Machine learning and deep learning

Here comes the chance!

Portal (or scan the poster QR code) : Click here for details.

Just in Beijing, from October 13 to 14, Jizhi prepared a two-day offline actual combat training camp course (limited to 60 seats). We will make your arrangement clear. We are afraid that you will not be able to learn, but you will not come.

14 days of pre-school +2 days of intensive training + no less than 3 task challenges

(Full ticket return for outstanding students)

Course Introduction:

This training camp emphasizes hands-on activities; The content is based on code implementation, with theoretical explanation as the root, supplemented by formula derivation. Explain the model theory and code practice of machine learning and deep learning, sort out the technical framework of machine learning, deep learning and computer vision, and fundamentally solve the problem of how to use and optimize the model; In each class, algorithm theory and a small amount of formula derivation are first elaborated, and then real data is used for data mining, machine learning, data analysis of deep learning, feature selection, parameter tuning and result comparison.

Through this training camp, you can:

  • Understand the way of thinking and key techniques of machine learning;

  • Understand the application of deep learning and machine learning in current industry;

  • Able to select suitable algorithm model and write code according to data distribution;

  • Preliminary proficiency in data mining, machine learning, deep learning and other work using Python.

Is this course right for me? :

  • Give you study materials two weeks in advance as a tool for the preview phase

  • The important part of the two-day training camp is to simulate the actual combat design of the real PK, through the actual combat task to complete a complete code project

  • After the offline training camp, the student group will continue to be retained, which is convenient for continuous communication.

Matters needing attention:

A laptop, Windows or Mac, is required for the course.

Case highlights:

Radio electromagnetic frequency field intensity prediction

Time series analysis

Review past