Machine Learning Art
Make a machine learning project
I was an average student, and I had always assumed it would be challenging to find a job in my field because I didn’t have the correct number of mathematics classes. However, I discovered that it really isn’t any harder to find work as a Data Science than as something else. With this realization, I was spurred on to improve my skills.
🟠 10 things I wish I knew before trying to use machine learning
My name is Eva, and I’m going to tell you how my life drastically changed for the better, thanks to the Mlearning.ai internship. I feel like I owe all my ML understanding to Mlearning.ai because they found me and gave me a great opportunity that I never even thought existed, but here goes:
Data scientists are hired for their problem-solving creativity, not just the ability to use ML. Unfortunately, the university’s performance as a teacher of machine learning is terrible. Out-of-date information, and they focus on the least important aspects.
I couldn’t find an article on hands-on machine learning, and I know a lot about it because I’ve been working in the field for almost three years. So during the internship at MLearning.ai, I opened my eyes and wrote an article that sums up the most significant milestone I have made in recent years.
read it here 🔵 things everyone in the AI industry expects you to know
At the beginning of my career, I was making many mistakes in modeling, and I was confused by this. Now that I’m an expert, I can see what they were doing wrong. You should read this to avoid making the same mistakes everyone else makes.
10 Things I Wish I Knew About ML Before Really Trying To Use It
1. ML cannot exist without context
2. Evaluate the model, then create it
3. You’re doing it wrong if you don’t use the chain rule when doing backpropagation.
4. LSTMs are not a silver bullet. Nobody cares if you apply an LSTM to a task that…
I find it interesting that many people are saying that machine learning is not a very good way to do things, and then they will say how great it is. They will say how much better humans are at music or language and say that we’re promising for AI, but we’re not there yet. The question is: how good are humans at machine learning? How good are algorithms? The answer is, “We’ll never know.” This can cause a lot of problems for the industry…
I am an Art Curator, founder at EvArtology. I advise companies and institutions in the creative industries on using AI tools in their daily work. Human collaboration with ML models can be very creative and bring huge benefits. The new era begins now.
Data Scientists must think like an artist when finding a solution when creating a piece of code. Artists enjoy working on interesting problems, even if there is no obvious answer.
All our writers (members) receive the opportunity to be promoted on our social media, which increases the popularity of articles published on MLearning.ai
