Dive into Deep Learning — Dive into Deep Learning 1.0.3 documentation

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• 7.6. Convolutional Neural Networks (LeNet) — Dive into Deep Learning 1.0.3 documentation

  Open the notebook in SageMaker Studio Lab We now have all the ingredients required to assemble a fully-functional CNN. In our earlier encounter with image data, we applied a linear model with softmax ...

• 23.8. The d2l API Document — Dive into Deep Learning 1.0.3 documentation

  Defines the computation performed at every call. Should be overridden by all subclasses. Note Although the recipe for forward pass needs to be defined within this function, one should call the Module...

• 8.2. Networks Using Blocks (VGG) — Dive into Deep Learning 1.0.3 documentation

  Open the notebook in SageMaker Studio Lab While AlexNet offered empirical evidence that deep CNNs can achieve good results, it did not provide a general template to guide subsequent researchers in des...

• 3.1. Linear Regression — Dive into Deep Learning 1.0.3 documentation

  real and predicted values of the target. The loss will usually be a nonnegative number where smaller values are better and perfect predictions incur a loss of 0. For regression problems, the most com...

• 1. Introduction — Dive into Deep Learning 1.0.3 documentation

  features (sometimes called covariates or inputs), based on which the model must make its predictions. In supervised learning problems, our goal is to predict the value of a special attribute, called...

• 3.4. Linear Regression Implementation from Scratch — Dive into Deep Learning 1.0.3 documentation

  %matplotlib inline from mxnet import autograd, np, npx from d2l import mxnet as d2l npx.set_np() %matplotlib inline import jax import optax from flax import linen as nn from jax import numpy as jnp ...