Update course book

_sources/tutorials/TechnicalHelp/Discord.md

@@ -1,3 +1,3 @@
 # Using Discord
 
-[Please click here for Discord Guide](https://docs.google.com/document/d/1a5l6QVhuqYnwFR090yDnQGhHSA3u2IEwOs0JZwkfyLo/edit?usp=sharing)
+[Please click here for Discord Guide](https://docs.neuromatch.io/p/bz7uXZLMwMvDqn/Discord-Guides)

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-14356126-e765-4652-94c2-a8e36873215c">
-<span class="eqno">(128)<a class="headerlink" href="#equation-14356126-e765-4652-94c2-a8e36873215c" title="Permalink to this equation">#</a></span>\[\begin{align}
+<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>\[\begin{align}
 \hat{x} &amp;= \lambda x_i + (1 - \lambda) x_j \\
 \hat{y} &amp;= \lambda y_i + (1 - \lambda) y_j
 \end{align}\]</div>

projects/modelingsteps/Example_Deep_Learning_Project.html

@@ -1779,20 +1779,20 @@ <h2>Data ingredients<a class="headerlink" href="#data-ingredients" title="Permal
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+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Counter({np.int64(0): 74,
+         np.int64(1): 74,
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 </pre></div>
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@@ -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({2: 13,
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+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Counter({np.int64(2): 13,
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+         np.int64(12): 12,
+         np.int64(13): 12,
+         np.int64(1): 12,
+         np.int64(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: 1.0587, Accuracy: 62.60%
+<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%
 ------------------------------------------
-Epoch [100/500], Step [2/2], Loss: 1.0646, Accuracy: 63.37%
+Epoch [100/500], Step [2/2], Loss: 0.9540, Accuracy: 64.34%
 ------------------------------------------
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [200/500], Step [1/2], Loss: 0.7002, Accuracy: 76.36%
+<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%
 ------------------------------------------
-Epoch [200/500], Step [2/2], Loss: 0.6983, Accuracy: 73.26%
+Epoch [200/500], Step [2/2], Loss: 0.7234, Accuracy: 76.74%
 ------------------------------------------
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [300/500], Step [1/2], Loss: 0.5926, Accuracy: 80.43%
+<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%
 ------------------------------------------
-Epoch [300/500], Step [2/2], Loss: 0.6010, Accuracy: 77.33%
+Epoch [300/500], Step [2/2], Loss: 0.6153, Accuracy: 78.29%
 ------------------------------------------
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [400/500], Step [1/2], Loss: 0.5081, Accuracy: 82.95%
+<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%
 ------------------------------------------
-Epoch [400/500], Step [2/2], Loss: 0.5129, Accuracy: 80.81%
+Epoch [400/500], Step [2/2], Loss: 0.4215, Accuracy: 87.40%
 ------------------------------------------
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch [500/500], Step [1/2], Loss: 0.3980, Accuracy: 86.43%
+<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%
 ------------------------------------------
-Epoch [500/500], Step [2/2], Loss: 0.3824, Accuracy: 87.02%
+Epoch [500/500], Step [2/2], Loss: 0.3677, Accuracy: 86.82%
 ------------------------------------------
 </pre></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/0bce2cac7f1f0e2b734c25561a7c9cb0ed1a8f5e0819a6c528aa4d99732c500c.png" src="../../_images/0bce2cac7f1f0e2b734c25561a7c9cb0ed1a8f5e0819a6c528aa4d99732c500c.png" />
+<img alt="../../_images/1a95bd40d4128913bd4488e76d1f5f5d0bd2f40f980384a709614b82496189bc.png" src="../../_images/1a95bd40d4128913bd4488e76d1f5f5d0bd2f40f980384a709614b82496189bc.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>71.51162790697676
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>74.4186046511628
 </pre></div>
 </div>
-<img alt="../../_images/24d1d9d82e69a7fd2a5b1bf4a51883ded823644299034f8f9f670eecde7cf405.png" src="../../_images/24d1d9d82e69a7fd2a5b1bf4a51883ded823644299034f8f9f670eecde7cf405.png" />
+<img alt="../../_images/1b0939990a6d4358fefacbe6afc306f17de24d2f7ba7598c8b2e37f6e7103e20.png" src="../../_images/1b0939990a6d4358fefacbe6afc306f17de24d2f7ba7598c8b2e37f6e7103e20.png" />
 </div>
 </div>
 <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
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 75.00%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 77.33%
 
 *** FITTING: Right Leg
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 69.19%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 72.09%
 
 *** 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: 64.53%
 
 *** FITTING: Right Arm
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 31.40%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 42.44%
 
 *** FITTING: Torso
 </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: 81.98%
 
 *** FITTING: Head
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 50.58%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>limb performance: 53.49%
 </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: 68.02%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 82.56%
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 44.77%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 84.88%
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 66.86%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 68.60%
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 74.42%
+<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: 84.30%
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 72.67%
-median performance: 70.35%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 82.56%
+median performance: 82.56%
 
 *** FITTING: limbs+torso+head
 </pre></div>
 </div>
-<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: 76.74%
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 81.98%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 81.40%
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 83.14%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 75.00%
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 74.42%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 86.63%
 </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: 75.00%
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 80.81%
-median performance: 82.27%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>performance: 77.91%
+median performance: 77.33%
 </pre></div>
 </div>
 </div>

projects/modelingsteps/TrainIllusionDataProjectDL.html

@@ -1772,8 +1772,8 @@ <h1>Question<a class="headerlink" href="#question" title="Permalink to this head
 <p><em>Part of Step 1</em></p>
 <p>Previous literature predicts that accumulated sensory evidence from vestibular signals determines the decision of whether self-motion is experienced or not. We now have vestibular neuron data (simulated in our case, but let’s pretend) and would like to see if that prediction holds true.</p>
 <p>The data contains <span class="math notranslate nohighlight">\(N\)</span> neurons and <span class="math notranslate nohighlight">\(M\)</span> trials for each of 3 motion conditions: no self-motion, slowly accelerating self-motion and faster accelerating self-motion.</p>
-<div class="amsmath math notranslate nohighlight" id="equation-87376974-c49b-40e2-a038-4467d6413558">
-<span class="eqno">(126)<a class="headerlink" href="#equation-87376974-c49b-40e2-a038-4467d6413558" title="Permalink to this equation">#</a></span>\[\begin{align}
+<div class="amsmath math notranslate nohighlight" id="equation-6e2f3d24-fb2c-4d4d-bee9-6c48fe368c80">
+<span class="eqno">(126)<a class="headerlink" href="#equation-6e2f3d24-fb2c-4d4d-bee9-6c48fe368c80" title="Permalink to this equation">#</a></span>\[\begin{align}
 N &amp;= 40 \\
 M &amp;= 400
 \end{align}\]</div>

projects/modelingsteps/TrainIllusionModelingProjectDL.html

@@ -1553,8 +1553,8 @@ <h1>Selected toolkit<a class="headerlink" href="#selected-toolkit" title="Permal
 <p><em>Part of step 5</em></p>
 <p>We chose to use a <a class="reference external" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2474742/">Drift-Diffusion Model (DDM)</a> because it is a well-established framework that allows us to model decision making in the case of 2 alternative choices (here: self-motion vs. other train motion).</p>
 <p>For our purposes simplest equation looks something like this:</p>
-<div class="amsmath math notranslate nohighlight" id="equation-3c777bf7-0244-4124-9a31-713fb6eafa23">
-<span class="eqno">(127)<a class="headerlink" href="#equation-3c777bf7-0244-4124-9a31-713fb6eafa23" title="Permalink to this equation">#</a></span>\[\begin{align}
+<div class="amsmath math notranslate nohighlight" id="equation-eb57baf0-d5a8-493f-bc46-d5e4097afa93">
+<span class="eqno">(127)<a class="headerlink" href="#equation-eb57baf0-d5a8-493f-bc46-d5e4097afa93" title="Permalink to this equation">#</a></span>\[\begin{align}
 \dot e = \frac{de}{dt}= -c \cdot e + v \, ,
 \end{align}\]</div>
 <p>where <span class="math notranslate nohighlight">\(e\)</span> is the accumulated evidence and <span class="math notranslate nohighlight">\(v\)</span> is our vestibular input already containing the noise (so we don’t need to add more noise?). <span class="math notranslate nohighlight">\(c\)</span> is the leakage constant, i.e., <span class="math notranslate nohighlight">\(c=0\)</span> means perfect integration; <span class="math notranslate nohighlight">\(c=1\)</span> means no integration (perfect leakage).</p>
@@ -1630,7 +1630,7 @@ <h2>1. Vestibular signal generator<a class="headerlink" href="#vestibular-signal
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(0, 0.5, &#39;vestibular signal (a.u.)&#39;)
 </pre></div>
 </div>
-<img alt="../../_images/e3fc33d99a557542825a5232b07ae795f98555d2aebf08ce8e704b6cb0729065.png" src="../../_images/e3fc33d99a557542825a5232b07ae795f98555d2aebf08ce8e704b6cb0729065.png" />
+<img alt="../../_images/ec4e470bac05e9119a4f38187442d35e2a5f7b584982911ee068f70396d2e604.png" src="../../_images/ec4e470bac05e9119a4f38187442d35e2a5f7b584982911ee068f70396d2e604.png" />
 </div>
 </div>
 </section>
@@ -1671,7 +1671,7 @@ <h2>2. Integrator (DDM mechanism)<a class="headerlink" href="#integrator-ddm-mec
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../../_images/f94eda82845a77aa689710ef57b699e04362bcbc35398d294d61ce9cf5379935.png" src="../../_images/f94eda82845a77aa689710ef57b699e04362bcbc35398d294d61ce9cf5379935.png" />
+<img alt="../../_images/ddbb4111d7ae61ca6a8d6948300a9e0315c370c93f9c81f7e30ceaaf5c83dc6b.png" src="../../_images/ddbb4111d7ae61ca6a8d6948300a9e0315c370c93f9c81f7e30ceaaf5c83dc6b.png" />
 </div>
 </div>
 </section>
@@ -1821,7 +1821,7 @@ <h1>Model evaluation &amp; testing<a class="headerlink" href="#model-evaluation-
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../../_images/ecace8595276e67df266136fec27864774d0ffef24534fbf01211997606729e6.png" src="../../_images/ecace8595276e67df266136fec27864774d0ffef24534fbf01211997606729e6.png" />
+<img alt="../../_images/d3bcd3ed3451162652b296aadb4671507a614ee183665acd6518902e23e0c4ae.png" src="../../_images/d3bcd3ed3451162652b296aadb4671507a614ee183665acd6518902e23e0c4ae.png" />
 </div>
 </div>
 <p>There seems to be some parameter redundancy, i.e., we could chose different parameter combinations to make the model do something sensible…</p>
@@ -1842,7 +1842,7 @@ <h1>Model evaluation &amp; testing<a class="headerlink" href="#model-evaluation-
 </div>
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 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> Motion detected for no-motion: 30.8% and motion: 59.5%
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> Motion detected for no-motion: 28.3% and motion: 58.4%
 </pre></div>
 </div>
 </div>
@@ -1880,7 +1880,7 @@ <h1>Model evaluation &amp; testing<a class="headerlink" href="#model-evaluation-
 </div>
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 <div class="cell_output docutils container">
-<img alt="../../_images/5219637fdd69d9d85950cab4edf4fb4bd6c14d82c15f1f99a4c4af295dee1997.png" src="../../_images/5219637fdd69d9d85950cab4edf4fb4bd6c14d82c15f1f99a4c4af295dee1997.png" />
+<img alt="../../_images/f0eb9f487c1c0735d49e58262ee07cbe238c46ff7f37b4853c841b775adc25eb.png" src="../../_images/f0eb9f487c1c0735d49e58262ee07cbe238c46ff7f37b4853c841b775adc25eb.png" />
 </div>
 </div>
 <p>Our <strong>hypothesis</strong> of linear increase of illusion strength with noise only holds true in a limited range of noise… It’s monotonic but saturating of course…</p>