My name is Dimitris Poulopoulos, and I’m a machine learning engineer working for Arrikto. I have designed and implemente

The first video was a video that I already watched, and I highly recommend that it. It does a great job of explaining how recommendation systems work intuitively. Then, I’ll go through the other two links and see where it takes me!

I already know what resources I plan on using when I want to dive into deep learning because I’ve had my eyes on these resources for a while. To get an intuitive understanding of neural networks, I plan on watching the first two YouTube videos. Then, I plan on going through the two courses that follow.

Below, I’ve identified five areas that I believe will help me become ten times more valuable than right now. For each area, I’ve explained why I’ve decided to dedicate my time learning it and the resources that I plan on using to help me.

I’ve also noticed that deep learning is becoming a bigger focus throughout the community. An increasing proportion of Kaggle competitions require deep learning, more job postings require experience in deep learning, and more articles are about deep learning!

Let’s be real. 2020 wasn’t the best year. So why not spend the remainder of this year to plan for the next? As 2021 approaches, I wanted to plan ahead and think about what I want to learn next year to make myself a better data scientist.

Similar to my reasons for deep learning, time series modelling is one of the most practical data science/statistical concepts, but I also find it one of the most difficult concepts to wrap my head around completely.

I want to learn it because I’ve noticed that a lot of problems at my work can be solved with time series modelling. Similarly, there are a lot of personal projects that I would like to complete which require knowledge in time series modelling.

More importantly, recommendation systems are revenue-generating. Amazon’s 29% increase in sales in 2015 was largely attributed to its recommendation systems all over its website. Also, a little bird told me that Faire, a company like Amazon, became profitable the year after it introduced recommendation systems.

It’s increasing in relevancy. Deep learning has significantly improved over the past few years — the techniques in deep learning have been refined, the efficiency of deep learning algorithms have improved, and there’s a lot more support online around deep learning (more open source projects and more educational resources).

I am still very much a novice in this area (I must admit!), but I believe there are several tools that can be used to deploy models like Docker, Heroku, Streamlit, and Airflow so I want to explore the different options and compare where they see fit.

It’s becoming increasingly important that data scientists not only know how to build models but how to deploy them as well. In fact, a lot of job postings are now requiring some experience in model deployment.

Similar to how I started learning machine learning, I don’t know what I don’t know. Therefore, I’m going to initially be skimming through a lot of different resources. I’ll most likely be starting with Docker, as it seems to be the most widely used for deployment.

The reason that it’s so important to learn how to deploy models is that a model is only as valuable as its ease of use and ease of interpretability. And so, being able to deploy it in a way that anyone can interact with will be extremely useful.

My name is Dimitris Poulopoulos, and I’m a machine learning engineer working for Arrikto. I have designed and implemented AI and software solutions for major clients such as the European Commission, Eurostat, IMF, the European Central Bank, OECD, and IKEA.

http://old.cocir.org/media/fxa/videos-cuiaba-v-juventude-v-pt-br-1acx2-4.php

http://skrs.vidrio.org/dod/video-cuiaba-v-juventude-v-pt-br-1jof2-12.php

http://stream88.colomboserboli.com/eca/videos-cuiaba-v-juventude-v-pt-br-1vcu2-3.php

http://old.cocir.org/media/fxa/videos-cuiaba-v-juventude-v-pt-br-1dht2-16.php

http://svt.munich.es/fmi/video-cuiaba-v-juventude-v-pt-br-1tsb2-6.php

http://vert.actiup.com/jls/videos-cuiaba-v-juventude-v-pt-br-1flb2-3.php

http://agro.ruicasa.com/vtm/Video-cuiaba-v-juventude-v-pt-br-1gmw2-21.php

http://m.dentisalut.com/qux/video-cuiaba-v-juventude-v-pt-br-1ohw2-6.php

http://skrs.vidrio.org/dod/video-cuiaba-v-juventude-v-pt-br-1dvr2-21.php

http://stream88.colomboserboli.com/eca/videos-cuiaba-v-juventude-v-pt-br-1ijs2-12.php

http://m.dentisalut.com/qux/Video-cuiaba-v-juventude-v-pt-br-1wjq2-26.php

http://agro.ruicasa.com/vtm/video-cuiaba-v-juventude-v-pt-br-1eqk2-4.php

http://vert.actiup.com/jls/videos-cuiaba-v-juventude-v-pt-br-1wbz2-19.php

http://skrs.vidrio.org/dod/video-cuiaba-v-juventude-v-pt-br-1eon2-24.php

http://svt.munich.es/fmi/video-cuiaba-v-juventude-v-pt-br-1gsu2-6.php

http://old.cocir.org/media/fxa/Video-cuiaba-v-juventude-v-pt-br-1poe2-6.php

http://m.dentisalut.com/qux/videos-Cuiaba-Juventude-v-en-gb-1eyk-.php

http://skrs.vidrio.org/dod/video-Cuiaba-Juventude-v-en-gb-1uql-.php

http://stream88.colomboserboli.com/eca/video-Cuiaba-Juventude-v-en-gb-1dzq-.php

http://agro.ruicasa.com/vtm/video-Cuiaba-Juventude-v-en-gb-1jtj-.php

one of the things I love about Python is its open-source principles. There is a wide community of professionals who openly share their ideas and groundwork, help others, and create some great projects together. This idea was confusing for me at first as for a person from the traditional corporate world, but later I have realized its enormous potential. That is why I am trying to motivate people to learn Python and join this great community.