Note: This course can provide invoices, conference fees, training fees, materials fees, service fees, etc., and provide relevant reimbursement vouchers.
All enterprises, institutions, universities and research institutes:
Python has become one of the most popular programming languages: according to the latest TIOBE rankings, Python has surpassed C#, becoming the top 4 most popular languages in the world along with Java, C, and C++. The simplicity, readability and extensibility of the Python language, and the development environment formed by its many extension libraries are very suitable for engineering technology, scientific researchers to process experimental data, make charts, and even develop scientific computing applications. At present, Microsoft, Tencent, Google, Facebook, Baidu, Alibaba, etc. take deep learning as the research focus of future industrial and Internet development. The Chinese Academy of Sciences, Tsinghua University, Peking University and other universities and research institutes have established professional research centers and laboratories to transform deep learning into scientific and technological achievements, which has significantly promoted the development of deep learning applications.
In order to further promote the development of research work in Python, artificial intelligence, machine learning, deep learning applications and current practical projects in colleges and universities, scientific research institutes and enterprises and institutions , and the China Science and Technology Soft Research (Beijing) Science and Technology Center ( http: //www.fzby.org.cn/ ) specially invited front-line experts in the field of artificial intelligence academic and R&D to jointly hold a national artificial intelligence Python machine learning and deep learning training course. The main theme is theory combined with practice, and the course emphasizes hands-on operation; the content is mainly based on code landing, with theoretical explanation as the root, and formula derivation as a supplement. It is sponsored by China and undertaken by Beijing Fuzhuo Baiyang Technology Co., Ltd. The specific matters are as follows:
1 Training advantages
2 Training experts
Senior experts of scientific research institutions such as Chinese Academy of Sciences and Tsinghua University. Front-line practical expert in the field of artificial intelligence , proficient in using Python artificial intelligence programming technology, concerned about various open source projects in the field of deep learning, such as TensorFlow, Caffe, Pytorc, etc. I like the teaching style that combines theory and practice. The curriculum is arranged from the shallower to the deeper, and the system is clear and complete. He has 2 patents and has completed a number of artificial intelligence related projects for schools, hospitals, enterprises, meteorological bureaus and other units. Invited to do in-house training on artificial intelligence technology for many large enterprises including China Mobile , China Telecom , Bank of China , Hua Xia Bank , Pacific Insurance , State Grid , CNOOC , Gree Electric, etc., including Fortune 500. The industry's top IT training platform 300,000 students have a favorable rate of 99%;
3 training time
August 26 , 2022 — August 28 , 2022 _ _
4 Training content
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An introduction to artificial intelligence |
1. Introduction to Artificial Intelligence/Machine Learning/Neural Networks/Deep Learning 2. Introduction to deep learning applications 3. Artificial intelligence technology framework |
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Second, python basic learning |
1.print use 2. Operators and Variables 3. Loop 4. List-tuple dictionary 5.if condition 6. Function 7. Modules 8. Use of classes 9.input usage 10. File read and write 11. Exception handling |
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3. Use and learn the scientific computing package numpy |
1. numpy properties 2. Create an array 3. numpy operations 4. Random number generation and matrix operations 5. numpy indexing 6.array merge 7.array segmentation |
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Fourth, the data analysis library pandas use learning |
1. Series, DataFrame 2. Select data 3. Assignment and operation 4. Read and write files 5. Merge Cases: Handling Missing Data |
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Five, the drawing toolkit matplotlib learning |
1. Basic usage 2.figure image 3. Set the coordinate axis 4. legend legend 5. scatter scatter plot |
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Six, python face detection project combat |
1. Use python to implement face detection function |
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7. Fundamentals of Artificial Intelligence and Machine Learning |
1. Overview of Artificial Intelligence 2. Overview of Machine Learning 3. Application Analysis of Machine Learning Algorithms |
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Eight, regression algorithm |
1. Univariate Linear Regression 2. Cost function 3. Gradient descent 4. sklearn univariate linear regression application 5. Multiple Linear Regression 6. sklearn multiple linear regression application Case: The relationship between wine quality and time |
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Nine, KNN classification algorithm |
1. Introduction to KNN classification algorithm 2. KNN classification algorithm application 3. KNN implementation Case: Classification of irises |
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Ten, decision tree algorithm |
1. Introduction to decision tree algorithm 2. Definition of entropy 3. Decision tree algorithm and application implementation Case: User Purchase Behavior Prediction |
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Eleven, support vector machine |
1. When is the optimal classification surface 2. What is the essence of the SVM algorithm? 3. How to deal with support vector machine when it is linearly inseparable Case: SVM completes face recognition application |
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12. K-means clustering algorithm |
1. Introduction to K-means algorithm 2. K-means algorithm application 3. Practical application case of K-means algorithm Case: Cluster Analysis of NBA Team Strength |
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13. Ensemble Algorithms and Random Forests |
1. Introduction to Bagging Algorithm 2. Random forest modeling method 3. Introduction to Adaboost algorithm 4.Introduction to Stacking Algorithm 5. Introduction to Voting Algorithm |
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14. Feature Engineering Application Cases |
1. Fill in missing values 2. Unique value processing 3. Filter useless features 4. Handling multi-valued ordered features 5. Handling multi-valued unordered features 6. Feature data scaling 7. PCA weft reduction 8. Data Balance Model Training and Evaluation |
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15. Basics of Deep Learning - Introduction to Neural Networks |
1. History of artificial neural network development 2. Single layer perceptron 3. Activation function, loss function and gradient descent 4. Introduction to BP Algorithm Case: BP algorithm solves the problem of handwritten digit recognition |
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Sixteen, Tensorflow basic application |
1. Tensorflow installation 2. Basic knowledge of Tensorlfow 3. Tensorflow Linear Regression 4. Tensorflow nonlinear regression 5. Mnist data set Softmax explanation 6. Use BP neural network to build handwritten digit recognition 7. Explanation and use of cross-entropy 8. Overfitting, Regularization, Dropout 9. Various optimizers Optimizer |
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17. Introduction to Pytorch |
1. Pytorch installation and basic knowledge 2. Pytorch builds a network to complete linear regression applications 3. Pytorch builds a network to complete nonlinear regression applications Complete handwritten digit recognition |
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Eighteen , convolutional neural network CNN application |
1.CNN Convolutional Neural Network 2. Convolutional layer, pooling layer (mean pooling, max pooling) 3. CNN handwritten digits case |
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Nineteen , long short-term memory network LSTM application |
1. RNN recurrent neural network 2. Long Short-Term Memory Network LSTM 3. LSTM application case |
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20. Image recognition using the pretrained model Inception-v3 |
1. Inception-v3 model explanation 2. Use the trained model for image recognition |
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Twenty-one , image recognition model VGG16 project actual combat (weather phenomenon classification, scene classification) |
1. VGG16 model explanation 2. Data increase 3. Use transfer learning to complete weather phenomenon classification 4. Use transfer learning to complete scene classification |
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22. Actual combat of natural language processing projects |
1. Introduction to Natural Language Processing Project 2. Introduction to word2vec 3. Train a new text classification model with LSTM |
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23. Introduction to Target Detection Algorithms |
1. Introduction to the target detection project 2. Detailed explanation of R-CNN model 3. Detailed explanation of SPPNET model 4. Detailed explanation of Fast-RCNN model 5. Detailed explanation of Faster-RCNN model |
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24. Use target detection algorithm to complete gesture recognition |
1. Data preparation and labeling methods 2. Detecting module introduction 3. Gesture recognition model training 4. Application of gesture recognition model |
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Twenty-five, optical character recognition OCR algorithm actual combat |
1. Use the OCR model to accurately identify the Chinese and English text in the picture and extract it. |
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Twenty-six, face recognition actual combat |
1. Face detection in practice 2. Face key point extraction practice Case: Face Recognition in Action |
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27. Auxiliary courses |
1. Discuss and propose solutions for practical problems faced by students 2. Establish QQ group and WeChat group 3. Equipped with machine learning and deep learning development teaching materials, it is convenient to gradually improve the ability after class. 4. The algorithms and models learned in the training can be applied to multiple industries such as communication /government/ medical /agriculture/industry/finance/meteorology/military industry. |
5 training costs
Class A: 3900 yuan/person (including training fee, material fee, Class A certificate fee, guidance fee, invoice fee, etc.)
Category B: 4,800 yuan per person (including training fees, materials fees, A +B certificate fees, guidance fees, invoice fees, etc.)
Category C: 5300 yuan per person ( including training fees, materials fees, A +B+C certificate fees, guidance fees, invoice fees, etc.)
6 Issuance of certificates
Category A: Obtainable : Senior "Artificial Intelligence Application Engineer" professional ability certificate issued by China Soft Research (Beijing) Science and Technology Center ;
Category B: Obtainthe senior "Artificial Intelligence Application Engineer" vocational skill certificate issued by the Vocational Education Research Institute of the Chinese Academy of Management Sciences, which is included in the talent pool of the Central Management Institute, and can be checked nationwide and can be used as a valid certificate for promotion and rating;
Category C: Obtainable: The senior " Artificial Intelligence Application Manager " vocational skill certificate issued by the China Communications Industry Association (national association) of the Ministry of Industry and Information Technology, which can be checked nationwide and can be used as a valid certificate for promotion and rating.
7 Preferential policies
1. Students can get a discount of 300 yuan with their student ID;
2. Group registration of more than 3 people (inclusive) can reduce 200 yuan per person;
8 How to register
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