Lecture topic: A brief introduction to deep learning and its development tools
Keynote speaker:John Cheng
Lecture time:10:00 on December 27rd,2020
Venue:Tencent Meeting,https://meeting.tencent.com/s/otISvIVsQnQe，ID：563 852 640
Abstract:
The talk will give a very brief introduction to deep learning and its development platform and tools. Emphasis will be placed on the conceptual understanding but not deep technical level on what deep learning is, what we can do with it, and what are typical issues have been successfully addressed already.
The basics of TensorFlow will be briefly explained, which is an open-source library developed by Google primarily for deep learning applications. Providing a collection of workflows to develop and train models using Python, and to easily deploy in the cloud, TensorFlow makes it easy for beginners and experts to develop machine learning projects.
Anaconda, an open-source distribution of the Python programming languages for scientific computing, will be concisely described. Anaconda has been built by data scientists, for data scientists. More than 20 million people around the world use it to solve the toughest problems. One key benefit of using Anaconda in the beginning phase of studying deep learning is that will simplify package management of TensorFlow.
Suitable audience for this talk is anyone who has little background knowledge about computer science and programing skills and has the interesting to have a peep into the world of deep learning.
Introduction:
JOHN (RUNWEI) CHENG is a research scientist with extensive industry experience in high-performance computing on heterogeneous computing platforms. Before joining the oil and gas industry, John worked in the finance industry for more than ten years as an expert in computational intelligence, providing advanced solutions based on genetic algorithms hybridized with data mining and statistical learning to solve real world business challenges. His recent book, entitled Professional CUDA C Programming, published by Wiley in 2014, has been ranked as the top one among 41 best parallel computing books of all time. As an internationally recognized researcher in the field of genetic algorithms and their application to industrial engineering, John has co-authored three books. John’s first book, Genetic Algorithms and Engineering Design, published by Wiley in 1997, is still used as a textbook in universities worldwide. John has a wide range of experience in both academic research and industry development, and is gifted in making complex subjects accessible to readers with a concise, illustrative, and edifying approach. John earned his doctoral degree in computational intelligence from the Tokyo Institute of Technology.
