![]() Note: When dealing with real-world data, this step is typically done right at the start of a project (the test set should always be kept separate from all other data). We can create them by splitting our X and y tensors. The model gets evaluated on this data to test what it has learned (like the final exam you take at the end of the semester).įor now, we'll just use a training and test set, this means we'll have a dataset for our model to learn on as well as be evaluated on. The model gets tuned on this data (like the practice exam you take before the final exam). ![]() The model learns from this data (like the course materials you study during the semester). One of most important steps in a machine learning project is creating a training and test set (and when required, a validation set).Įach split of the dataset serves a specific purpose: Split We'll use linear regression to create the data with known parameters (things that can be learned by a model) and then we'll use PyTorch to see if we can build model to estimate these parameters using gradient descent.ĭon't worry if the terms above don't mean much now, we'll see them in action and I'll put extra resources below where you can learn more.īut before we build a model we need to split it up. Let's create our data as a straight line. Sometimes one and two can be done at the same time. Pick or build a model to learn the representation as best as possible.Turn your data, whatever it is, into numbers (a representation).A table of numbers (like a big Excel spreadsheet), images of any kind, videos (YouTube has lots of data!), audio files like songs or podcasts, protein structures, text and more. ![]() I want to stress that "data" in machine learning can be almost anything you can imagine. Let's take all of the above and combine it. You may want to use your model elsewhere, or come back to it later, here we'll cover that. Our model's found patterns in the data, let's compare its findings to the actual ( testing) data. Making predictions and evaluating a model (inference) We've got data and a model, now let's let the model (try to) find patterns in the ( training) data.Ĥ. Here we'll create a model to learn patterns in the data, we'll also choose a loss function, optimizer and build a training loop. Specifically, we're going to cover: Topicĭata can be almost anything but to get started we're going to create a simple straight line In this module we're going to cover a standard PyTorch workflow (it can be chopped and changed as necessary but it covers the main outline of steps).įor now, we'll use this workflow to predict a simple straight line but the workflow steps can be repeated and changed depending on the problem you're working on. Loading a saved PyTorch model's state_dict()Ġ2. Making predictions with a trained PyTorch model (inference) Making predictions using torch.inference_mode()Ĭreating a loss function and optimizer in PyTorchĤ.
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