foreword
Python has become a mainstream language for developing artificial intelligence because it is easy to learn, has a large community of users and developers, and is supported by many popular libraries and frameworks.
Python is a relatively easy language to learn, and its syntax is similar to natural languages. It is also a dynamically typed language, which means that variables do not need to be declared before they are used. This makes it a good choice for beginners and developing quick prototypes.
Python has a large community of users and developers. That means there's plenty of documentation and support, and lots of people willing to help with issues.
Python is supported by many popular libraries and frameworks. This means that there are many tools available for developing AI applications. Some of the most popular Python libraries and frameworks include NumPy, SciPy, Pandas, and TensorFlow.
Java, Ruby, and other programming languages are also used to develop AI applications. However, Python is the most popular language for this purpose. Some reasons for this include the following:
- Python is easier to learn than Java or Ruby.
- Python has a larger community of users and developers.
- Python is supported by more popular libraries and frameworks.
Below is an example on how to develop an artificial intelligence application using Python. Suppose we want to develop a program that can classify images of cats and dogs. We can use the following steps:
- Collect a dataset of images of cats and dogs.
- Use a library like TensorFlow to train a machine learning model on a dataset.
- Use the model to classify new images of cats and dogs.
Here is an example of Python code that can be used to train a machine learning model on a dataset of images of cats and dogs:
import tensorflow as tf
# Load the dataset
images, labels = tf.keras.datasets.mnist.load()
# Flatten the images
images = images.reshape(images.shape[0], images.shape[1] * images.shape[2])
# Create the model
model = tf.keras.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(10, activation='softmax'))
# Train the model
model.fit(images, labels, epochs=10)
# Evaluate the model
model.evaluate(images, labels)
Here is an example of Python code that can be used to classify new cat and dog images:
# Load the model
model = tf.keras.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(10, activation='softmax'))
# Load the new image
image = tf.keras.preprocessing.image.load_image('image.jpg')
# Flatten the image
image = image.reshape(image.shape[0], image.shape[1] * image.shape[2])
# Predict the class
prediction = model.predict(image)
# Print the class prediction
print(prediction)
This is just a simple example showing how Python can be used to develop artificial intelligence applications. Python has many other capabilities, and there are many resources online to help you learn how to use it for AI development.
1. Learning routes in all directions of Python
Just started learning python, if you don't even plan the complete learning steps, it is basically impossible to learn python. He sorted out all the directions of Python to form a summary of knowledge points in various fields.(The wife in the picture is too big. I can’t put it here. If you don’t have a full version, you can get it for free at the end of the article)
2. Getting started with a full set of learning videos
When we watch videos and learn, we can’t just move our eyes and brain without using our hands. A more scientific learning method is to use them after understanding. At this time, the hands-on project is very suitable.
Three, Python operation example
Learning python is the same as learning mathematics. You can’t just read the book without doing the questions. Looking directly at the steps and answers will make people mistakenly think that you have mastered everything, but you will still be at a loss when you encounter a problem.
Therefore, in the process of learning python, you must remember to write more codes by hand. You only need to read the tutorial once or twice.
4. Python employment project actual combat
We must learn Python to find a high-paying job or a high-paying part-time job. The following are some practical projects that companies can use. After learning these, I believe everyone will be able to find a satisfactory job.
11 Django Framework
16 WeChat public account
18 Common crawler module usage
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21 Data Analysis
22 Machine Learning
There are other things, such as my own Python introductory graphic tutorials, you can use your mobile phone to learn knowledge when you don’t have a computer, and after learning the theory, you can type the code to practice verification, and there is also the library information of the Chinese version of Python. , MySQL and HTML tags, etc., these are things that can be given to fans.
Data collection
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Good article recommendation
Understand the prospect of python: https://blog.csdn.net/SpringJavaMyBatis/article/details/127194835
Learn about python's part-time sideline: https://blog.csdn.net/SpringJavaMyBatis/article/details/127196603