Intelligent education is the main direction of China's future development. In the information age, the traditional teaching mode can no longer meet the higher requirements for information and knowledge quality in students' learning. Based on this, this paper proposes a method to help improve neural network training speed, reduce training time and improve memory efficiency. First, it introduces the relevant theories of intelligent pedagogy through literature analysis, and then designs a set of experimental scheme according to the actual situation, and verifies its effectiveness and accuracy. Finally, the feasibility of the design scheme is verified by the experimental results. The test results show that the data processing time of the model is relatively fast, the delay time is relatively low, and the compatibility rate of the model is up to more than 90%. This provides a reference for further exploration of AI education in the future, and also contributes to the future development of China.

Yang Wang and Shangbin Li. Appliance of Deep Learning in Intelligent Education. International Journal of Neural Network (2022), Vol. 3, Issue 4: 69-76. https://doi.org/10.38007/NN.2022.030409.

[1] Kenza Belhouchette:Facial Action Units Detection to Identify Interest Emotion: An appliance of Deep Learning. Adv. Data Sci. Adapt. Anal. 14(1&2): 2250006:1-2250006:15 (2022). https://doi.org/10.1142/S2424922X22500061

[2] Noé Tits:Controlling the emotional expressiveness of synthetic speech: a deep learning approach. 4OR 20(1): 165-166 (2022). https://doi.org/10.1007/s10288-021-00473-2

[3] Matthew Avaylon, Robbie Sadre, Zhe Bai, Talita Perciano:Adaptable Deep Learning and Probabilistic Graphical Model System for Semantic Segmentation. Adv. Artif. Intell. Mach. Learn. 2(1): 288-302 (2022). https://doi.org/10.54364/AAIML.2022.1119

[4] Atta-ur-Rahman, Muhammad Umar Nasir, Mohammed Gollapalli, Muhammad Zubair, Muhammad Aamer Saleem, Shahid Mehmood, Muhammad Adnan Khan, Amir Mosavi:Advance Genome Disorder Prediction Model Empowered With Deep Learning. IEEE Access 10: 70317-70328 (2022). https://doi.org/10.1109/ACCESS.2022.3186998

[5] Shanza Abbas, Muhammad Umair Khan, Scott Uk-Jin Lee, Asad Abbas, Ali Kashif Bashir:A Review of NLIDB With Deep Learning: Findings, Challenges and Open Issues. IEEE Access 10: 14927-14945 (2022). https://doi.org/10.1109/ACCESS.2022.3147586

[6] Saif Hameed Abbood, Haza Nuzly Abdull Hamed, Mohd Shafry Mohd Rahim, Amjad Rehman, Tanzila Saba, Saeed Ali Bahaj:Hybrid Retinal Image Enhancement Algorithm for Diabetic Retinopathy Diagnostic Using Deep Learning Model. IEEE Access 10: 73079-73086 (2022). https://doi.org/10.1109/ACCESS.2022.3189374

[7] Abd-Elsalam R. Abd-Elhay, Wael A. Murtada, Mohamed I. Youssef:A Reliable Deep Learning Approach for Time-Varying Faults Identification: Spacecraft Reaction Wheel Case Study. IEEE Access 10: 75495-75512 (2022). https://doi.org/10.1109/ACCESS.2022.3191331

[8] Andres J. Anaya-Isaza, Matha Zequera-Diaz:Detection of Diabetes Mellitus With Deep Learning and Data Augmentation Techniques on Foot Thermography. IEEE Access 10: 59564-59591 (2022). https://doi.org/10.1109/ACCESS.2022.3180036

[9] Meftah Elsaraiti, Adel Merabet:Solar Power Forecasting Using Deep Learning Techniques. IEEE Access 10: 31692-31698 (2022). https://doi.org/10.1109/ACCESS.2022.3160484

[10] [Arwa Saif Fadel, Mostafa Elsayed Saleh, Osama Ahmed Abulnaja:Arabic Aspect Extraction Based on Stacked Contextualized Embedding With Deep Learning. IEEE Access 10: 30526-30535 (2022). https://doi.org/10.1109/ACCESS.2022.3159252

[11] Leticia Monasterio Expósito, Daniel Pizarro, Javier Macías Guarasa:Label Augmentation to Improve Generalization of Deep Learning Semantic Segmentation of Laparoscopic Images. IEEE Access 10: 37345-37359 (2022). https://doi.org/10.1109/ACCESS.2022.3162630

[12] Abdul Karim Gizzini, Marwa Chafii:A Survey on Deep Learning Based Channel Estimation in Doubly Dispersive Environments. IEEE Access 10: 70595-70619 (2022). https://doi.org/10.1109/ACCESS.2022.3188111

[13] Armando M. Fernandes, Andrei B. Utkin, Paulo Chaves:Automatic Early Detection of Wildfire Smoke With Visible Light Cameras Using Deep Learning and Visual Explanation. IEEE Access 10: 12814-12828 (2022). https://doi.org/10.1109/ACCESS.2022.3145911

[14] Olawunmi George, Sarthak Dabas, Abdur Sikder, Roger O. Smith, Praveen Madiraju, Nasim Yahyasoltani, Sheikh Iqbal Ahamed:State-of-the-Art Versus Deep Learning: A Comparative Study of Motor Imagery Decoding Techniques. IEEE Access 10: 45605-45619 (2022). https://doi.org/10.1109/ACCESS.2022.3165197

[15] Balla Goutam, Mohammad Farukh Hashmi, Zong Woo Geem, Neeraj Dhanraj Bokde:A Comprehensive Review of Deep Learning Strategies in Retinal Disease Diagnosis Using Fundus Images. IEEE Access 10: 57796-57823 (2022). https://doi.org/10.1109/ACCESS.2022.3178372

[16] Rakshitha Godahewa, Chang Deng, Arnaud Prouzeau, Christoph Bergmeir:A Generative Deep Learning Framework Across Time Series to Optimize the Energy Consumption of Air Conditioning Systems. IEEE Access 10: 6842-6855 (2022). https://doi.org/10.1109/ACCESS.2022.3142174

[17] Eun-Jeong Han, Sengly Muy, Jung-Ryun Lee:Balancing Fairness and Energy Efficiency in SWIPT-Based D2D Networks: Deep Reinforcement Learning Based Approach. IEEE Access 10: 64495-64503 (2022). https://doi.org/10.1109/ACCESS.2022.3182686

[18] K. P. Rasheed Abdul Haq, V. P. Harigovindan:Water Quality Prediction for Smart Aquaculture Using Hybrid Deep Learning Models. IEEE Access 10: 60078-60098 (2022). https://doi.org/10.1109/ACCESS.2022.3180482