Learn Deep Learning and the frontier of AI technologies
The emergence of Deep Learning technology has dramatically improved AI technology, and had a big impact on diverse industry and social institution. In the near future, every industry and its types should be influenced by AI technologies. Deep Learning JP is offering a series of educational programme for the future that AI technologies would have played an important role in society.
Deep Learning Basics
The programme starts from MLP and basics of neural networks, core technologies and new topics of Deep Learning、from basic to advance. Based on the idea “Practice makes perfect”, we aim to let you acquire the technique through the exercise. In exercise, you can concentrate on the point of the lecture by using our specific development platform “ilect.net” : enables you to code in Python with GPU on a browser（The detail of iLect is here）, save time and effort for preparing huge amounts of knowledge and setting up development environments for Deep Learning.
- 「Frontier Artificial Intelligence I」2017（For the graduate students of the University of Tokyo）
- 「Deep Learning Basics」2017（Open class）
- 「Frontier Artificial Intelligence I」2016（For the graduate students of the University of Tokyo）
- 「Deep Learning Basics」2016（Open class）
- 「Deep Learning Basics」2015（Open class）
Advanced Deep Learning Course
After you finishing Deep Learning Basics, you will obtain technical skills with handling actual data and real problems through this practical project-style lecture using big data analysis and reinforcement learning with GPU server as subjects.
- 「Frontier Artificial Intelligence II」2017（For the graduate students of the University of Tokyo）
- 「Applied Deep Learning」2017（Open class）
- 「Frontier Artificial Intelligence II」2016（For the graduate students of the University of Tokyo）
Deep Learning Developer Course
For engineers, this course deals with practical application development with Deep Learning technologies, provides practical contents: learning technique, data analysis using GPU server etc…
- Deep Learning Basics
- Network construction
- Back propagation and computational graph
- Reuse of pre-trained network
- The technique of learning
- RNN and a neural language model