📚Other Strategies

Other Strategies of Deep Learning

➰ Multi-Task Learning

In short: We start simultaneously trying to have one NN do several things at same time and then each of these tasks helps all of the other tasks 🚀

In other words: Let's say that we want to build a detector to detect 4 classes of objects, instead of building 4 NN for each class, we can build one NN to detect the four classes 🤔 (The output layer has 4 units)

🤔 When Is It Practical?

• 🤳 Training on a set of tasks that could benefit from having shared lower level features

• ⛱ Amount of data we have for each task is quite similar (sometimes) ⛱

• 🤗 Can train a big enough NN to do well on all the tasks (instead of building a separate network fır each task)

👓 Multi task learning is used much less than transfer learning

👀 Visualization

🏴 End to End Deep Learning

• Briefly, there have been some data processing systems or learning systems that requires multiple stages of processing,

• End to end learning can take all these multiple stages and replace it with just a single NN

👩‍🔧 Long Story Short: breaking the big task into sub smaller tasks with the same NN

➕ Pros:

• 🦸‍♀️ Shows the power of the data

• ✨ Less hand designing of components needed

➖ Cons:

• 💔 May need large amount of data

• 🔎 Excludes potentially useful hand designed components

🚩 Guideline to Make Decision to Use It

Key question: do you have sufficient data to learn a function of the complexity needed to map x to y?

🔃 End to End Learning vs Transfer Learning