I experience a lot of collaborations during this semester, projects in the class as well as works at GTRI. Most of them works great, but one of them makes me really uncomfortable.I’m not here to complain but to summarize what I learned from this experience and how to avoid this in the future.

Always respect my teammates, people in the team may have different understanding on a specific field, whenever they have different opinion and even negate my thoughts, I should discuss with them until we are more like on the same page before this discussion if we are in a small team.

Keep everyone has this good chemistry within this team. Everyone should feels like one can always learn from each other instead of think other people are not as good as oneself.

Good Communication Skill is necessary to connect people’s mind.

Manage a team is not easy. It is an art involve everything.You expertise in the field, the way you organize the team as a whole, a little bit psychology, a little bit about perceiving people’s gesture and expression. It’s really hard for a manager to precisely manage a team.( What if we come up with some manage tools or software that can help us quantify this conveniently and precisely?)

When you feels not comfortable working with someone in the team, talk to him/her first to better understand what leads to the current situation. If it is really hard to solve, avoid further cooperation.

A team works as a whole share the same or relative similar vision, has the same working pace, respect each other.I mean this is the place inspires creativity and productivity. I hope in not long from now on, I can find this place.

Today I watch the talk Andrew Ng gave at GPU Technology Conference.

The important screenshots are: The migrate from cloud computing to HPC is because the synchronizability between computation or the Goal HPC do computation. The main difference between cloud computing and HPC is in cloud computing it tolerate the fault nodes in computation, and just start another node once some nodes in the cloud fails, this might suitable for some internet services like hosting the websites, but for computing the neural network, it is most of the time matrix computation and really needs every core is on the same page most of the time compared to the cloud’s asynchronous communication in the most of the time.

Obviously more GPU will do faster job but where is the sweetspot for the cost of GPUS as well as the cost of Time? I always believe the best way to learn is by doing it and think carefully what’s happening during the experiments.And find the best resource we could have to explain and solve the problem.In machine learning term is supervised learning for human, we should always try to get the feedback for what we’ve done, and try to talk to people and refer to technology in books and papers that can solve our current problems. The new promising deep speech structure: a BDRNN. Help neural network’s each recognizing unit better capture what is before and after itself.BTW I should remind myself here BDRNN is the one I should learn during this summer.