Decebal on programming

Serverless Days - London - My Key Takeaways Twitter: https://twitter.com/serverlessldn Official Announcement: https://london.serverlessdays.io/news/keynote-speakers-2019/ Table of contents: The what, why and who of the event Followed Agenda My Key TakeAways from the event The what, why and who of the event What: Serverless Days in London I found to be a great opportunity to see what other people are doing with the serverless technologies out there and what is the state of this space, in particular what is the state of the conference space and what level of maturity the community has in London. [Read More]

What Kind Of Data Scientist Do You Want To Be? Video: https://youtu.be/QB2r06mpI0I According to this video: > There are several data scientist archetypes: - The Detective, a master of analysis, - The Oracle, a master of modeling, - The Maker, a master of engineering, and - The Generalist, proficient at everything. I liked the fact that they offered insights on the niches of Machine Learning inside a company. Looking forward I was so tempted to set myself the goal of becoming a Generalist without realising how good an Oracle I am. [Read More]

Resources explored 7 Companies Using Blockchain To Power AI Applications More explicitly this looks at 7 companies exploring this new frontier at the intersection of blockchain, AI, and data marketplaces. I love what most of these companies propose, although at the moment their selling point might be on the part of exposing data that wouldn’t normally be exposed. I guess this is where my limitations are at the moment. [Read More]

Resources explored Smaller Collaborative Robots Are Disrupting The Robotics Industry I love the reports from www.cbinsights.com, their research does more than scratch the surface and often offers myself a way to link up ideas with the state of the market. As it stands I have no partnership with them, I just consider them an awesome resource. Warning, exponentially difficult business idea: Personally after reading the report I started dreaming of a personal cook robot with an AI that can adjust recipes based on my feedback on food and tailor it differently for my wife or my future children. [Read More]

Today I have introduced a new section to each of the past ones: My Impressions inspired by Jim Kwik: Speed Reading, Memory, & Superlearning https://youtu.be/t9setjA0AT8?t=13m17s Capture Notes | Creating Notes Creating Notes => Questions that you would have, how would you be able to use it, how it relates to things you already know. Dare to DayDream! Classic Read Resource: https://youtu.be/qGl2XsuZHWY My one golden nugget: teaching can unlock the passion within instead of trying to rewrite everything from scratch while allowing for politics. [Read More]

Resources explored Getting Started with Weka - Machine Learning Recipes #10 Weka looks a tool to try, but I am not the biggest fan of it’s UI. I comes with datasets, pretty useful. other recipes from Google can be found in this playlist A Complete Machine Learning Project Walk-Through in Python: Part One awesome guide for this challenge equally awesome for beginners trying to find their steps into this i am looking forward to build on this process you have to look at the whole series: [Read More]

Resources explored For people whom need a little more structure I looked into some courses, here’s what I found so far: Practical Convolutional Neural Networks: The Course Overview | packtpub.com https://youtu.be/EgKQeuwmTEk Looks amazingly useful for beginners Love the examples Authors seem well versed on the subject TensorFlow 1.x Deep Learning Recipes for Artificial Intelligence App: Course Overview | packtpub.com https://youtu.be/RMILgtLeNVc Dives into Deep Learning and generally goes a step further that the previous course The recipes for TensorFlow are my personal favourite Driven Data Competitions [Read More]

Resources explored Streaming Project Requests This page is an amazing starting point for ideas on what to tackle next. Up for grabs - repositories in machine learning looking for contributors openly At the time of publishing there are only 3 such repositories “up for grabs”: Dive into Machine Learning - quite a big educational resource starting from beginner level EvalAI - part of https://cloudcv.org/ , has a nice collection of ongoing challenges at the moment. [Read More]

After last day I decided to continue researching for 15 days more, before diving into building a real-world app. I already have a list of rough app ideas, but I expect more ideas to come to surface when going through these amazing resources out there. Resources explored A Recap from TensorFlow Summit https://youtu.be/kV2by5VNBsA Speaker’s github profile contains a good quantity of workshops using TensorFlow, between them one Image Recognition workshop seems interesting to study. [Read More]

Resources explored on third day Top 8 open source AI technologies in machine learning The list of 8 libraries worth looking at before anything else: - TensorFlow - Keras - Scikit-learn - Microsoft Cognitive Toolkit - Theano - Caffe = Convolutional Architecture for Fast Feature Embedding - Torch - Accord.Net Machine Learning - a Github Collection A really good source of training data sets and classified algorithms to explore. [Read More]