To do deep learning algorithms, it is better to go to the larger Internet to do development and test

Intelligent Q&A and Deep Learning" by Wang Hailiang, CEO of Chatopera, is very useful for friends who learn AI! Highly recommended!

1. Deep learning is a class of machine learning algorithms that use multiple layers to gradually extract higher-level features from the original input. For example, in image processing, the lower layer can recognize the edges, while the higher layer can recognize the parts that are meaningful to humans, such as numbers and letters or faces.

2. There are three types of deep learning algorithms: regression algorithms. Regression algorithms are a class of algorithms that attempt to explore the relationship between variables by measuring errors, and are powerful tools for statistical machine learning. Instance-based algorithms.

3. Deep learning will learn neural networks, BP backpropagation algorithms, TensorFlow deep learning tools, etc.

4. What you need to learn about deep learning includes: neural network, BP backpropagation algorithm, TensorFlow deep learning tools, etc. The full name of deep learning in English is:

Deeplearning is a code-lifting branch of machine learning, which mainly uses artificial neural networks as a framework to represent and learn from data.

5. Only a simple understanding: There are three common deep learning algorithms: convolutional neural network, recurrent neural network, and generative adversarial network.

Summary. Hello, deep learning algorithms rely heavily on high-end machines, as opposed to traditional machine learning algorithms, which can run on low-end machines. This is because the requirements for deep learning algorithms include GPUs, which are an integral part of their work.

Deep learning algorithms essentially do a lot of matrix multiplication operations, and using GPUs can effectively optimize these operations, and that's the purpose of using GPUs.

Hello, deep learning algorithms rely heavily on high-end machines, as opposed to traditional machine learning algorithms, which can run on low-end machines. This is because the requirements for deep learning algorithms include GPUs, which are an integral part of their work. Deep learning algorithms essentially do a lot of matrix multiplication operations, and using GPUs can effectively optimize these operations, and that's the purpose of using GPUs.

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The difference between traditional algorithms and deep learning in specific operations, I don't want to know the difference in hardware.

Explain the difference in an easy-to-understand sentence.

The traditional algorithm implements an established algorithm processing logic, which corresponds to a specific input and an output to a response. Deep learning algorithms, on the other hand, learn from the data to learn the algorithms contained in the data.

1. Data dependency

The main difference between deep learning and traditional machine learning is that its performance grows as the size of the data increases. Deep learning algorithms don't perform well when there is little data. This is because deep learning algorithms require a lot of data to understand it perfectly.

3. Hardware dependency

Deep learning algorithm requires a large number of matrix operations, and GPUs are mainly used to efficiently optimize matrix operations, so GPUs are necessary hardware for deep learning to work properly. Compared to traditional machine learning algorithms, deep learning relies more on high-end machines with GPUs installed.

2. Feature processing

Feature processing is the process of putting domain knowledge into a feature extractor to reduce the complexity of the data and generate patterns that make learning algorithms work better. The feature processing process is time-consuming and requires specialized knowledge.

Deep learning attempts to derive high-level features directly from the data, which is the main difference between deep learning and traditional machine learning algorithms. Based on this, deep learning cuts out the work of designing feature extractors for each problem.

For example, a convolutional neural network tries to learn low-level features in the front layer, then a partial face, and then a description of a high-level face. For more information, read about the interesting applications of neural machines in deep learning.

When traditional machine learning algorithms are applied to solve problems, traditional machine learning usually decomposes the problem into multiple sub-problems, solves them one by one, and finally combines the results of all sub-problems to obtain the most significant final result. Deep learning, on the other hand, advocates straightforward, end-to-end problem-solving.

There are three common algorithms for deep learning: convolutional neural network, recurrent neural network, and generative adversarial network.

Convolutional neural networks (CNNs) are a class of feedforward neural networks with deep structures that include convolutional computation, and are one of the representative algorithms of deep learning.

Recurrent neural network (RNN) is a type of recurrent neural network that uses sequence data as input, recursively in the evolution direction of the sequence, and all nodes (recurrent units) are connected in a chain.

Generative Adversarial Networks (GANs) are deep learning models that have become very popular in the past two years.