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The artificial intelligence of the industry outlet will have a talent gap of more than 5 million people in China, while the number of artificial intelligence talents in China is currently only 50,000 (data from the Education and Examination Center of the Ministry of Industry and Information Technology).
And the current job premium is quite serious. In 2017, artificial intelligence ranked third in the salary of Internet jobs, with a monthly salary of 20.1k. If the general salary in 16 months is calculated, then the salary of artificial intelligence in 2017 is 2.01*16=32.16 Ten thousand. Look at a set of salary data for 2018:
In January 2018, the "General High School Curriculum Plan and Curriculum Standards for Chinese and Other Subjects" issued by the Ministry of Education added AI-related courses such as data structure, artificial intelligence, and open source hardware design.
This means that newcomers to the workplace and students who are looking for a job, if you don't work hard, you will be eliminated! After all, this is an era of big waves and gold rush. There is no one skill that can stand by, how to "explore the territory" and "start a family and start a business"!
So if you are not satisfied with your major/job, and if you want to make further progress, now is the best time to enter the field of artificial intelligence to study employment/change careers.
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To this end, Tianshan College specially launched "Teach you to start artificial intelligence from scratch in three months! | Deep Learning Essence Practice Course", the beauty lecturer 30+ blog posts laid the foundation, creating 200+ class hours of quality courses !
Click on the video to try it out for free
Click on the video to try it out for free
course features
Comprehensive planning: Covering the current mainstream deep learning fields, including image recognition, image detection, natural language processing, GAN, distributed training framework, etc.
Highlight the key points : abandon the cumbersome mathematical proofs, proceed from reality, highlight key points, and master key knowledge in a short period of time.
Practical drills : The course contains multiple practical cases, combined with practical project experience to teach you how to do deep learning projects in the enterprise.
Course Outline
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Chapter 1: What is Artificial Intelligence
Artificial Intelligence Background Introduction
Preliminary environmental preparation
Chapter 2: Introductory Basics of Deep Learning
Deep Learning Environment Preparation
Tensorflow Quick Start 1 - Basic Concepts and Framework
Tensorflow Quick Start 2 - Practical Exercise and Model Training
Tensorflow Quick Start 3 - Skills Summary
Deep Learning Mathematical Knowledge List
Homework: Example: Train a binary classification model on your own data
Homework Explanation: How to Make Your Own Dataset
Chapter 3: Understanding of Traditional Neural Networks and Parameters
What is a multilayer perceptron
The principle, category and implementation of activation function
The principle, category and implementation of loss function
Gradient descent method (1)
Gradient descent method (2)
How to set the learning rate
Regularization method (1)
Regularization method (2)
Example: Identifying the type of flower
Homework: Change different parameters to improve the accuracy of identifying flower types
Homework Explanation: The principle of the change of the accuracy rate due to the change of different parameters
Chapter 4: Forward and Backpropagation
The principle of forward propagation
Code implementation of forward propagation
The principle of backpropagation
Code implementation of backpropagation
Example: Write a complete BP by yourself
Assignment: Write an Autoencoder
Homework Explanation: How to Write an Autoencoder
Chapter 5: The Principle and Application of Self-encoding Autocoder
What is Autoencoder
The principle and implementation of Autoencoder
Difference between Autoencoder and PCA
Variants of Autoencoder (1)
Variants of Autoencoder (2)
Example: Application of Autoencoder and Clustering in Predicting User Preferences
Homework: Use Autoencoder to reduce dimensionality of massive data
Homework explanation: How to efficiently use Autoencoder to reduce dimensionality
Chapter 6: Classical Convolutional Neural Networks and Image Classification
The network structure and realization of LeNet
The network structure and implementation of AlexNet
Network Structure and Implementation of Vgg
The network structure and realization of GoogLeNet
The network structure and realization of ResNet
Example: Image classification on cifar-10 data with classical convolutional neural networks
Chapter 7: Object Detection
Traditional object detection methods
The first generation algorithm: Region CNN
Upgrade: SPP Net, Fast RCNN, Faster RCNN
Deep Learning Takes Another Way: YoLo and SSD
Example: The core of autonomous driving: real-time object detection
Chapter 8: Transfer Learning
theoretical analysis
Migration model & original training model
How to design a new network
Example: Expression Recognition / Face Recognition / Animal Recognition
Chapter 9: Recurrent Neural Networks (RNNs)
Detailed explanation of RNN principle
Sentiment Analysis Project Introduction
Example: Sentiment Analysis
Chapter 10: Natural Language Processing
Before processing: speech to text
Word expression: word vectors and word2vec
Statement Generation LSTM
Example: teach you to implement a simple chatbot
Chapter 11: Important Applications of Deep Convolutional Neural Networks
Picture quiz
Image Mode Conversion
High-definition image
Go program, Alpha go
Autonomous Gaming Robot, DeepMind Atari
Example: Image Art Style Transformation
Chapter 12: Unsupervised Learning: Adversarial Networks GANs
Traditional Unsupervised Learning Autoencode, K Means, Sparse Coding
RBM Restricted Boseman Machine, Another Branch of Deep Learning
Generative Adversarial Network GAN
Example: Machine-generated pictures to make fakes look real
Chapter 13: High Performance Computing
The realization process of the unit price single card
The realization process of single-machine multi-card
Implementation and Deployment of Multiple Machines and Single Card
Implementation and deployment of multiple machines and multiple cards
Example: Distributed training Example: Building a distributed training framework based on docker
Course evaluation
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Course Q&A
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flash sale
Original price 499
The current price is only 299!
It only needs to spend 3+ yuan per day,
A set of courses with an annual salary of 50+ to take home!
For two consecutive years, watching repeatedly,
VIP member group, answer questions online!
Charlotte Hu
Senior Algorithm Engineer
Good at explaining deep learning and machine learning algorithms in an easy-to-understand way, familiar with deep learning frameworks such as Tensorflow, PaddlePaddle, etc., responsible for many machine learning landing projects, such as automatic spam filtering, user-level precision marketing, distributed deep learning platform construction Wait, it's got pretty good results. Blog column: https://www.cnblogs.com/charlotte77/
what are you waiting for? Come and join "Three months to teach you to start artificial intelligence from zero! ! "Bar! The future will be our time!
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