Update course book

NeuromatchAcademy · Nov 15, 2024 · 647151e · 647151e
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diff --git a/.buildinfo b/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 665b87bc356bcf7d6c6e949e546d49c8
+config: 1060e10d9f90f986cce81c57e41aa119
 tags: 645f666f9bcd5a90fca523b33c5a78b7
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diff --git a/_sources/tutorials/Schedule/daily_schedules.md b/_sources/tutorials/Schedule/daily_schedules.md
@@ -1,39 +1,33 @@
 # General schedule
 
-## 2024 course will run: July 8 - 26, 2024
-
-Course live events: TBD — will update 
+## 2025 course will run: July 7 - 25, 2025
 
 ## Course overview
 
-* Sun, July 7: Opening Ceremonies
-   * 8:30 am UTC
-   * 5:30 pm UTC
-
 * Week 1
-   * Mon, July 8: Basics and Pytorch
-   * Tue, July 9: Linear Deep Learning
-   * Wed, July 10: Multilayer Perceptrons
-   * Thu, July 11: Projects day
-   * Fri, July 12: Optimization
+   * Mon, July 7: Basics and Pytorch
+   * Tue, July 8: Linear Deep Learning
+   * Wed, July 9: Multilayer Perceptrons
+   * Thu, July 10: Projects day
+   * Fri, July 11: Optimization
 
 * Week 2
-   * Mon, July 15: Regularization
-   * Tue, July 16: Convnets and DL Thinking 1
-   * Wed, July 17: Modern Convnets
-   * Thu, July 18: Generative models (VAEs and Diffusion models)
-   * Fri, July 19: Attention and Transformers
+   * Mon, July 14: Regularization
+   * Tue, July 15: Convnets and DL Thinking 1
+   * Wed, July 16: Modern Convnets
+   * Thu, July 17: Generative models (VAEs and Diffusion models)
+   * Fri, July 18: Attention and Transformers
 
 * Week 3: Advanced concepts
-   * Mon, July 22: Time Series and Natural Language Processing
-   * Tue, July 23: DL Thinking 2 & Half Projects Day (Abstract Writing)
-   * Wed, July 24: Unsupervised and Self-supervised learning
-   * Thu, July 25: Basic Reinforcement Learning
-   * Fri, July 26: Reinforcement Learning for Games & DL Thinking 3
+   * Mon, July 21: Time Series and Natural Language Processing
+   * Tue, July 22: DL Thinking 2 & Half Projects Day (Abstract Writing)
+   * Wed, July 23: Unsupervised and Self-supervised learning
+   * Thu, July 24: Basic Reinforcement Learning
+   * Fri, July 25: Reinforcement Learning for Games & DL Thinking 3
 ----
 
 ## Daily coursework schedule
-These schedules refer to the coursework time.
+### 2025 course schedule coming soon. Below is the 2024 course schedule. 
 
 ### General schedule
 All days (except W1D4, W3D2, and W3D5) will follow this schedule for course time:

diff --git a/projects/ComputerVision/data_augmentation.html b/projects/ComputerVision/data_augmentation.html
@@ -1763,8 +1763,8 @@ <h2>Cutout<a class="headerlink" href="#cutout" title="Permalink to this heading"
 <section id="mixup">
 <h2>Mixup<a class="headerlink" href="#mixup" title="Permalink to this heading">#</a></h2>
 <p>Mixup is a data augmentation technique that combines pairs of examples via a convex combination of the images and the labels. Given images <span class="math notranslate nohighlight">\(x_i\)</span> and <span class="math notranslate nohighlight">\(x_j\)</span> with labels <span class="math notranslate nohighlight">\(y_i\)</span> and <span class="math notranslate nohighlight">\(y_j\)</span>, respectively, and <span class="math notranslate nohighlight">\(\lambda \in [0, 1]\)</span>, mixup creates a new image <span class="math notranslate nohighlight">\(\hat{x}\)</span> with label <span class="math notranslate nohighlight">\(\hat{y}\)</span> the following way:</p>
-<div class="amsmath math notranslate nohighlight" id="equation-efffedd2-3c0f-454f-b100-a54d6e702ebe">
-<span class="eqno">(128)<a class="headerlink" href="#equation-efffedd2-3c0f-454f-b100-a54d6e702ebe" title="Permalink to this equation">#</a></span>\[\begin{align}
+<div class="amsmath math notranslate nohighlight" id="equation-2ad93d08-692c-42e1-893e-5fbbbdc17a6d">
+<span class="eqno">(128)<a class="headerlink" href="#equation-2ad93d08-692c-42e1-893e-5fbbbdc17a6d" title="Permalink to this equation">#</a></span>\[\begin{align}
 \hat{x} &amp;= \lambda x_i + (1 - \lambda) x_j \\
 \hat{y} &amp;= \lambda y_i + (1 - \lambda) y_j
 \end{align}\]</div>

diff --git a/projects/modelingsteps/Example_Deep_Learning_Project.html b/projects/modelingsteps/Example_Deep_Learning_Project.html
@@ -2067,33 +2067,33 @@ <h2>Build model<a class="headerlink" href="#build-model" title="Permalink to thi
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [100/500], Step [1/2], Loss: 1.1024, Accuracy: 62.98%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [100/500], Step [1/2], Loss: 0.9360, Accuracy: 67.83%
 ------------------------------------------
-Epoch [100/500], Step [2/2], Loss: 1.1382, Accuracy: 62.60%
+Epoch [100/500], Step [2/2], Loss: 1.0686, Accuracy: 61.82%
 ------------------------------------------
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [200/500], Step [1/2], Loss: 0.8495, Accuracy: 68.99%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [200/500], Step [1/2], Loss: 0.6698, Accuracy: 77.13%
 ------------------------------------------
-Epoch [200/500], Step [2/2], Loss: 0.8329, Accuracy: 69.19%
+Epoch [200/500], Step [2/2], Loss: 0.6251, Accuracy: 77.33%
 ------------------------------------------
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [300/500], Step [1/2], Loss: 0.7093, Accuracy: 75.58%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [300/500], Step [1/2], Loss: 0.5240, Accuracy: 82.36%
 ------------------------------------------
-Epoch [300/500], Step [2/2], Loss: 0.6627, Accuracy: 75.58%
+Epoch [300/500], Step [2/2], Loss: 0.4623, Accuracy: 81.98%
 ------------------------------------------
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [400/500], Step [1/2], Loss: 0.5297, Accuracy: 79.65%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [400/500], Step [1/2], Loss: 0.3841, Accuracy: 85.27%
 ------------------------------------------
-Epoch [400/500], Step [2/2], Loss: 0.5616, Accuracy: 80.04%
+Epoch [400/500], Step [2/2], Loss: 0.4109, Accuracy: 86.82%
 ------------------------------------------
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [500/500], Step [1/2], Loss: 0.4855, Accuracy: 83.72%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [500/500], Step [1/2], Loss: 0.3158, Accuracy: 88.18%
 ------------------------------------------
-Epoch [500/500], Step [2/2], Loss: 0.4633, Accuracy: 85.66%
+Epoch [500/500], Step [2/2], Loss: 0.3384, Accuracy: 88.95%
 ------------------------------------------
 </pre></div>
 </div>
@@ -2123,7 +2123,7 @@ <h2>Build model<a class="headerlink" href="#build-model" title="Permalink to thi
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test Accuracy of the model on the 172 test moves: 76.744%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test Accuracy of the model on the 172 test moves: 87.209%
 </pre></div>
 </div>
 </div>
@@ -2137,7 +2137,7 @@ <h2>Build model<a class="headerlink" href="#build-model" title="Permalink to thi
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../../_images/9296fda502703a92010d8097ac19f5979fdfaf1ca2e3ca86f95f31ffb27458af.png" src="../../_images/9296fda502703a92010d8097ac19f5979fdfaf1ca2e3ca86f95f31ffb27458af.png" />
+<img alt="../../_images/409f9fd79de9fd07bc781a10009f52d974e309f50a7998389a24e3d09c2b3df0.png" src="../../_images/409f9fd79de9fd07bc781a10009f52d974e309f50a7998389a24e3d09c2b3df0.png" />
 </div>
 </div>
 <p>The errors vary each time the model is run, but a common error seems to be that head scratching is predicted from some other movements that also involve arms a lot: throw/catch, hand clapping, phone talking, checking watch, hand waving, taking photo. If we train the model longer, these errors tend to go away as well. For some reason, crossed legged sitting is sometimes misclassified for crawling, but this doesn’t always happen.</p>
@@ -2225,10 +2225,10 @@ <h1>Step 8: Modeling completion<a class="headerlink" href="#step-8-modeling-comp
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>73.25581395348837
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>73.83720930232558
 </pre></div>
 </div>
-<img alt="../../_images/ef5c82744f505d4b0f20340789cb1b66af3bcbd0e92f42d5aeecb5d976c1e002.png" src="../../_images/ef5c82744f505d4b0f20340789cb1b66af3bcbd0e92f42d5aeecb5d976c1e002.png" />
+<img alt="../../_images/0eb6063ba785e40e156e302d871f9a5387295004e93378f1b9c3f09fa30076a7.png" src="../../_images/0eb6063ba785e40e156e302d871f9a5387295004e93378f1b9c3f09fa30076a7.png" />
 </div>
 </div>
 <p>That is some pretty good performance based on only 6 / 24 joints!</p>
@@ -2274,17 +2274,17 @@ <h1>Step 9: Model evaluation<a class="headerlink" href="#step-9-model-evaluation
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>*** FITTING: Left Leg
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 77.91%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 76.16%
 
 *** FITTING: Right Leg
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 62.21%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 74.42%
 
 *** FITTING: Left Arm
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 51.74%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 63.95%
 
 *** FITTING: Right Arm
 </pre></div>
@@ -2294,12 +2294,12 @@ <h1>Step 9: Model evaluation<a class="headerlink" href="#step-9-model-evaluation
 *** FITTING: Torso
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 81.98%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 75.58%
 
 *** FITTING: Head
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 48.26%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 50.00%
 </pre></div>
 </div>
 </div>
@@ -2353,44 +2353,44 @@ <h1>Step 9: Model evaluation<a class="headerlink" href="#step-9-model-evaluation
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>*** FITTING: limbs only
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 44.19%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 79.07%
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 77.91%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 73.26%
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 81.40%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 56.40%
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 78.49%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 83.14%
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 70.93%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 77.91%
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 83.14%
-median performance: 78.20%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 71.51%
+median performance: 75.58%
 
 *** FITTING: limbs+torso+head
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 73.26%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 85.47%
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 82.56%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 80.23%
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 78.49%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 87.79%
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 88.95%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 71.51%
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 78.49%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 36.05%
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 80.81%
-median performance: 79.65%
+median performance: 80.52%
 </pre></div>
 </div>
 </div>

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