import React from 'react' import { NBLink, ImgWrap, Img, NotebookContainer, NotebookWrapper, NotebookRow, Column1, Column2, TextWrapper, TopLine, Heading, Subtitle} from './NotebookElements' // export const homeObjOne = { // id: 'about', // lightBg: false, // lightText: true, // lightTextDesc: true, // topLine: 'Premium Bank', // headline: 'Unlimited transactions with zero fees', // description: // 'Get access to our exclusive app that allows you to send unlimited transactions without getting charged any fees', // buttonLabel: 'Get Started', // imgStart: false, // img: require('../../images/svg-1.svg').default, // alt: 'Car', // dark: true, // primary: true, // darkText: false // }; import img from '../../images/coding.svg' const NotebookSection = () => { return ( <> Coding exercises Python notebooks covering the whole text Sixty eight python notebook exercises with missing code to fill in based on the text Car
  • Notebook 1.1 - Background mathematics: ipynb/colab
  • Notebook 2.1 - Supervised learning: ipynb/colab
  • Notebook 3.1 - Shallow networks I: ipynb/colab
  • Notebook 3.2 - Shallow networks II: ipynb/colab
  • Notebook 3.3 - Shallow network regions: ipynb/colab
  • Notebook 3.4 - Activation functions: ipynb/colab
  • Notebook 4.1 - Composing networks: ipynb/colab
  • Notebook 4.2 - Clipping functions: ipynb/colab
  • Notebook 4.3 - Deep networks: ipynb/colab
  • Notebook 5.1 - Least squares loss: ipynb/colab
  • Notebook 5.2 - Binary cross-entropy loss: ipynb/colab
  • Notebook 5.3 - Multiclass cross-entropy loss: ipynb/colab
  • Notebook 6.1 - Line search: ipynb/colab
  • Notebook 6.2 - Gradient descent: ipynb/colab
  • Notebook 6.3 - Stochastic gradient descent: ipynb/colab
  • Notebook 6.4 - Momentum: ipynb/colab
  • Notebook 6.5 - Adam: ipynb/colab
  • Notebook 7.1 - Backpropagation in toy model: ipynb/colab
  • Notebook 7.2 - Backpropagation: ipynb/colab
  • Notebook 7.3 - Initialization: ipynb/colab
  • Notebook 8.1 - MNIST-1D performance: ipynb/colab
  • Notebook 8.2 - Bias-variance trade-off: ipynb/colab
  • Notebook 8.3 - Double descent: ipynb/colab
  • Notebook 8.4 - High-dimensional spaces: ipynb/colab
  • Notebook 9.1 - L2 regularization: ipynb/colab
  • Notebook 9.2 - Implicit regularization: ipynb/colab
  • Notebook 9.3 - Ensembling: ipynb/colab
  • Notebook 9.4 - Bayesian approach: ipynb/colab
  • Notebook 9.5 - Augmentation ipynb/colab
  • Notebook 10.1 - 1D convolution: ipynb/colab
  • Notebook 10.2 - Convolution for MNIST-1D: ipynb/colab
  • Notebook 10.3 - 2D convolution: ipynb/colab
  • Notebook 10.4 - Downsampling & upsampling: ipynb/colab
  • Notebook 10.5 - Convolution for MNIST: ipynb/colab
  • Notebook 11.1 - Shattered gradients: ipynb/colab
  • Notebook 11.2 - Residual networks: ipynb/colab
  • Notebook 11.3 - Batch normalization: ipynb/colab
  • Notebook 12.1 - Self-attention: ipynb/colab
  • Notebook 12.2 - Multi-head self-attention: ipynb/colab
  • Notebook 12.3 - Tokenization: ipynb/colab
  • Notebook 12.4 - Decoding strategies: ipynb/colab
  • Notebook 13.1 - Encoding graphs: ipynb/colab
  • Notebook 13.2 - Graph classification : ipynb/colab
  • Notebook 13.3 - Neighborhood sampling: ipynb/colab
  • Notebook 13.4 - Graph attention: ipynb/colab
  • Notebook 15.1 - GAN toy example: ipynb/colab
  • Notebook 15.2 - Wasserstein distance: ipynb/colab
  • Notebook 16.1 - 1D normalizing flows: ipynb/colab
  • Notebook 16.2 - Autoregressive flows: ipynb/colab
  • Notebook 16.3 - Contraction mappings: ipynb/colab
  • Notebook 17.1 - Latent variable models: ipynb/colab
  • Notebook 17.2 - Reparameterization trick: ipynb/colab
  • Notebook 17.3 - Importance sampling: ipynb/colab
  • Notebook 18.1 - Diffusion encoder: ipynb/colab
  • Notebook 18.2 - 1D diffusion model: ipynb/colab
  • Notebook 18.3 - Reparameterized model: ipynb/colab
  • Notebook 18.4 - Families of diffusion models: ipynb/colab
  • Notebook 19.1 - Markov decision processes: ipynb/colab
  • Notebook 19.2 - Dynamic programming: ipynb/colab
  • Notebook 19.3 - Monte-Carlo methods: ipynb/colab
  • Notebook 19.4 - Temporal difference methods: ipynb/colab
  • Notebook 19.5 - Control variates: ipynb/colab
  • Notebook 20.1 - Random data: ipynb/colab
  • Notebook 20.2 - Full-batch gradient descent: ipynb/colab
  • Notebook 20.3 - Lottery tickets: ipynb/colab
  • Notebook 20.4 - Adversarial attacks: ipynb/colab
  • Notebook 21.1 - Bias mitigation: ipynb/colab
  • Notebook 21.2 - Explainability: ipynb/colab
) } export default NotebookSection