A quiet, friendly, yet powerful AI diligently working behind the scenes to help people do whatever they do better is so much more comfortable and approachable for people, compare to a robotic killing machine in a SciFi movie.
So if I had to summarize the secret to a great ML project in one sentence, it would be: Build a project with an interesting dataset that took obvious effort to collect, and make it as visually impactful as possible.
Recommendation: A concise and straightforward way to get up to speed with modern ML tooling and architectures would be to take both of the Fast.ai courses. You’ll get up to speed with powerful frameworks and already build some interesting, fundamental mod…
…e GoDaddy, HERE, and GoGo, data scientists solve problems by applying machine learning on big data. Some examples are: predicting customers’ probability to cancel subscription, identifying data anomalies, computing ad-hoc analysis on gigabytes or terabytes of data, clustering customers into meaningful groups, text analytics to find topics in customer chat transcripts, calculating revenue projection, and the list does not end.
…like image/text classification, tabular data, collaborative filtering, etc.), it does it very well. It is not as extensive as Keras, but it’s very sharp and focused. Kind of like Vim and Emacs if you are familiar with the command line text editor war. 😜
concept is a l…ep with multiple methods/functions to adapt to different types of data and the ways data is stored. This concept is a lot like the Linux philosophy, highly modulized and with each module only do one thing but really really well. You are free to explore the wonderful API here, for the above code though, it does the following th…
I’ll leave it to you guys to contemplate what it means to humanity. When an OpenAI model agent running within a Boston Dynamics robot or killer drones, and video surveillance networks everywhere to watch your every step, if you are the hider playing this game, what is the chance of you winning?
This simple technique is also called ‘Progressive resizing’ by Jeremy Howard from fast.ai and helped his team beat Google in a competition of speed training IMAGENET in DAWNBench by training the IMAGGNET in a whopping18 minutes and $40 Amazon AWS cost.