed processing and production environments.
• PyTorch : An open source framework developed by Facebook that is 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 calculation graph that is more flexible and interactive than TensorFlow’s static graph.
• Keras : A high-level API that can run on top of 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 recurrent neural networks (RNN).
• SciKit-Learn : A popular Python machine learning library that also supports some deep learning functions, such as neural network models, feature extraction, dimensionality reduction, etc., can only run on the CPU.
• Apache MXNet : An open source framework that supports multiple languages ​​such as Python, R, Scala, and Julia. It runs on both CPU and GPU across multiple devices. It provides a declarative programming style that allows for easy parallelization and optimization.

Other deep learning frameworks include Caffe (a computer vision application framework), Theano (a Python symbolic mathematics library), Deeplearning4j (a Java framework), MATLAB (a numerical computing environment), Sonnet (a library built on TensorFlow), and Baidu’s PaddlePaddle .

Application scenarios of deep learning

In various tasks such as image recognition, natural language processing, speech recognition, etc., deep learning can achieve very high accuracy, sometimes even exceeding human performance. Here is how deep learning relies on its ability to learn from data and perform complex tasks. Some examples of changes across various industries and fields:

• Computer Vision: Deep learning can be used to automatically detect objects, faces, scenes, and activities in images and videos. For example, deep learning powers self-driving cars 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.
• Healthcare: Deep learning can be used to diagnose disease, discover new drugs, analyze medical images, and personalize treatments. 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 flag suspicious activity.
• Agriculture: Deep learning can be used to monitor crops, optimize yields, and detect pests and diseases. For example, deep learning can help identify weeds from aerial images.
• Cybersecurity: Deep learning can be used to detect malware attacks. For example, deep learning can help identify malicious files or network intrusions.