标签： 新上海sh419

1-0，1-1！ The Premier League is crazy for one night, fighting for four to celebrate the finale, two records are born, and the Red Army hopes for a miracle.

In the 37th round of the Premier League, Manchester United and Liverpool played at the same time. The outcome of this round of competition is directly related to the qualification of next season’s Champions League. In the end, Manchester United scored all three points in 1-0 away from Bournemouth, while Liverpool drew 1-1 at home with Aston Villa, and the Premier League competed for four, which basically declared the finale!

Newcastle United, like Manchester United in theory, has not yet locked in the Champions League! However, at present, Newcastle United are three points ahead of Liverpool in the latest curling iron, and there are still two rounds left. In the last two rounds, only one point is needed to lock in the Champions League qualification quota for the new season. Considering Newcastle’s huge lead in goal difference in the league, even if they suffer two consecutive defeats, even if Liverpool score three more points, it will be harmless! Newcastle’s last two opponents were leicester city and Chelsea!

After beating Bournemouth 1-0 away, Manchester United scored 69 points, which is the same as Newcastle United, which also played 36 rounds. Liverpool, with the last glimmer of hope, drew 1-1 at home with Aston Villa. After 37 rounds, their score was 66 points! But unlike Newcastle United, Newcastle United’s goal difference so far is +35, Liverpool’s goal difference is +28 and Manchester United’s goal difference is only +11!

In the case of Liverpool winning the final game, Manchester United must score another point! Manchester United’s opponents in the last two rounds were Chelsea and Fulham, and getting a point can be said to be like taking something out of the bag. Liverpool is looking forward to a miracle. If Manchester United suffer two consecutive defeats and win in the final round, Liverpool will 100% crowd out Manchester United and rank among the top four.

So far this season in the Premier League, Degea has won 16 clean sheets! As Degea’s only rival in the league, alysson, was beaten by Aston Villa for 14 times, Manchester United goalkeeper Degea won the Golden Glove Award in the Premier League this season with 16 times! This is the second time that Degea has won the Golden Glove Award in the Premier League. The first time was in the 2017-18 season!

Liverpool 1-1 Aston Villa. Firmino ushered in a farewell game at home, and in the 89th minute, he helped Liverpool to draw an absolute draw! Bid farewell to Anfield with a goal, and Premier League officials awarded firmino the best match of the game, which was the last home game of the Brazilian striker’s Liverpool career! Firmino has scored 16 goals in his Liverpool career when he came off the bench, which is second only to David Fairclough’s 18 goals in team history.

In the last six years, Liverpool have beaten the quartet in the Champions League and won one Champions League champion and two Champions League runners-up! However, now, Liverpool is facing the embarrassment of not playing in the Champions League in the new season!

Infrastructure for training AI to solve common problems

In order to train artificial intelligence models that can solve common problems, infrastructure is needed to provide support. These infrastructures are usually composed of hardware, software and tools to improve the efficiency and accuracy of model training. This article will introduce the infrastructure for training AI to solve common problems.

I. Hardware infrastructure

When training artificial intelligence models, it is usually necessary to use high-performance computing hardware to provide support. The following are several common hardware infrastructures:

  1. CPU: The central processing unit (CPU) is a general-purpose computing hardware, which can be used to run various types of software, including artificial intelligence models. Although the performance of CPU is relatively low, it is still useful in training small models or debugging.

  2. GPU: A graphics processor is a special computing hardware, which is usually used to process images and videos. Because of its highly parallel structure, GPU can provide higher computing performance than CPU when training artificial intelligence models, so it is widely used.

  3. TPU: Tensor processor is a kind of hardware specially used for artificial intelligence computing, developed by Google. The performance of TPU is higher than that of GPU, and it is suitable for large-scale artificial intelligence model training and reasoning.

Second, the software infrastructure

In addition to hardware infrastructure, some software tools are needed to support the training of artificial intelligence model. The following are some common software infrastructures:

  1. Operating system: Artificial intelligence models usually need to run on an operating system, such as Linux, Windows or macOS.

  2. Development environment: Development environment usually includes programming language, editor and integrated development environment (IDE) for writing and testing artificial intelligence models. Common development environments include Python, TensorFlow, PyTorch and Jupyter Notebook.

  3. Frames and libraries: Frames and libraries provide some common artificial intelligence model algorithms and data processing tools, making model development and training more convenient. Common frameworks and libraries include TensorFlow, PyTorch, Keras and Scikit-Learn.

Third, the tool infrastructure

In addition to the hardware and software infrastructure, some tools are needed to support the training of artificial intelligence models. The following are several common tool infrastructures:

Dataset tool: Dataset tool is used to process and prepare training datasets, such as data cleaning, preprocessing, format conversion, etc. Common data set tools include Pandas, NumPy and SciPy.

2 Visualization tools: Visualization tools are used to visualize the training process and results to help users better understand the performance and behavior of the model. Common visualization tools include Matplotlib, Seaborn and Plotly.

Automatic parameter tuning tool: The automatic parameter tuning tool is used to optimize the parameters of the model to improve the performance and accuracy of the model. Common automatic parameter tuning tools include Optuna, Hyperopt and GridSearchCV.

In short, training artificial intelligence models to solve common problems requires the use of a variety of infrastructures, including hardware, software and tools. These infrastructures are designed to improve the efficiency and accuracy of model training, so that the model can better solve various practical problems. In practical application, users need to choose the appropriate infrastructure according to specific requirements and data characteristics, and design and implement it accordingly.