I bought an iPhone 15 the other day to replace my five-year-old iPhone 11. The phone is powered by the new A17 Pro chip and has a terabyte of data storage and accordingly was eye-wateringly expensive. I had, of course, finely honed rationales for splashing out on such a scale. I've always had a policy of writing only about kit that I buy with my own money (no freebies from tech companies), for example. The fancy A17 processor is needed to run the new "AI" stuff that Apple is promising to launch soon; the phone has a significantly better camera than my old handset had – which matters (to me) because my Substack blog goes out three times a week and I provide a new photograph for each edition; and, finally, a friend whose ancient iPhone is on its last legs might appreciate an iPhone 11 in good nick.

If you are training a binary classifier, chances are you are using binary cross-entropy / log loss as your loss function. Have you ever thought about what exactly does it mean to use this loss function? The thing is, given the ease of use of today's libraries and frameworks, it is very easy to overlook the true meaning of the loss function used. I was looking for a blog post that would explain the concepts behind binary cross-entropy / log loss in a visually clear and concise manner, so I could show it to my students at Data Science Retreat. Let's start with 10 random points: This is our only feature: x.

If you are training a binary classifier, chances are you are using binary cross-entropy / log loss as your loss function. Have you ever thought about what exactly does it mean to use this loss function? The thing is, given the ease of use of today's libraries and frameworks, it is very easy to overlook the true meaning of the loss function used. I was looking for a blog post that would explain the concepts behind binary cross-entropy / log loss in a visually clear and concise manner, so I could show it to my students at Data Science Retreat. Let's start with 10 random points: This is our only feature: x.

As part of my quest to learn about AI, I set myself the goal of building a simple neural network in Python. To ensure I truly understand it, I had to build it from scratch without using a neural network library. Thanks to an excellent blog post by Andrew Trask I achieved my goal. In this blog post, I'll explain how I did it, so you can build your own. I'll also provide a longer, but more beautiful version of the source code.

My eye was caught by a headline in the Register, an invaluable online source of tech news and opinion. "Clearance sale shows Apple's iPad is over. This was a quotation from a piece by Volker Weber on the latest product announcements from Apple. "iPad is the biggest news," he wrote, "and it says: the iPad is done. Apple is just refining the components, but there isn't much they can do these days to make yet another super-duper Earth-shattering innovation here."

As part of my quest to learn about AI, I set myself the goal of building a simple neural network in Python. To ensure I truly understand it, I had to build it from scratch without using a neural network library. Thanks to an excellent blog post by Andrew Trask I achieved my goal. In this blog post, I'll explain how I did it, so you can build your own. I'll also provide a longer, but more beautiful version of the source code.