What do you need to learn about Python artificial intelligence?

The reason why many students learn Python is that they are interested in artificial intelligence and are interested in working in related industries. Today we will talk about some of the skills required in this direction.

#Here we are mainly talking about programming skills. (Recommended learning: Python video tutorial)

If you plan to use Python as the main development language (which is also the current mainstream in the field of artificial intelligence), then the development foundation of Python is a must What you need to master is the foundation of all Python-based development. You must have an understanding of Python's basic syntax, data types, and common modules, be able to correctly use logic such as conditions and loops, master data structures such as list and dict and their common operations, and understand the concepts and uses of functions, modules, and object-oriented. wait.

After you have become proficient in this, you need to learn the Python tool library related to data processing:

NumPy

NumPy provides many data structures and methods for mathematical calculations, which are much more efficient than Python's own list. The ndarray it provides greatly simplifies matrix operations.

Pandas

Data processing tool based on NumPy. Provides a large number of models and methods for data statistics and analysis. One-dimensional Series, two-dimensional DataFrame and three-dimensional Panel are its main data structures.

SciPy

A Python toolkit for scientific calculations, providing many methods such as calculus, linear algebra, signal processing, Fourier transform, curve fitting, etc. .

Matplotlib

Python’s most basic drawing tool. It is rich in functions and highly customizable, and can meet almost all kinds of daily drawing needs, but the configuration is complicated.

As long as you use Python to deal with data, you cannot avoid the above libraries, so be sure to learn them.

After that, you need to choose a more professional tool kit for research and application according to your specific direction.

Python’s most famous tool libraries in artificial intelligence mainly include:

Scikit-Learn

Scikit-Learn is A machine learning library developed in Python, which contains a large number of machine learning algorithms and data sets, and is a convenient tool for data mining. It is based on NumPy, SciPy and Matplotlib and can be installed directly via pip.

TensorFlow

TensorFlow was originally developed by Google for machine learning research. TensorFlow can run on GPU or CPU and excels in deep learning. It is currently widely used in both academic research and engineering applications. But TensorFlow is relatively low-level, and more often we will use other frameworks developed based on it.

Theano

Theano is a mature and stable deep learning library. Similar to TensorFlow, it is a relatively low-level library suitable for numerical calculation optimization and supports GPU programming. There are many libraries based on Theano that take advantage of its data structures, but its interface is not very user-friendly for development.

Keras

Keras is a highly modular neural network library written in Python and capable of running on TensorFlow or Theano. Its interface is very simple and easy to use, which greatly improves development efficiency.

Caffe

Caffe is well-known in the field of deep learning. It was developed by the Berkeley Vision and Learning Center (BVLC) and community contributors. It has the advantages of modularity and high performance, especially in the field of computer vision. Caffe itself is not a Python library, but it provides an interface to Python.

PyTorch

Torch is also an old machine learning library. The framework used in Facebook's artificial intelligence research is Torch, and DeepMind also used Torch before being acquired by Google (later converted to TensorFlow), which shows its capabilities. However, the Lua language is not popular enough. Until its Python implementation PyTorch appeared.

MXNet

Amazon AWS's default deep learning engine, distributed computing is one of its features, supporting multiple CPU/GPU training networks.

With the help of these powerful tools, you can already use various classic models to train and predict data sets. But if you want to be a qualified artificial intelligence developer, it is not enough to just call the tool's API and adjust parameters.

Python is an important tool for artificial intelligence development, and programming is an essential skill in this direction. But mastering Python does not mean mastering artificial intelligence. The core of artificial intelligence is machine learning (Machine Learning) and deep learning. Their basis is mathematics (advanced mathematics/linear algebra/probability theory, etc.), and programming is the means of implementation.

So if you want to enter this field, in addition to programming skills, mathematical foundation is essential, and then you also need to understand data mining, machine learning, deep learning and other knowledge.

This is not a quick path that can be achieved in a few months, but if you persist, you will definitely gain something.

For more Python related technical articles, please visit the Python Tutorial column to learn!

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