Still Waters Run Deep Learning

Digging Deeper He shovels well, he shovels VERY well. ( Wikipedia link , IMDB link ) As I mentioned in the last blog ( In Which Instructions were Sought, Part 2 , link ), I wanted to get more experience in some of the modern frameworks which are commonly used nowadays. The deep learning specialization covered the theoretical background well, and the assignments involved implementing the algorithms directly, which helped solidify understanding, but they did not go in depth into any of the frameworks, which means that in many production environments where frameworks are used to speed up implementation, I would have no relevant experience with the use of those frameworks. Having enjoyed the Coursera Deep Learning Specialization offered by deeplearning.ai, I wanted to take another course on the Coursera platform. I found two specializations which interested me: TensorFlow in Practice Specialization, offered by deeplearning.ai ( Coursera link )         Du...

A Fire Under My Posterior I confess, I lied a little in the last blog post. (Here it is, In Which Instructions were Sought, Part 1 ( link ).) Take a look. In that blog entry I talked about the kinds of deep learning courses available online, and which ones looked promising. I had said that the listing of courses I had chosen had no particular order. Actually, it did. It was the order of my preferences for which classes to take. I wanted to take a class with a fee, to both motivate myself and to have proof that I finished it, but didn't want to go the path of the Udacity course with the high price without knowing that it had a significant better return on investment than the Coursera course. I also wanted an easy-to-access community of fellow students to discuss questions and issues. Thus the ranking of Coursea -> fast.ai -> Stanford videos -> Udacity. So I signed up for the Coursera deep learning specialization. Coursera Deep Learning Specialization Review ...

"Give me six hours to chop down a tree and I will spend the first four sharpening the axe." - Abraham Lincoln Now that I have my machine ready, it is time to dive into the actual study of deep learning.  (Please read my previous blog entries,  In Which a Hardware Platform was Chosen, Part 1 ( link ) and  Part 2  ( link ), where I talked about the decision between cloud and personal device, and between desktop and laptop computers, and the specs of the computer I will use for my study of deep learning.) Nose in a Book... or Screen Do birds even have a sense of smell? ( Wikipedia link ) Nowadays there are so many resources online and offline that you can find instructional material in a plethora of formats, whether you prefer prerecorded videos, live videos, in-person classes, online courses, e-books, dead tree books, audiobooks, etc. It's all out there. What would work best would depend on your preferences, and the way that you absorb material most na...

How Much Computer Do You Need? I buy computers based on best price per pound. This is part two of two posts about how I decided on what kind of computing platform to use for my deep learning studies. If you haven't read it yet, the previous blog was   In Which a Hardware Platform was Chosen, Part 1  ( link ), where I talked about the decision between cloud and personal device, and between desktop and laptop computers. Laptops comes in many shapes and sizes nowadays, with different trade-offs in each design. Due to the GPU requirement, tiny laptops such as ultrabooks are out of the picture, as they do not have the space or the heat dissipation capacity of larger laptops. As this laptop is going to be my desktop replacement and my main computer, I wanted as much of a behemeth of a machine as possible, with the biggest screen and the highest specs available for a CPU and GPU that is within a reasonable price point. The largest laptops in production have 17" screens...