Cloud-aware cloud-aware machine learning solutions like Amazon and Micorosoft promise to provide developers with an easy way to integrate machine learning into their applications, which is the latest headline news. . Commitment seems to be good, but developers need to be cautious.
For developers, cloud-based machine learning tools bring the possibility of using machine learning to create and deliver new features. However, when we use it inappropriately, these tools will output poor results, and users may feel uncomfortable. Those who have tested Microsoft's age-detection machine learning tools will find that the ease of use with plug-and-play comes with major accuracy issues—it shouldn't be trustworthy for critical applications or major decisions.
Developers who want to successfully integrate into machine learning in their applications need to be aware of the following key points:
1. The more data the algorithm uses, the more accurate it will be, so try to avoid sampling if possible. Machine learning theory has a very intuitive description of the prediction error. In short, the difference in prediction error between the machine learning model and the optimal prediction (theoretically the best possible error) can be broken down into three parts:
Since the error of the model without the correct functional form is found, if the training set is limited, it may not be able to support the model complexity required to solve the problem. The basic laws of statistics tell us that if we can, we should use all the data instead of sampling.
2. Choosing the best machine learning algorithm for a given problem is the key to success. For example, Gradient Lifting Tree (GBT) is a very popular supervised learning algorithm that is widely used by industry developers for its accuracy. However, despite its high popularity, we cannot blindly apply this algorithm to any problem. Instead, the algorithm we use should be an algorithm that best fits the data features while ensuring accuracy.
To prove this point, try to do an experiment to test the GBT algorithm and the linear support vector machine (SVM) algorithm on the popular text categorizaTIon dataset rcv1 and compare the accuracy of the two. We observed that on this issue, linear SVM is superior to GBT algorithm in terms of error rate. This is because in the text world, data is usually high-dimensional. A linear classifier can perfectly separate N samples in the N-1 dimension, so a sample model usually performs better on this data. In addition, the simpler the model, the lesser the problem can be achieved by using a limited training sample to avoid overfitting and learning the parameters, and providing an accurate model.
On the other hand, GBT is highly non-linear and more powerful, but in this environment it is more difficult to learn and more prone to overfitting, often with lower accuracy.
3. In order to get a better model, you must choose the best algorithm and related parameters. This may not be easy for non-data scientists. Modern machine learning algorithms have many parameters that can be adjusted. For example, for the popular GBT algorithm alone, there are twelve parameters that can be set, including how to control the size of the tree, the learning rate, the sampling method of rows or columns, the loss function, the regularization option, and so on. A unique project needs to find its optimal value for each parameter on a given data set and achieve the most accurate accuracy, which is really not an easy task. But in order to get the best results, data scientists need to train a large number of models, and intuition and experience will help them to try again based on cross-validation scores and then decide what parameters to use.
4. Machine learning models will get better with good data, and erroneous data collection and data processing will reduce your ability to build predictive and inductive machine learning models. Based on experience, it is recommended to carefully review the data related to the topic to gain insight into the process of generating data and behind-the-scenes data. Often this process identifies data quality issues related to records, features, values, or samples.
5. Understanding data characteristics and improving them (by creating new features or removing a feature) has a high impact on predictive power. One of the basic tasks of machine learning is to find a rich feature space that can be fully utilized by machine learning algorithms to replace raw data. For example, feature transformation is a popular method that can be implemented by extracting new features using mathematical transformations based on raw data. The final feature space (that is, the feature that is ultimately used to describe the data) is better able to capture the complexity of the data (such as nonlinearity and interaction between multiple features), which is critical for a successful learning process. .
6. In the application, choosing the appropriate objective/loss function inspired by business value is critical to the final success. Almost all machine learning algorithms are ultimately considered an optimization problem. According to the nature of the business, it is the key to the success of machine learning to properly set or adjust the optimized objective function.
Taking the support vector machine as an example, the generalization error of a two-class problem is optimized by assuming that the weights of all error types are equal. This loss-sensitive problem is not appropriate, such as fault detection, where some types of errors may be higher than other types. In this case, it is recommended to adjust the weight loss function of the SVM by adding more penalties to the specific error types to explain their weights.
7. Ensure that the training and test data are processed correctly so that when the model is deployed in production, the test data can simulate the input data. For example, we can see how important this is to time-dependent data. In this case, using standard cross-validation methods for training and adjustment, the results of the test model may be biased or even inaccurate. This is because it does not accurately simulate the nature of the input data on the implementation platform. In order to correct this, we must deploy the model in the same way as the model. We should use a time-based cross-validation to validate the training model with newer data.
8. Understand the generalization error of the model before deployment. The generalization error measures the performance of the model on unknown data. Because the performance of a model on training data does not mean that it performs well on unknown data. A well-designed model evaluation process used to simulate actual deployments is needed to estimate model generalization errors.
It's easy to violate the rules of cross-validation without paying attention, and there is no obvious way to express the inaccuracy of cross-validation, which usually happens when you are trying to find a shortcut calculation. Before any model is deployed, it is necessary to pay careful attention to the correctness of cross-validation to obtain a scientific assessment of deployment performance.
9. Know how to handle unstructured and semi-structured data such as text, time series, space, graphics, or image data. When most machine learning algorithms process data in a feature space, a feature set represents an object, and each element of the feature set describes a feature of the object. In practice, data is not introduced in this format, often from the most primitive format, and ultimately must be transformed into an ideal format that machine learning algorithms can recognize. For example, we must know how to use various computer vision techniques to extract features from images or how to apply natural language processing techniques to film text.
10. Learn to turn business problems into machine learning algorithms. Some important business issues, such as fraud detection, product recommendation, and accurate advertising, all have "standard" machine learning expressions and have achieved reasonable results in practice. Even for these well-known problems, there are still little-known but more powerful expressions that lead to higher prediction accuracy. Appropriate machine learning methods are less obvious for the business problems of small instances that are generally discussed in blogs and forums.
If you are a developer, learning these ten tips for success may seem like a difficult task, but don't be discouraged. In fact, developers are not data scientists. It is unfair to think that developers can make full use of all machine learning tools. But that doesn't mean developers don't have the opportunity to learn some standard data science to improve their applications. With the right enterprise solutions and increased automation, developers can do everything from model building to implementation deployment, using machine learning best practices to maintain high precision.
Automation is the key to extending machine learning in an application. Even if you can afford a small team of small data scientists and developers to work together, there is not enough talent. AutoModels like Skytree help developers automatically determine the best parameters and get the maximum model accuracy. An easy-to-use interface guides developers through training, tuning, and testing models to prevent statistical errors.
There are many ways to automate the machine learning process, including the artificial intelligence principles of data scientists or developers, allowing algorithms to think, learn, and take on more modeling tasks. In other words, it is wrong to think that data scientists can decouple from machine learning, especially on mission-critical models. Beware of this commitment to simple machine learning, that is, the ability to use machine learning without the need for proper and complex thinking or scalable application technology – which often does not result in high prediction accuracy and high machine learning Business value results. To make matters worse, using a bad model in an application can actually be counterproductive and quickly build untrusted products or services between its users.
Error in the model due to the lack of finding the best parameters due to the error of the model without sufficient data
Domestic communication, 4 g module in addition to support two kinds of LTE FDD LTE/TD - LTE standard, also compatible with 2 g / 3 g down, support td-scdma/WCDMA/EDGE/GP/GSM/CDMA1X/EVDO mode. The data service downstream rate can reach up to 100Mbps, the uplink rate can reach up to 50Mbps, providing customers with high-speed Internet access and wireless data connection and other functions. In addition to providing high-speed data access, it also provides voice, SMS, communication book and other functions, which can be widely used in notebook, tablet, touch screen communication devices, smart phones, video surveillance, multimedia mobile phones and vehicle equipment and other terminal products and vertical applications. Today, I will introduce you to what is 4G module and how to use 4G module.
What is 4G module
4G module said TD-LTE and FDD-LTE and other LTE network standards are collectively referred to. It has the characteristics of fast communication speed, wide network spectrum and flexible communication. 4G module refers to a product whose hardware is loaded to the specified frequency band, software supports the standard LTE protocol, and software and hardware are highly integrated and modular. The hardware integrates radio frequency and baseband on a small PCB board to complete wireless receiving, transmitting and baseband signal processing functions. The software supports voice dialing, SMS sending and receiving, dial-up networking and other functions.
Second, how to use 4G module
The 4G module is usually inserted into the circuit board or the patch module is welded to the circuit board, and the corresponding SIM card is inserted, and the network is connected through the 4G cellular network. In the common transparent transmission mode, data can be directly sent to the target server through the 4G module, and the 4G module can receive data from the server at the same time, and send the data to the serial port, which is received by the serial port device connected to the 4G module. Users do not need to pay special attention to the data conversion process between serial port data and network data packets, only need to insert the module to the target position, simple connection and parameter setting, you can achieve transparent transmission between the serial port and the network end.
What is the 4G module How does the 4G module work
4G module, also known as 4G transmission module, 4G communication module, 4G LTE module. It is a kind of hardware loaded to the specified frequency band, the software supports the standard LTE protocol, and the software and hardware are highly integrated modular products, with good compatibility, fast communication speed, large amount of communication data, wide network spectrum, communication flexibility and other characteristics, widely used in electric power, environmental protection, finance, industrial automation and other fields.
The working principle of 4G module
It has the characteristics of fast communication speed, wide network spectrum and flexible communication. The hardware integrates radio frequency and baseband on a small PCB board to complete wireless receiving, transmitting and baseband signal processing functions. The software supports voice dialing, SMS sending and receiving, dial-up networking and other functions.
Through high-speed networking, the 4G module transmits all terminal business data and equipment operating status to the business center in real time. Through the management monitoring platform, the terminal equipment operating status can be monitored in real time and equipment faults can be timely checked. The efficiency has been greatly improved in many aspects such as operating cost, operation scale and service timeliness. Suitable for shared massage chair, public charging pile, vending machine, vehicle system, environmental monitoring, power remote monitoring and other industries.
4G module refers to a product whose hardware is loaded to the specified frequency band, software supports the standard LTE protocol, and software and hardware are highly integrated and modular. It has the characteristics of fast communication speed, wide network spectrum and flexible communication. The hardware integrates radio frequency and baseband on a small PCB board to complete wireless receiving, transmitting and baseband signal processing functions. The software supports voice dialing, SMS sending and receiving, dial-up networking and other functions. Through high-speed networking, the 4G module transmits all terminal business data and equipment operating status to the business center in real time. Through the management monitoring platform, the terminal equipment operating status can be monitored in real time and equipment faults can be timely checked. The efficiency has been greatly improved in many aspects such as operating cost, operation scale and service timeliness.
Good compatibility
4G module supports FDD LTE/TD-LTE two LTE standards, can also be backward compatible with 2G/3G, support LTE-FDD, LET-TDD, WCDMA, TD-SCDMA, CDMA, GSM and other bands.
Large amount of communication data
4G module downstream rate can reach up to 150Mbps, uplink rate can reach up to 50Mbps, to provide customers with high-speed Internet access and wireless data connection and other functions, suitable for large data transmission applications, such as: remote video surveillance.
High communication speed
The 4G module has a lower ping value than the 2G/3G module, and it takes less time for the module to send data packets to receive the response from the server, which is suitable for applications requiring high response speed, such as remote alarm.
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Shenzhen MovingComm Technology Co., Ltd. , https://www.szmovingcomm.com