In the last video, we installed Python and created our first neural network from scratch. And while it is good to do at least once, from this point on, we're going to be using libraries to make it easier on us. I've decided to use the environment called Anaconda to do all of this. So that's what this episode is going to be about, just installing that, installing TensorFlow and Keras so we can be up and running for the next few episodes. OK, so to get started, all you need to do is download the Python 3.6 version, or if you prefer Python 2.7 version, but if you want to be following exactly, I would recommend doing 3.6. So once you've downloaded that, you'll have the installation file. So to get started, you just need to double click on it. It's fairly straightforward installation. Once it's open, just press Run. OK, so you're going to be going with the pretty straightforward example, just most of the recommended stuff. Choose where you want to download it to or install it. I will go into my Python folder, create a new subfolder called Anaconda. And that'll do. Next, you don't want to add that to your Python environment variable. OK, now that that's installed, we'll just continue on. So next, and finish. And then everything is installed, and we'll be moving on to the next part. OK, so all you want to do now is type up Anaconda, and it should bring up Anaconda Navigator. Just make sure it starts up. OK, so now that it's started up, the first thing we're going to do is go into environments. And actually, I think before we do that, I'll show you the environment that we're going to be working in. So it's called spider. OK, so this is the Navigator or the IDE. Actually, the spider IDE. It's running Python 3.6, so it's fully up to date. And this is where everything's going to be done. Similar to idle, you've got your code, you've got your. I mean, this is an I Python console, so it's a bit different. But it's just better and everybody pretty much. So now back to the actual installing part again, you just want to go back into the Navigator. And we're going to go into environments. And click on the root play button. Left click on it, right click, and press open terminal. OK, so first, what you're going to do is just type conda install TensorFlow. So conda is the package manager for an economy install, just means install and TensorFlow, which is what we're going to be installing. OK, now that's done. And for some reason, when you're installing carousel, it needs to install a sci-fi, which is the scientific library, I guess. And all it does is just cause an error. You can't install carousel with pip without installing sci-fi beforehand. And the only way I found it to that is just do conda install sci-fi. OK, for some reason, it didn't finish last time. So I'm going to try that again. So conda install. I'm going to install sci-fi. OK, so that's done. It was already installed. Now there's one more step you just need to do. PIP this time, which is just the default Python equivalent of conda. So pip install carousel. OK, so that's installed. Now let's just see if that works because it says it's installed with the anno, which is not all you want. We want it to instead install with TensorFlow. So let's just see if it has actually decided to use TensorFlow as the back end. And the way we do that is we go into spider. And we just do import. First we'll check if TensorFlow works. Actually, that might cause an error. So we'll just check if carous works. And put carousel. OK, so it is using the TensorFlow back end, which is exactly what we want. So that means that TensorFlow is installed correctly. And both also carousels installed correctly, which means that we are totally ready to go for the coming videos. I in another video later on, I might show you how to install the TensorFlow GPU. It's a bit of an annoying process, but it can be worthwhile. Thanks for watching. If you enjoyed this video, make sure to leave a like and check out some of my other videos. If you haven't already, subscribe and enable notifications so you can know whenever I upload. See you in the next video. Bye.