Actually, I haven't tried this problem, but I had to write a proposal about this kind of task, and had to argue a lot with my CEO for my idea's feasibility.(though the point of the argument was not about the feasibility, but his misunderstanding of deep learning basics...)
hoping that after each submission, you climb a few steps in the public leaderboard. Technical minds, like software and ML engineers, love to build things. I include myself in this group. We do that even before we understand the problem we...
The one thing I do try to follow is to go on streaks of reading a lot of books on a particular topic around the same time. Doing this is useful because it means I don’t have to just trust one author’s perspective on a particular topic, and it helps me connect a lot of facts ...
For example, you may be solving the problem as a learning exercise. This is useful to clarify as you can decide that you don’t want to use the most suitable method to solve the problem, but instead you want to explore methods that you are not familiar with in order to learn new skil...
Unless you want to devote yourself to Ph.D research, that's way overkill. For most people, the self-starter approach is superior to the academic approach for 3 reasons: You'll have more fun.By cycling between theory, practice, and projects, you'll arrive at real results faster. This is...
2.1 Data Level approach: Resampling Techniques Dealing with imbalanced datasets entails strategies such as improving classification algorithms or balancing classes in the training data (data preprocessing) before providing the data as input to the machine learning algorithm. The later technique is preferred...
You might have a machine learning problem if: It's difficult or impossible to write down a set of rules, but It's easy to collect historical examples The parity problem fails on the first point. The rules are very simple: even if the number is divisible by 2, odd otherwise. There's ...
Hands-On Machine Learning with Scikit-Learn & Tensorflowis thought for beginners in Machine Learning, that are looking for a practical approach to learning by building projects and studying the different Machine Learning algorithms within a specific context. ...
The increasing use of AI in different societal contexts intensified the debate on risks, ethical problems and bias. Accordingly, promising research activities focus on debiasing to strengthen fairness, accountability and transparency in machine learning.
I like this approach because it defends the need to telescope in on a specific case of the algorithm from many possible cases at each step of the description while also leaving the option open for the description of variations. There are many descriptions you could use of varying specificity ...