It's a legitimately valid part of machine learning, and its not easy to do for novices.
And I need help putting it on my badger damn it!
If we're talking about a longer format, such as a book, then we might consider digging deeper and implementing as much as possible using the barest of Python requirements. Indeed, Joel Grus does implement everything from scratch in his great (although a bit dated) book https://www.amazon.com/Data-Science-Scratch-Principles-Pytho....
EDIT: This is still a work in progress (and relies on numpy and matplotlib), but here is my version: https://github.com/DataForScience/DeepLearning These notebooks are meant as support for a webinar so they might not be the clearest as standalone, but you also have the slides there.
But maybe it’s educational to do once if you never have before.
The problem is it's extremely hard to make it efficient. Dozens of men-years are spent trying to optimize linear algebra libraries. There are handful linalg libraries that have competitive performance. It was my college project to make a fast linalg library, and boy it is fast. There are some things like matrix multiplication that if you implement in C with the trivial algorithm, takes >2 mins but with some tricks you can make it as fast as <second (vectorization, OpenMP, handwritten assembly, automatically optimized code, various optimizations, better algorithm.....).
So, if you want to implement linalg in some language and compile it, go ahead, more power to you. But it's basically impossible to do it efficiently. My opinion is: this is fine and we should do this. There should be linalg libraries written in pure python (and are 1000x slower than lapack) but just understand that it's impossible to satisfy all use cases of numpy this way (at least currently).
That's the first step. If you have a 64kB cache, then you want to fill that cache ONCE, calculate everything you can with that 64kB chunk of data. Save off the result, and then load a new 64kB chunk. This is called "tiling".
Its actually kinda tricky to do just right, but once you know the concept, you basically spend effort ensuring that main-memory hits are minimized.
GPUs have many different memory regions: global Memory, L2, L1, "Shared" memory, and finally registers. Maximizing your math and minimizing your memory-moves is one big part of optimization.
There are a ton of other optimization tricks: GPUs are more efficient if they access memory in certain ways. "Shared Memory" in GPUs are typically banked (on modern NVidia and AMD GPUs).
If you have 64-threads, it is more efficient if thread#0 accesses X+0. Thread#1 accesses X+1. Thread#2 accesses X+2. (etc. etc.) Thread#63 accesses X+63. In fact, a GPU can perform all 64-memory loads simultaneously.
However, if the 64-threads all access memory location X+4, you have a "bank conflict". The 64-threads can only read this memory location one-at-a-time, resulting in a massive slowdown.
There are a huge number of tricks in "tricking" the for loops to access memory optimally across your many, many threads that are calculating the multiplication.
> Some kind of clever mathematical tricks?
The clever mathematical tricks have been solved and are known. LU decomposition, etc. etc. Its important to know them, but that's not generally what people mean by "optimization".
They happen on reads and writes.
The best way to describe it is... GPUs internally not only have tons of cores and threads (albeit "SIMD" threads, not true threads...)... they also have multiple memory banks.
I know AMD's architecture better. So let me talk about GCN. The smallest "cohesive" structure of an AMD GCN GPU is the execution unit. An execution unit has its own L1 data-cache, a 64kB "shared-memory" region, and finally the 256 vALUs (vector ALUs). There are 4-instruction pointers (64-simd threads per instruction pointer).
I'll focus on the high performance "64kB Shared Memory" region.
The 64kB shared memory is organized into 32-channels. In effect, channel 0 handles all addresses ending in XXXX00. Channel 1 handles all addresses ending in XXXX04. Channel 2 handles all addresses ending in XXXX08. Etc. etc. Channel 31 handles all addresses ending in XXXX7C.
Each channel can serve ONE thread per clock cycle. So if all 256-threads of the execution unit try to grab address 400000 (ending in 00, so channel 0), they all hammer channel 0. Channels 1 through 31 don't do anything, because no one asked for data from them. In this case, ONLY one channel is working, while 31-channels are sitting around doing nothing.
In this case, the last thread will be waiting for ~256 clock cycles, because its got 255 threads in front of it trying to grab data.
If you instead wrote your program so that all the threads asked for data "equally" between all 32-channels, then your code would be 32x faster (because all 32-channels would be working). Each channel has 8 requests (32x8 == 256 reads), and the 256 threads all get their data in just 8 clock cycles.
This isn't a problem in CPU world, because your L1 cache on Intel / AMD systems only has one channel per core. Actually, AMD offers TWO L1 channels per core (!!), and Intel offers 3x channels per core (2x reads + 1x write). So your one thread can do 2-reads + 1x write per clock tick.
If you have a massive 32-core CPU, you still have 2x reads + 1x write per core. So total of 64-reads + 32 writes across the whole system. This makes CPUs simple.
GPUs however, have a compromise. The 256-simd threads (or really, up to 2560-threads on an AMD system per EU) share the same 32 read/write channels.
Technically speaking, there are "channels" and "banks", two different memory organization schemes in a GPU. In practice, you can just pretend that only channels exist, because a bank conflict more or less acts the same as a channel conflict.
A lot of optimization is just knowing all of the special ways you can move memory around. Broadcast was common enough that they've given AMD GPUs a special instruction just for it.
So in the case of neural networks all reading from the same input, you'd want to do it through the broadcast instructions, instead of through shared memory. Shared memory would create bank conflicts.
Most approaches to doing matrix multiplication on the GPU benefit from doing operations in a way that plays off the behavior described above, make good use of caching, and respond to how the data in your matrix actually looks (e.g. is it sparse, etc).
To learn how you'd do matrix multiplication on the GPU, you might want to look up how its done via CUDA since many applications that make use of the GPU do so via CUDA and it doesn't require specific knowlege of graphics programming. This seems like a good introduction: https://www.shodor.org/media/content/petascale/materials/UPM...
After that, there are some caching optimizations to be had by iterating over the nested loops to minimize cache misses. Another optimization is to split up the matrices if their shapes are appropriate and use compounding matrix operations to recombine them, allowing for further use of parallelization in phases.
There are a few fancier algorithms out there which are optimized for certain assumptions -- extremely large matrices, several consecutive operations, matrices with many 0s or many identical entries or following certain patterns for values such as the identity matrix or eigenvectors, etc.
But to get within the same order of magnitude, tiling the workload for better cache utilization is usually the most important step. This article  explains it quite well and also lists a few other tricks.
In addition, there's also the fast Fourier transform for large filter kernels and Winograd convolutions  for small filter kernels.
IIRC his kernels shipped in cuBLAS at some point.
> (vectorization, OpenMP, handwritten assembly, automatically optimized code, various optimizations, better algorithm.....)
also in addition to these, I used caching. Also GPU programming, but it's a trade-off.
Perhaps it is overkill. It's just not actually from scratch without it, you know?
If you're not used to work with matrices simply reading the Wikipedia article might tell you enough to implement them yourself.
Or, just download a fast BLAS from your hardware vendor...
I'm a C# developer and I'm sure it would take me all of about 30 seconds to install a matrix multiplication package through nuget. I'm sure it would be immediately obvious how to add items to matrices or do a dot product.
It was dead easy to get code examples as needed.
The assignments are not directly available, but my email is easy to find.
So much learning that we're missing by not going through this step.
It uses Octave - but you first do everything (in the section on NN) "by hand" - building and looping for the matrix operations. Only after you've gone that far, does he (Ng) introduce the fact that Octave has vector/matrix primitives...
I took the original ML Class in the Fall of 2011; it was a great class, and opened my eyes a great deal on the topic of machine learning and neural networks, which I had struggled with understanding in the past (mainly on what and how backprop worked).
Here's an example of what one student of the ML Class built, after being inspired by what he was learning and videos that played during the course:
It kinda shocked me at the time, because I knew quite a bit about ALVINN from books and articles I had read as a teenager in the 80s and 90s. This guy had created the same thing using a cell phone and a cheap RC vehicle! Ok, there was also an Arduino and computer involved - but it really hit home the fact that technology around neural networks had advanced quite a bit!
I also took the other course, "AI Class", but due to personal issues I had to drop out about halfway through.
The next year, after Udacity started, they introduced a course similar to AI Class called "How to Build Your Own Self-Driving Vehicle" (it's called something else today - something like "Robotics and Artificial Intelligence 302" or something like that).
That class was done in Python, and taught me even more about AI/ML - with a focus towards self-driving vehicles of course. Things I learned about that I struggled with or had no real concepts of before:
1. SLAM (Simultaneous Localization and Mapping)
2. Path Finding algorithms (A* and the like)
3. Kalman Filtering (what it is for, how it works)
4. PID Algorithm (how to implement and tune it)
5. More neural network stuff...
...and many other things. Another very excellent and free course to take if you're interested in learning this stuff.
It was detailed enough for me to do all the calculations in an excel workbook, 1 complete cycle (forward, backward, and forward with the learned weights)
Which allowed me to play with the name and factors to understand better how they impact the network as a whole.
I found that I had to read a bunch of these things to really grasp them myself.