Update course book

NeuromatchAcademy · Jul 16, 2024 · 782a1f0 · 782a1f0
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diff --git a/_sources/projects/ComputerVision/em_synapses.ipynb b/_sources/projects/ComputerVision/em_synapses.ipynb
diff --git a/_sources/projects/ComputerVision/spectrogram_analysis.ipynb b/_sources/projects/ComputerVision/spectrogram_analysis.ipynb
diff --git a/_sources/projects/NaturalLanguageProcessing/sentiment_analysis.ipynb b/_sources/projects/NaturalLanguageProcessing/sentiment_analysis.ipynb
diff --git a/_sources/projects/Neuroscience/blurry_vision.ipynb b/_sources/projects/Neuroscience/blurry_vision.ipynb
diff --git a/_sources/projects/Neuroscience/cellular_segmentation.ipynb b/_sources/projects/Neuroscience/cellular_segmentation.ipynb
diff --git a/_sources/projects/ReinforcementLearning/human_rl.ipynb b/_sources/projects/ReinforcementLearning/human_rl.ipynb
diff --git a/_sources/projects/ReinforcementLearning/lunar_lander.ipynb b/_sources/projects/ReinforcementLearning/lunar_lander.ipynb
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-a5523f5a-0b64-40da-a805-fd7b38d332ef">
-<span class="eqno">(128)<a class="headerlink" href="#equation-a5523f5a-0b64-40da-a805-fd7b38d332ef" title="Permalink to this equation">#</a></span>\[\begin{align}
+<div class="amsmath math notranslate nohighlight" id="equation-1585e5df-6a6b-44d4-8d67-9b15997e50a3">
+<span class="eqno">(128)<a class="headerlink" href="#equation-1585e5df-6a6b-44d4-8d67-9b15997e50a3" 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/ComputerVision/em_synapses.html b/projects/ComputerVision/em_synapses.html
diff --git a/projects/ComputerVision/spectrogram_analysis.html b/projects/ComputerVision/spectrogram_analysis.html
diff --git a/projects/NaturalLanguageProcessing/sentiment_analysis.html b/projects/NaturalLanguageProcessing/sentiment_analysis.html
diff --git a/projects/Neuroscience/blurry_vision.html b/projects/Neuroscience/blurry_vision.html
diff --git a/projects/Neuroscience/cellular_segmentation.html b/projects/Neuroscience/cellular_segmentation.html
diff --git a/projects/ReinforcementLearning/human_rl.html b/projects/ReinforcementLearning/human_rl.html
diff --git a/projects/ReinforcementLearning/lunar_lander.html b/projects/ReinforcementLearning/lunar_lander.html
diff --git a/projects/modelingsteps/Example_Deep_Learning_Project.html b/projects/modelingsteps/Example_Deep_Learning_Project.html
@@ -1779,20 +1779,20 @@ <h2>Data ingredients<a class="headerlink" href="#data-ingredients" title="Permal
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Counter({np.int64(0): 74,
-         np.int64(1): 74,
-         np.int64(6): 74,
-         np.int64(7): 74,
-         np.int64(8): 74,
-         np.int64(9): 74,
-         np.int64(10): 74,
-         np.int64(11): 74,
-         np.int64(12): 74,
-         np.int64(13): 74,
-         np.int64(4): 73,
-         np.int64(5): 73,
-         np.int64(3): 73,
-         np.int64(2): 73})
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Counter({0: 74,
+         1: 74,
+         6: 74,
+         7: 74,
+         8: 74,
+         9: 74,
+         10: 74,
+         11: 74,
+         12: 74,
+         13: 74,
+         4: 73,
+         5: 73,
+         3: 73,
+         2: 73})
 </pre></div>
 </div>
 </div>
@@ -1804,20 +1804,20 @@ <h2>Data ingredients<a class="headerlink" href="#data-ingredients" title="Permal
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Counter({np.int64(2): 13,
-         np.int64(3): 13,
-         np.int64(5): 13,
-         np.int64(4): 13,
-         np.int64(6): 12,
-         np.int64(7): 12,
-         np.int64(8): 12,
-         np.int64(9): 12,
-         np.int64(11): 12,
-         np.int64(10): 12,
-         np.int64(12): 12,
-         np.int64(13): 12,
-         np.int64(1): 12,
-         np.int64(0): 12})
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Counter({2: 13,
+         3: 13,
+         5: 13,
+         4: 13,
+         6: 12,
+         7: 12,
+         8: 12,
+         9: 12,
+         11: 12,
+         10: 12,
+         12: 12,
+         13: 12,
+         1: 12,
+         0: 12})
 </pre></div>
 </div>
 </div>
@@ -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: 1.0456, Accuracy: 64.15%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [100/500], Step [1/2], Loss: 1.0727, Accuracy: 61.63%
 ------------------------------------------
-Epoch [100/500], Step [2/2], Loss: 0.9540, Accuracy: 64.34%
+Epoch [100/500], Step [2/2], Loss: 1.0326, Accuracy: 64.92%
 ------------------------------------------
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [200/500], Step [1/2], Loss: 0.6982, Accuracy: 75.19%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [200/500], Step [1/2], Loss: 0.8082, Accuracy: 72.09%
 ------------------------------------------
-Epoch [200/500], Step [2/2], Loss: 0.7234, Accuracy: 76.74%
+Epoch [200/500], Step [2/2], Loss: 0.8386, Accuracy: 66.67%
 ------------------------------------------
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [300/500], Step [1/2], Loss: 0.6144, Accuracy: 78.49%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [300/500], Step [1/2], Loss: 0.7040, Accuracy: 74.61%
 ------------------------------------------
-Epoch [300/500], Step [2/2], Loss: 0.6153, Accuracy: 78.29%
+Epoch [300/500], Step [2/2], Loss: 0.6436, Accuracy: 77.71%
 ------------------------------------------
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [400/500], Step [1/2], Loss: 0.4783, Accuracy: 84.69%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [400/500], Step [1/2], Loss: 0.6655, Accuracy: 75.39%
 ------------------------------------------
-Epoch [400/500], Step [2/2], Loss: 0.4215, Accuracy: 87.40%
+Epoch [400/500], Step [2/2], Loss: 0.6367, Accuracy: 76.55%
 ------------------------------------------
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [500/500], Step [1/2], Loss: 0.4122, Accuracy: 87.21%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [500/500], Step [1/2], Loss: 0.5918, Accuracy: 78.29%
 ------------------------------------------
-Epoch [500/500], Step [2/2], Loss: 0.3677, Accuracy: 86.82%
+Epoch [500/500], Step [2/2], Loss: 0.5490, Accuracy: 78.49%
 ------------------------------------------
 </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: 80.814%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test Accuracy of the model on the 172 test moves: 76.163%
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@@ -2137,7 +2137,7 @@ <h2>Build model<a class="headerlink" href="#build-model" title="Permalink to thi
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-<img alt="../../_images/1a95bd40d4128913bd4488e76d1f5f5d0bd2f40f980384a709614b82496189bc.png" src="../../_images/1a95bd40d4128913bd4488e76d1f5f5d0bd2f40f980384a709614b82496189bc.png" />
+<img alt="../../_images/31ed92e5c2ca3eeaf6cc8b2485430e046e51df5210a5fa7e7bc55e0eda1069cb.png" src="../../_images/31ed92e5c2ca3eeaf6cc8b2485430e046e51df5210a5fa7e7bc55e0eda1069cb.png" />
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 <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
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>74.4186046511628
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>70.34883720930233
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-<img alt="../../_images/1b0939990a6d4358fefacbe6afc306f17de24d2f7ba7598c8b2e37f6e7103e20.png" src="../../_images/1b0939990a6d4358fefacbe6afc306f17de24d2f7ba7598c8b2e37f6e7103e20.png" />
+<img alt="../../_images/f779c042380a9a3a46e32f6eada61d9c401d1ed6544b483125122eedaa201a5a.png" src="../../_images/f779c042380a9a3a46e32f6eada61d9c401d1ed6544b483125122eedaa201a5a.png" />
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 <p>That is some pretty good performance based on only 6 / 24 joints!</p>
@@ -2274,32 +2274,32 @@ <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
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 77.33%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 71.51%
 
 *** FITTING: Right Leg
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 72.09%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 76.16%
 
 *** FITTING: Left Arm
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 64.53%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 63.37%
 
 *** FITTING: Right Arm
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 42.44%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 41.28%
 
 *** FITTING: Torso
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-<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: 83.14%
 
 *** FITTING: Head
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 53.49%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 51.16%
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@@ -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
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-<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: 66.28%
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 84.88%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 53.49%
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 68.60%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 71.51%
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 56.40%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 85.47%
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 84.30%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 67.44%
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 82.56%
-median performance: 82.56%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 78.49%
+median performance: 69.48%
 
 *** FITTING: limbs+torso+head
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 76.74%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 84.88%
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-<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: 71.51%
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 75.00%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 78.49%
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 86.63%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 82.56%
 </pre></div>
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 75.00%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 83.14%
 </pre></div>
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 77.91%
-median performance: 77.33%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 83.14%
+median performance: 82.85%
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