Originally published here: https://www.notion.so/lankinen/2021-My-working-methods-458bba133c49484ab371b648b873ff6c I work the best when everything is in order and I don’t need to waste time middle of a task to e.g. find certain notes or files. Over 2 years I have been focusing the way I work, I have learned a few things and took…

This article is about how I tested a hypothesis I had by building a website in 3 days. It’s not my first time building something to test business hypothesis but this time I wanted to do it faster and document the processes. Hypothesis The hypothesis is that people enjoy commenting 💩…

fastai/course-v4 fastai/fastbook High school math is enough to understand deep learning. Lots of data is not needed but some record-breaking results have been made with <50 items. You don’t need an expensive computer but start of the art results can be made for free. Deep learning is the same as…

fastai/fastbook Language model = a model that tries to predict the next word of a sentence. A language model works well on transfer learning as the base model because it knows something about language as it can predict the next word of a sentence. The base language model should be…

fastai/fastbook Weight decay (L2 regularization) The idea is to add the sum of all the weights squared to the loss function. This way the model tries to keep the weights as small as possible because bigger weights will increase the final loss. loss_with_wd = loss + wd * (parameters**2).sum() It’s…

fastai/fastbook learn = cnn_learner(dls, resnet34, metrics=error_rate) learn.fine_tune(2, base_lr=0.1) Learning rate finder helps to pick the best learning rate. The idea is to change the learning rate after every mini-batch and then plot the loss. Good learning rate is somewhere between the steepest point and the minimum. So for example based…

fastai/fastbook Create dataset train_x = torch.cat([stacked_threes, stacked_sevens]).view(-1, 28*28) train_y = tensor([1] * len(threes) + [0] * len(sevens)).unsqueeze(1) print(train_x.shape, train_y.shape) CONSOLE: (torch.Size([12396, 784]), torch.Size([12396, 1])) dset = list(zip(train_x, train_y)) x, y = dset[0] print(x.shape, y) PRINT: (torch.Size([784]), tensor([1])) Create weights def init_params(size, variance=1.0): return (torch.randn(size)*variance).requires_grad_()

fastai/fastbook # Load saved learner learn_inf = load_learner(path/'export.pkl') # Predict given image learn_inf.predict('images/grizzly.jpg') # See labels learn_inf.dls.vocab Jupyter Notebook widgets are created using IPython widgets. Deploying CPU is easier than GPU in most of the cases. GPU is needed only if the deployed model still requires a lot of computation…