Day 10 of Machine Learning in Review

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]

Day 9 of Machine Learning in Review

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]

Day 8 of Machine Learning in Review

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]

Day 7 of Machine Learning in Review

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]

Day 6 of Machine Learning in Review

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]

Day Five of Machine Learning in Review

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]

Day Four of Machine Learning in Review

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]

Day Three of Machine Learning in Review

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]

Day Two of Machine Learning in Review

Resources explored on second day Machine learning needs machine teaching > Machine teaching and machine learning are necessary complements to one another; you need both. And for the large part, most of what comprises machine teaching these days consists of giant label data sets. Common use cases for reinforcement learning: [..] tuning Machine Teaching is the new Programming How machine learning can be used to write more secure computer programs > As an example, when you find a vulnerability in code, the question that often arises is whether there are similar vulnerabilities still in that same program. [Read More]

Day 1 of Machine Learning in Review

First and foremost I felt I need to do this with a purpose, an end goal. I would love to be able to diagnose profanity from uploaded pictures at the end of this, but it feels somewhat limiting to start with this one so I decided to explore goals that I can set at the end of my first week into this within an industry or cause. Hopefully I will have some more clarity on how I can contribute towards bringing “The Rising Billion” (Reference from “Abundance”) closer to western living standards. [Read More]