├── .github └── workflows │ └── quarto-render.yml ├── .gitignore ├── CondaPkg.toml ├── LICENSE ├── Manifest.toml ├── Project.toml ├── README.md ├── _extensions └── pat-alt │ ├── documenterjl │ └── _extension.yml │ └── julia │ ├── _extension.yml │ ├── custom.scss │ └── revealjs.scss ├── _freeze ├── chapters │ ├── c4 │ │ └── execute-results │ │ │ └── html.json │ └── c5 │ │ ├── execute-results │ │ └── html.json │ │ └── figure-html │ │ ├── cell-15-output-1.png │ │ ├── cell-26-output-1.svg │ │ ├── cell-27-output-1.png │ │ └── cell-27-output-1.svg └── site_libs │ └── clipboard │ └── clipboard.min.js ├── _quarto.yml ├── artifacts └── c5_resnet.jld2 ├── chapters ├── _metadata.yml ├── c4.qmd ├── c5.qmd └── images │ └── grizzly.jpg ├── contributors.qmd ├── docs ├── CNAME ├── chapters │ ├── c4.html │ ├── c5.html │ └── c5_files │ │ └── figure-html │ │ ├── cell-15-output-1.png │ │ ├── cell-26-output-1.svg │ │ ├── cell-27-output-1.png │ │ └── cell-27-output-1.svg ├── contributors.html ├── index.html ├── intro.html ├── logo.png ├── references.html ├── search.json ├── site_libs │ ├── bootstrap │ │ ├── bootstrap-icons.css │ │ ├── bootstrap-icons.woff │ │ ├── bootstrap.min.css │ │ └── bootstrap.min.js │ ├── clipboard │ │ └── clipboard.min.js │ ├── quarto-html │ │ ├── anchor.min.js │ │ ├── popper.min.js │ │ ├── quarto-syntax-highlighting.css │ │ ├── quarto.js │ │ ├── tippy.css │ │ └── tippy.umd.min.js │ ├── quarto-nav │ │ ├── headroom.min.js │ │ └── quarto-nav.js │ └── quarto-search │ │ ├── autocomplete.umd.js │ │ ├── fuse.min.js │ │ └── quarto-search.js ├── summary.html └── www │ ├── c4_gd.gif │ ├── c4_mnist.gif │ └── c5_lr.png ├── index.qmd ├── intro.qmd ├── logo.png ├── references.bib ├── references.qmd ├── setup_render.jl ├── summary.qmd └── www ├── c4_gd.gif ├── c4_mnist.gif └── c5_lr.png /.github/workflows/quarto-render.yml: -------------------------------------------------------------------------------- 1 | on: 2 | push: 3 | branches: main 4 | pull_request: 5 | 6 | name: Render 7 | jobs: 8 | build-deploy: 9 | runs-on: ubuntu-latest 10 | steps: 11 | - name: Check out repository 12 | uses: actions/checkout@v3 13 | - name: Set up Quarto 14 | uses: quarto-dev/quarto-actions/setup@v2 15 | # with: 16 | # To install LaTeX to build PDF book 17 | # tinytex: true 18 | # uncomment below and fill to pin a version 19 | # version: SPECIFIC-QUARTO-VERSION-HERE 20 | - name: Setup Julia 21 | uses: julia-actions/setup-julia@latest 22 | with: 23 | version: 1.9 24 | - name: Julia Cache 25 | uses: julia-actions/cache@v1 26 | - name: Cache Quarto 27 | id: cache-quarto 28 | uses: actions/cache@v3 29 | env: 30 | cache-name: cache-quarto 31 | with: 32 | path: _freeze 33 | key: ${{ runner.os }}-${{ env.cache-name }}-${{ hashFiles('*.qmd') }} 34 | restore-keys: | 35 | ${{ runner.os }}-${{ env.cache-name }}- 36 | - name: Cache CondaPkg 37 | id: cache-condaPkg 38 | uses: actions/cache@v3 39 | env: 40 | cache-name: cache-condapkg 41 | with: 42 | path: /.CondaPkg 43 | key: ${{ runner.os }}-${{ env.cache-name }}-${{ hashFiles('CondaPkg.toml') }} 44 | restore-keys: | 45 | ${{ runner.os }}-${{ env.cache-name }}- 46 | - name: "Render" 47 | run: "julia setup_render.jl" -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Other: 2 | references.bib.sav 3 | 4 | # Files generated by invoking Julia with --code-coverage 5 | *.jl.cov 6 | *.jl.*.cov 7 | 8 | # Files generated by invoking Julia with --track-allocation 9 | *.jl.mem 10 | 11 | # System-specific files and directories generated by the BinaryProvider and BinDeps packages 12 | # They contain absolute paths specific to the host computer, and so should not be committed 13 | deps/deps.jl 14 | deps/build.log 15 | deps/downloads/ 16 | deps/usr/ 17 | deps/src/ 18 | 19 | # Build artifacts for creating documentation generated by the Documenter package 20 | docs/build/ 21 | docs/site/ 22 | 23 | # File generated by Pkg, the package manager, based on a corresponding Project.toml 24 | # It records a fixed state of all packages used by the project. As such, it should not be 25 | # committed for packages, but should be committed for applications that require a static 26 | # environment. 27 | # Manifest.toml 28 | 29 | /.quarto/ 30 | data/ 31 | 32 | -------------------------------------------------------------------------------- /CondaPkg.toml: -------------------------------------------------------------------------------- 1 | [deps] 2 | jupyter = "" 3 | python = "3.11" -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 Pat Alt 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /Project.toml: -------------------------------------------------------------------------------- 1 | [deps] 2 | CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0" 3 | CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab" 4 | DataAugmentation = "88a5189c-e7ff-4f85-ac6b-e6158070f02e" 5 | DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" 6 | Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" 7 | FastAI = "5d0beca9-ade8-49ae-ad0b-a3cf890e669f" 8 | FastMakie = "ca994201-3d74-4fad-8816-f8bc5449c7c3" 9 | FastTabular = "d896c661-108c-409d-a26f-7fe446e2d3d3" 10 | FastVision = "7bf02486-ff4c-4e73-b158-40c00866b54f" 11 | FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549" 12 | Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" 13 | FreeType = "b38be410-82b0-50bf-ab77-7b57e271db43" 14 | IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a" 15 | ImageInTerminal = "d8c32880-2388-543b-8c61-d9f865259254" 16 | Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0" 17 | MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458" 18 | MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54" 19 | Measures = "442fdcdd-2543-5da2-b0f3-8c86c306513e" 20 | Metalhead = "dbeba491-748d-5e0e-a39e-b530a07fa0cc" 21 | OneHotArrays = "0b1bfda6-eb8a-41d2-88d8-f5af5cad476f" 22 | Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" 23 | StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" 24 | UnicodePlots = "b8865327-cd53-5732-bb35-84acbb429228" 25 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # julia-deeplearning-book 2 | Repo that hosts the companion book of [Julia for Deep Learning](https://github.com/cpfiffer/julia-deeplearning). 3 | -------------------------------------------------------------------------------- /_extensions/pat-alt/documenterjl/_extension.yml: -------------------------------------------------------------------------------- 1 | title: | 2 | `Documenter.jl` 3 | author: pat-alt 4 | version: 1.0.0 5 | quarto-required: ">=1.0" 6 | contributes: 7 | formats: 8 | commonmark: 9 | variant: -raw_html+tex_math_dollars 10 | wrap: preserve -------------------------------------------------------------------------------- /_extensions/pat-alt/julia/_extension.yml: -------------------------------------------------------------------------------- 1 | title: Julia 2 | author: pat-alt 3 | version: 1.0.0 4 | quarto-required: ">=1.0" 5 | contributes: 6 | formats: 7 | html: 8 | toc: true 9 | theme: [default, custom.scss] 10 | revealjs: 11 | theme: [default, custom.scss, revealjs.scss] 12 | 13 | 14 | -------------------------------------------------------------------------------- /_extensions/pat-alt/julia/custom.scss: -------------------------------------------------------------------------------- 1 | /*-- scss:defaults --*/ 2 | @font-face { 3 | font-family: JuliaMono-Light; 4 | src: url("https://cdn.jsdelivr.net/gh/cormullion/juliamono/webfonts/JuliaMono-Light.woff2"); 5 | } 6 | 7 | @import url("https://fonts.googleapis.com/css?family=Roboto"); 8 | 9 | $font-family-monospace: "JuliaMono-Light" !default; 10 | $font-family-sans-serif: "Roboto" !default; 11 | 12 | // Colors 13 | // Define the Julia colours as per https://github.com/JuliaLang/julia-logo-graphics#color-definitions 14 | $julia-blue: #4063D8; 15 | $julia-green: #389836; 16 | $julia-purple: #9558B2; 17 | $julia-red: #CB3C33; 18 | // Assign: 19 | $primary: $julia-blue !default; 20 | $body-bg: #ffffff !default; 21 | $body-color: #000000 !default; 22 | $presentation-heading-color: $julia-blue !default; 23 | $link-color: $julia-blue !default; 24 | // Code: 25 | $code-color: $julia-green !default; 26 | $dark-bg-code-color: lighten($code-color, 15%) !default; 27 | $light-bg-code-color: darken($code-color, 15%) !default; 28 | $code-block-border-left: lighten($code-color, 15%) !default; 29 | $btn-code-copy-color: $julia-purple !default; 30 | $btn-code-copy-color-active: lighten($btn-code-copy-color, 15%) !default; 31 | // Toc: 32 | $toc-color: $julia-purple !default; 33 | $toc-active-border: $julia-purple !default; 34 | $toc-inactive-border: lighten($toc-active-border, 15%) !default; 35 | // Callouts: 36 | $callout-color-note: $julia-blue !default; 37 | $callout-color-tip: $julia-green !default; 38 | $callout-color-warning: $julia-purple !default; 39 | $callout-color-caution: lighten($callout-color-warning, 30%) !default; 40 | $callout-color-important: $julia-red !default; 41 | 42 | /*-- scss:rules --*/ 43 | h1, h2, h3, h4, h5, h6 { 44 | font-family: "JuliaMono-Light"; 45 | } 46 | 47 | hr { 48 | color: $julia-blue; 49 | } -------------------------------------------------------------------------------- /_extensions/pat-alt/julia/revealjs.scss: -------------------------------------------------------------------------------- 1 | /*-- scss:rules --*/ 2 | .reveal { 3 | 4 | h1, h2, h3, h4, h5, h6 { 5 | font-family: "JuliaMono-Light"; 6 | } 7 | 8 | hr { 9 | color: $julia-blue; 10 | } 11 | 12 | .progress { 13 | color: $code-color; 14 | } 15 | } 16 | -------------------------------------------------------------------------------- /_freeze/chapters/c5/figure-html/cell-15-output-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pat-alt/julia-deeplearning-book/2a4fa695a149f5ca82e09e0f82a6a1c15492a3ee/_freeze/chapters/c5/figure-html/cell-15-output-1.png -------------------------------------------------------------------------------- /_freeze/chapters/c5/figure-html/cell-27-output-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pat-alt/julia-deeplearning-book/2a4fa695a149f5ca82e09e0f82a6a1c15492a3ee/_freeze/chapters/c5/figure-html/cell-27-output-1.png -------------------------------------------------------------------------------- /_freeze/site_libs/clipboard/clipboard.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * clipboard.js v2.0.11 3 | * https://clipboardjs.com/ 4 | * 5 | * Licensed MIT © Zeno Rocha 6 | */ 7 | !function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return b}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),r=n.n(e);function c(t){try{return document.execCommand(t)}catch(t){return}}var a=function(t){t=r()(t);return c("cut"),t};function o(t,e){var n,o,t=(n=t,o="rtl"===document.documentElement.getAttribute("dir"),(t=document.createElement("textarea")).style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style[o?"right":"left"]="-9999px",o=window.pageYOffset||document.documentElement.scrollTop,t.style.top="".concat(o,"px"),t.setAttribute("readonly",""),t.value=n,t);return e.container.appendChild(t),e=r()(t),c("copy"),t.remove(),e}var f=function(t){var e=1 label_func 87 | ) 88 | end 89 | 90 | image, class = loadimageclass(p) 91 | 92 | @show class 93 | image 94 | ``` 95 | 96 | Finally, we can use `mapobs` to lazily load all the images and their classes: 97 | 98 | ```{julia} 99 | data = mapobs(loadimageclass, files); 100 | ``` 101 | 102 | ```{julia} 103 | @show numobs(data) 104 | image, label = getobs(data, 1) 105 | ``` 106 | 107 | ## Using the Data Block API 108 | 109 | ::: {.callout-warning} 110 | 111 | ## `FastAI.jl` convention 112 | 113 | Contrary to fast.ai, `FastAI.jl` separates the data loading and container generation from the data augmentation. From the [documentation](https://fluxml.ai/FastAI.jl/dev/FastAI@dev/doc/docs/notebooks/siamese.ipynb.html): 114 | 115 | > In FastAI.jl, the preprocessing or "encoding" is implemented through a learning task. Learning tasks contain any configuration and, beside data processing, have extensible functions for visualizations and model building. One advantage of this separation between loading and encoding is that the data container can easily be swapped out as long as it has observations suitable for the learning task (in this case a tuple of two images and a Boolean). It also makes it easy to export models and all the necessary configuration. 116 | 117 | ::: 118 | 119 | First, we follow the standard procedure to split the data into training and validation sets: 120 | 121 | ```{julia} 122 | train_data, val_data = splitobs(data; at=0.8, shuffle=true) 123 | ``` 124 | 125 | Next, we define the data augmentation task separately as a `BlockTask`: 126 | 127 | ```{julia} 128 | _resize = 128 129 | blocks = ( 130 | Image{2}(), 131 | Label{String}(unique(labels)), 132 | ) 133 | task = BlockTask( 134 | blocks, 135 | ( 136 | ProjectiveTransforms( 137 | (_resize, _resize), 138 | sharestate=false, 139 | buffered=false, 140 | ), 141 | ImagePreprocessing(buffered=false), 142 | OneHot(), 143 | ) 144 | ) 145 | describetask(task) 146 | ``` 147 | 148 | We can apply the augmentation to the data as follows: 149 | 150 | ```{julia} 151 | batchsize = 3 152 | train_dl, val_dl = taskdataloaders(train_data, val_data, task, batchsize) 153 | ``` 154 | 155 | Let's quickly verify that the images look as expected: 156 | 157 | ```{julia} 158 | showbatch(task, first(train_dl)) 159 | ``` 160 | 161 | Finally, we can build our model as follows. First, we define the backbone: 162 | 163 | ```{julia} 164 | # Get backbone: 165 | _backbone = Metalhead.ResNet(18, pretrain=true).layers[1][1:end-1] 166 | ``` 167 | 168 | Here we have removed the final layer of the ResNet model, because we will instead use a custom head. We could use the `taskmodel` function to build the model with an appropriate head automatically: 169 | 170 | ```{julia} 171 | model = taskmodel(task, _backbone) 172 | model.layers[end] 173 | ``` 174 | 175 | Equivalently, we could have obtained an appropriate head as follows, 176 | 177 | ```{julia} 178 | h, w, ch, b = Flux.outputsize(_backbone, (_resize, _resize, 3, 1)) 179 | _head = Models.visionhead(ch, length(unique(labels))) 180 | ``` 181 | 182 | and then construct our model by chaining the backbone and head: 183 | 184 | ```{julia} 185 | Chain(_backbone, _head) 186 | ``` 187 | 188 | With the model defined, we can now create a `Learner` object from scratch: 189 | 190 | ```{julia} 191 | 192 | # Task data loader for new batch size: 193 | batchsize = 64 194 | train_dl, val_dl = taskdataloaders(train_data, val_data, task, batchsize) 195 | 196 | # Set up loss function, optimizer, callbacks, and learner: 197 | lossfn = Flux.Losses.logitcrossentropy 198 | optimizer = Flux.Adam() 199 | error_rate(ŷ, y) = mean(onecold(ŷ) .!= onecold(y)) 200 | callbacks = [ToGPU(), Metrics(error_rate)] 201 | 202 | learner = Learner( 203 | model, (train_dl, val_dl), 204 | optimizer, lossfn, 205 | callbacks... 206 | ) 207 | ``` 208 | 209 | 210 | ::: {.callout-tip} 211 | 212 | ## The `FastAI.jl` way 213 | 214 | Most of the manual jobs above can be done automatically using the `tasklearner` function: 215 | 216 | ```{julia} 217 | learner = tasklearner( 218 | task, train_data, val_data; 219 | backbone=_backbone, callbacks=callbacks, 220 | lossfn=lossfn, optimizer=optimizer, batchsize=batchsize, 221 | ) 222 | ``` 223 | 224 | Note that in this case, we pass on the raw, non-encoded data to the `tasklearner` function. This is because the `tasklearner` function will automatically encode the data using the `task` object. 225 | 226 | ::: 227 | 228 | We will begin by using the learning rate finder to find a good learning rate: 229 | 230 | ```{julia} 231 | #| eval: false 232 | 233 | res = lrfind(learner) 234 | ``` 235 | 236 | ![Learning rate finder output.](../www/c5_lr.png){#fig-lr-finder} 237 | 238 | 239 | Below we fine-tune the model for 5 epochs and then save it to disk: 240 | 241 | ```{julia} 242 | #| eval: false 243 | 244 | finetune!(learner, 5, 2e-3) 245 | ``` 246 | 247 | ::: {.callout-note} 248 | 249 | ## Freeze epochs 250 | 251 | Note that by default, this will train the model for one epoch with pre-trained weights (our `_backbone`) completely frozen. In other weights, only the parameters of our `_head` will be updated during this epoch, before the second phase of training begins. 252 | 253 | ::: 254 | 255 | Now we will fit the whole training cycle: 256 | 257 | ```{julia} 258 | #| eval: false 259 | 260 | fitonecycle!(learner, 5, 2e-3) 261 | savetaskmodel("artifacts/c5_resnet.jld2", task, learner.model, force=true) 262 | ``` 263 | 264 | Using our model, we can now make predictions on the validation set as follows: 265 | 266 | ```{julia} 267 | task, model = loadtaskmodel("artifacts/c5_resnet.jld2") 268 | 269 | samples = [getobs(data, i) for i in rand(1:numobs(val_data), 3)] 270 | images = [sample[1] for sample in samples] 271 | _labels = [sample[2] for sample in samples] 272 | 273 | preds = predictbatch(task, model, images; device = gpu, context=Validation()) 274 | ``` 275 | 276 | The accuracy is given by: 277 | 278 | ```{julia} 279 | acc = sum(_labels .== preds) / length(preds) 280 | ``` 281 | 282 | We can visualize the predictions as follows: 283 | 284 | ```{julia} 285 | showsamples(task, collect(zip(images, preds))) 286 | ``` 287 | -------------------------------------------------------------------------------- /chapters/images/grizzly.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pat-alt/julia-deeplearning-book/2a4fa695a149f5ca82e09e0f82a6a1c15492a3ee/chapters/images/grizzly.jpg -------------------------------------------------------------------------------- /contributors.qmd: -------------------------------------------------------------------------------- 1 | # Contributors' Guide 2 | 3 | In this guide, we collect useful information for authors and contributors to the book. 4 | 5 | ## Local rendering instructions 6 | 7 | By default, the book is rendered automatically through GitHub actions when you merge to `main`. However, if you want to preview the book locally, you can do so by following these instructions: 8 | 9 | 1. Clone [the repo](https://github.com/pat-alt/julia-deeplearning-book) for this book 10 | somewhere on your computer. Navigate using a terminal to the folder containing 11 | the repository. 12 | 2. Add the Julia kernel to Jupyter. This is currently tested with Julia 1.9, 13 | your results may vary with different versions of julia. 14 | 15 | ```julia 16 | # Load the current environment 17 | using Pkg; Pkg.activate("."); Pkg.instantiate() 18 | 19 | # Import IJulia 20 | using IJulia 21 | 22 | # Call a notebook. You can close it once you call this -- 23 | # we only need to run notebook() once for it to build the 24 | # jupyter kernel for Julia. 25 | notebook() 26 | ``` 27 | 28 | ::: {.callout-tip} 29 | ## Troubleshooting 30 | 31 | If you get an error message about `jupyter` not being found, you may need to `build IJulia` first. This 32 | ::: 33 | 34 | 3. Install [Quarto](https://quarto.org/docs/get-started/). 35 | 4. Render the website from the root of this folder with 36 | 37 | ``` 38 | quarto render 39 | ``` 40 | 5. Optionally, if you want to preview the HTML version of the page 41 | before you merge it to `main`, you can run 42 | 43 | ``` 44 | quarter preview 45 | ``` -------------------------------------------------------------------------------- /docs/CNAME: -------------------------------------------------------------------------------- 1 | book.deep-learning.club -------------------------------------------------------------------------------- /docs/chapters/c5_files/figure-html/cell-15-output-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pat-alt/julia-deeplearning-book/2a4fa695a149f5ca82e09e0f82a6a1c15492a3ee/docs/chapters/c5_files/figure-html/cell-15-output-1.png -------------------------------------------------------------------------------- /docs/chapters/c5_files/figure-html/cell-27-output-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pat-alt/julia-deeplearning-book/2a4fa695a149f5ca82e09e0f82a6a1c15492a3ee/docs/chapters/c5_files/figure-html/cell-27-output-1.png -------------------------------------------------------------------------------- /docs/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | Julia for Deep Learning 14 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 |
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Julia for Deep Learning

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Companion book of the Julia for Deep Learning Course

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Authors
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Affiliations
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Patrick Altmeyer

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Cameron Pfiffer

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221 | 222 | Stanford University 223 | 224 |

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Published
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August 17, 2023

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Preface

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This book provides an overview of a pure Julia implementation of the fastai book: Deep Learning for Coders with fastai and PyTorch. The original book works with Python and PyTorch. This book works with Julia and relies primarily on Flux.jl (Innes 2018). The original book is written by Jeremy Howard and Sylvain Gugge (Howard and Gugger 2020). This book is written by participants of the Julia for Deep Learning course.

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The book is served from GitHub.

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1  Introduction

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TBD

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204 | Howard, Jeremy, and Sylvain Gugger. 2020. Deep Learning 205 | for Coders with Fastai and PyTorch. 206 | "O’Reilly Media, Inc.". 207 |
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209 | Innes, Mike. 2018. “Flux: Elegant Machine Learning 210 | with Julia.” Journal of Open Source 211 | Software 3 (25): 602. 212 |
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214 | 215 | 216 | 217 |
218 | 514 | 523 |
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-------------------------------------------------------------------------------- /docs/site_libs/quarto-nav/headroom.min.js: -------------------------------------------------------------------------------- 1 | /*! 2 | * headroom.js v0.12.0 - Give your page some headroom. 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8 | -------------------------------------------------------------------------------- /docs/site_libs/quarto-nav/quarto-nav.js: -------------------------------------------------------------------------------- 1 | const headroomChanged = new CustomEvent("quarto-hrChanged", { 2 | detail: {}, 3 | bubbles: true, 4 | cancelable: false, 5 | composed: false, 6 | }); 7 | 8 | window.document.addEventListener("DOMContentLoaded", function () { 9 | let init = false; 10 | 11 | // Manage the back to top button, if one is present. 12 | let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; 13 | const scrollDownBuffer = 5; 14 | const scrollUpBuffer = 35; 15 | const btn = document.getElementById("quarto-back-to-top"); 16 | const hideBackToTop = () => { 17 | btn.style.display = "none"; 18 | }; 19 | const showBackToTop = () => { 20 | btn.style.display = "inline-block"; 21 | }; 22 | if (btn) { 23 | window.document.addEventListener( 24 | "scroll", 25 | function () { 26 | const currentScrollTop = 27 | window.pageYOffset || document.documentElement.scrollTop; 28 | 29 | // Shows and hides the button 'intelligently' as the user scrolls 30 | if (currentScrollTop - scrollDownBuffer > lastScrollTop) { 31 | hideBackToTop(); 32 | lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; 33 | } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { 34 | showBackToTop(); 35 | lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; 36 | } 37 | 38 | // Show the button at the bottom, hides it at the top 39 | if (currentScrollTop <= 0) { 40 | hideBackToTop(); 41 | } else if ( 42 | window.innerHeight + currentScrollTop >= 43 | document.body.offsetHeight 44 | ) { 45 | showBackToTop(); 46 | } 47 | }, 48 | false 49 | ); 50 | } 51 | 52 | function throttle(func, wait) { 53 | var timeout; 54 | return function () { 55 | const context = this; 56 | const args = arguments; 57 | const later = function () { 58 | clearTimeout(timeout); 59 | timeout = null; 60 | func.apply(context, args); 61 | }; 62 | 63 | if (!timeout) { 64 | timeout = setTimeout(later, wait); 65 | } 66 | }; 67 | } 68 | 69 | function headerOffset() { 70 | // Set an offset if there is are fixed top navbar 71 | const headerEl = window.document.querySelector("header.fixed-top"); 72 | if (headerEl) { 73 | return headerEl.clientHeight; 74 | } else { 75 | return 0; 76 | } 77 | } 78 | 79 | function footerOffset() { 80 | const footerEl = window.document.querySelector("footer.footer"); 81 | if (footerEl) { 82 | return footerEl.clientHeight; 83 | } else { 84 | return 0; 85 | } 86 | } 87 | 88 | function updateDocumentOffsetWithoutAnimation() { 89 | updateDocumentOffset(false); 90 | } 91 | 92 | function updateDocumentOffset(animated) { 93 | // set body offset 94 | const topOffset = headerOffset(); 95 | const bodyOffset = topOffset + footerOffset(); 96 | const bodyEl = window.document.body; 97 | bodyEl.setAttribute("data-bs-offset", topOffset); 98 | bodyEl.style.paddingTop = topOffset + "px"; 99 | 100 | // deal with sidebar offsets 101 | const sidebars = window.document.querySelectorAll( 102 | ".sidebar, .headroom-target" 103 | ); 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-------------------------------------------------------------------------------- /docs/summary.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Julia for Deep Learning - 4  Summary 11 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 |
80 |
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167 | 168 |
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170 |

4  Summary

171 |
172 | 173 | 174 | 175 |
176 | 177 | 178 | 179 | 180 |
181 | 182 | 183 |
184 | 185 |

In summary, this book has no content whatsoever.

186 | 187 | 188 | 189 |
190 | 486 | 498 |
499 | 500 | 501 | 502 | 503 | -------------------------------------------------------------------------------- /docs/www/c4_gd.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pat-alt/julia-deeplearning-book/2a4fa695a149f5ca82e09e0f82a6a1c15492a3ee/docs/www/c4_gd.gif -------------------------------------------------------------------------------- /docs/www/c4_mnist.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pat-alt/julia-deeplearning-book/2a4fa695a149f5ca82e09e0f82a6a1c15492a3ee/docs/www/c4_mnist.gif -------------------------------------------------------------------------------- /docs/www/c5_lr.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pat-alt/julia-deeplearning-book/2a4fa695a149f5ca82e09e0f82a6a1c15492a3ee/docs/www/c5_lr.png -------------------------------------------------------------------------------- /index.qmd: -------------------------------------------------------------------------------- 1 | # Preface {.unnumbered} 2 | 3 | This book provides an overview of a pure Julia implementation of the fastai book: [Deep Learning for Coders with fastai and PyTorch](https://github.com/fastai/fastbook). The original book works with Python and PyTorch. This book works with Julia and relies primarily on [`Flux.jl`](https://fluxml.ai/) [@innes2018flux]. The original book is written by Jeremy Howard and Sylvain Gugge [@howard2020deep]. This book is written by participants of the [Julia for Deep Learning](https://www.deep-learning.club/) course. 4 | 5 | The book is served from [GitHub](https://github.com/pat-alt/julia-deeplearning-book). 6 | 7 | 8 | 9 | -------------------------------------------------------------------------------- /intro.qmd: -------------------------------------------------------------------------------- 1 | # Introduction 2 | 3 | TBD 4 | -------------------------------------------------------------------------------- /logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pat-alt/julia-deeplearning-book/2a4fa695a149f5ca82e09e0f82a6a1c15492a3ee/logo.png -------------------------------------------------------------------------------- /references.bib: -------------------------------------------------------------------------------- 1 | 2 | @Unpublished{innes2018fashionable, 3 | author = {Innes, Michael and Saba, Elliot and Fischer, Keno and Gandhi, Dhairya and Rudilosso, Marco Concetto and Joy, Neethu Mariya and Karmali, Tejan and Pal, Avik and Shah, Viral}, 4 | title = {Fashionable Modelling with Flux}, 5 | archiveprefix = {arXiv}, 6 | date-added = {2022-12-13 12:58:01 +0100}, 7 | date-modified = {2022-12-13 12:58:01 +0100}, 8 | eprint = {1811.01457}, 9 | eprinttype = {arxiv}, 10 | year = {2018}, 11 | } 12 | 13 | @Article{innes2018flux, 14 | author = {Innes, Mike}, 15 | title = {Flux: {{Elegant}} Machine Learning with {{Julia}}}, 16 | number = {25}, 17 | pages = {602}, 18 | volume = {3}, 19 | date-added = {2022-12-13 12:58:01 +0100}, 20 | date-modified = {2022-12-13 12:58:01 +0100}, 21 | journal = {Journal of Open Source Software}, 22 | year = {2018}, 23 | } 24 | 25 | @Book{howard2020deep, 26 | author = {Howard, Jeremy and Gugger, Sylvain}, 27 | date = {2020-06}, 28 | title = {Deep {Learning} for {Coders} with fastai and {PyTorch}}, 29 | isbn = {9781492045496}, 30 | language = {en}, 31 | note = {Google-Books-ID: yATuDwAAQBAJ}, 32 | publisher = {"O'Reilly Media, Inc."}, 33 | abstract = {Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.Train models in computer vision, natural language processing, tabular data, and collaborative filteringLearn the latest deep learning techniques that matter most in practiceImprove accuracy, speed, and reliability by understanding how deep learning models workDiscover how to turn your models into web applicationsImplement deep learning algorithms from scratchConsider the ethical implications of your workGain insight from the foreword by PyTorch cofounder, Soumith Chintala}, 34 | file = {Google Books Link:https\://books.google.nl/books?id=yATuDwAAQBAJ:text/html}, 35 | keywords = {Computers / Data Science / Machine Learning, Computers / Image Processing, Computers / Computer Science, Computers / Machine Theory, Computers / Data Science / Neural Networks, Computers / Programming / General, Computers / Languages / Python, Computers / Data Science / Data Visualization}, 36 | } 37 | 38 | @Comment{jabref-meta: databaseType:bibtex;} 39 | -------------------------------------------------------------------------------- /references.qmd: -------------------------------------------------------------------------------- 1 | # References {.unnumbered} 2 | 3 | ::: {#refs} 4 | ::: 5 | -------------------------------------------------------------------------------- /setup_render.jl: -------------------------------------------------------------------------------- 1 | using Pkg 2 | Pkg.activate(@__DIR__) 3 | Pkg.status() 4 | Pkg.resolve() 5 | Pkg.instantiate() 6 | 7 | # Always accept data downloads: 8 | ENV["DATADEPS_ALWAYS_ACCEPT"] = "true" 9 | 10 | # Conda and render: 11 | using CondaPkg 12 | CondaPkg.withenv() do 13 | Pkg.resolve() 14 | Pkg.instantiate() 15 | @info "Render book" 16 | Pkg.build("IJulia") # build IJulia to the right version. 17 | run(`quarto render`) 18 | end -------------------------------------------------------------------------------- /summary.qmd: -------------------------------------------------------------------------------- 1 | # Summary 2 | 3 | In summary, this book has no content whatsoever. 4 | -------------------------------------------------------------------------------- /www/c4_gd.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pat-alt/julia-deeplearning-book/2a4fa695a149f5ca82e09e0f82a6a1c15492a3ee/www/c4_gd.gif -------------------------------------------------------------------------------- /www/c4_mnist.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pat-alt/julia-deeplearning-book/2a4fa695a149f5ca82e09e0f82a6a1c15492a3ee/www/c4_mnist.gif -------------------------------------------------------------------------------- /www/c5_lr.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pat-alt/julia-deeplearning-book/2a4fa695a149f5ca82e09e0f82a6a1c15492a3ee/www/c5_lr.png --------------------------------------------------------------------------------