🌍 ♻️ We as populations create too much waste in the world that's immensely unsustainable. Although the real waste problem emerges from the nation and corporations, I believe we as individuals can do our part to reduce our waste contribution toward a more sustainable future.
🧠 Every once in a while, I challenge myself by engaging a design problem from where I have a deep passion. I'll create a concept product and write a case study detailing how I reached the final solution.
👉 Starting from today, I want to share with you my process from research, to UX and UI and how I'd reach a design solution for the problem of too much waste produced by societies. It’ll be a side project for my weekends and weekdays for when I’m available. .
(Note, being a digital product designer, the final solution will be digital. But this won't always be the smartest solution when trying to solving a problem.)
📐I share lots of tools and best practices here on IG and decided it could be a fun idea to show you how I engage with them and use them in my process. But know this: this isn't a course or anything like that. Just myself having some fun with solving a problem with the design and sharing that with you.
🌱 Let’s go have some fun.
2 days ago
A few days before my NLP exam and summer vacation 👌🏼🔥
🔹What is an AI Engineer?🔹 AI is a complex network of algorithms that think like a human brain. Programming and training AI takes time and expertise but not only for the work of deploying the models.
You must also screen for problems, handle system maintenance, and make improvements. Yes, you can find people to write algorithms,
but what you want is someone who can make decisions about the system itself. AI engineers build and test AI models. They must be able
to move between traditional software development and the unique needs of AI learning. They also navigate the learning spaces of their neural networks and the business value those networks provide.Once your organization moves to AI-driven initiatives, it will need someone to be the
point person for creating and evaluating those algorithms. Consequently, the position is a hybrid of data engineering, software development, and data science.
So, what’s the big difference from what you have on your team now? AI Engineers understand how to deploy machine learning.
AI engineers must: * be familiar with a variety of systems including cloud-native systems and chips.
* understand the principles of deep learning
* decide when models are ready to deploy and maintain them for accuracy (plus unintended consequences
21 hours ago
Today i worked on Lambdas in Python.
Tomorrow will go for Object Orientated Programming in Python.
Follow for future posts update.
This is so clever. Mona Lisa frown: Machine learning brings old paintings and photos to life https://buff.ly/2wD1A1w⠀
21 hours ago
Using a data-backed scientific approach to predict who will win Cricket World Cup 2019.
We present a predictive analysis model for 2019 men’s Cricket World Cup. We believe this predictive analysis strategy would be very useful for viewers, sponsors, and team strategists. This would also give insights to various cricket analysts and commentators about the features that play a crucial role in the statistical analysis. This model is developed based on the historical data collected for the 10 participating teams (Afghanistan, Australia, Bangladesh, England, India, New Zealand, Pakistan, South Africa, Sri Lanka, and West Indies). In addition, we test our model on 2015 world cup data and measure the accuracy of predictions. We are planning to expand this model as the tournament is close by and once the final squads are announced. This model is developed based on the players who were a part of their respective teams/squads in the recently 5 concluded tournaments.
To train our model, we utilize the data collected from every men’s cricket world cup. From 1975 to the present, there have been 11 world cups (1975, 1979, 1983, 1987, 1992, 1996, 1999, 2003, 2007, 2011 and 2015) played so far. One thing to be noticed is that until 1983 world cup, each team played 60 overs each whereas from 1987 onwards, 50 overs. Also, run scoring has increased incredibly over the last few years, that will be considered in our features as well.
Follow @theaistory for more.
Palestra sobre Customer
Pesquisa de Mercado com
Bento Müssnich do curso Digital Product Leadership da Tera. Muitos matériais para pesquisa sobre o assunto . Gerenciamento de produtos digitais é um mundo bem maior do que eu eu imaginava . Bem vindo a esse mundo ! Vamos aprender 😀