> If my laptop doesn't have a GPU, can I still do all exercises? To answer this, while technically you could do the exercises, it is possible that it will take

> If my laptop doesn't have a GPU, can I still do all exercises? To answer this, while technically you could do the exercises, it is possible that it will take longer (perhaps much longer) to train models and whatnot without a GPU. Where you'll really have a hard time will be anything for deep learning and large neural networks. What might take a few seconds or minutes to train on a GPU might take hours with just a CPU with a few cores. But not to worry - in the course it explains that you can use a variety of "cloud based" GPU systems that are fairly cheap to access (provided you have a credit card). So if you can't get access to a GPU, there are other ways that won't put you in the poorhouse (just make absolutely sure to completely shutdown and/or delete your VM instances you spin up). Also - while you could use another language, it is best to stick with Python for now, because it has become almost the defacto language for ML and Deep Learning, at least as far as for education and such. Mainly because it has such great libraries for scientific computing available, plus it is easily approachable. It also has a great interface to Tensorflow (and ultimately CUDA and NVidia GPUs - also, get used to the fact that if you do anything DL related, you will be using NVidia almost exclusively; while AMD and others have their own DL hardware, it isn't nearly as well supported - maybe it will be better supported in the future). That said - Tensorflow does have decent support in a number of languages, especially C++ - so if you are more familiar with that language, you'll be set to do some amazing things once you complete the course. But Python, again, is really where it's at. Tensorflow is a great library that abstracts away a lot of the pain in dealing with CUDA. But even it has pain - and there are other libraries layered over it that make things even easier to work with (ie - Keras). Seriously; in the training I've had with DL (mainly thru Udacity), we had to learn how to build a simplified DL "library" of methods for building simple neural networks. Once you learn about Tensorflow, you will have an epiphany of "OMG so much easier!". Then - when you learn about Keras, it's yet another epiphany - it makes Tensorflow look "difficult". Of course, it's best to learn in this manner if you can, so that you understand how the lower levels work - instead of everything being a black-box. Of course, at some level there will be "black-boxes"; for instance, you may or may not want to learn how Tensorflow interfaces with CUDA. Or how CUDA interfaces with the GPU. Or certain other methods in Tensorflow or some other library that perhaps wraps certain methods and functionality. Honestly, things are much nicer today than they were even 5 years ago. You can decide just what level of depth you want to explore down to.