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update of typos

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This commit is contained in:
Morten Hjorth-Jensen
2021-11-03 08:00:38 +01:00
parent 3f221abed3
commit 4db982a698
13 changed files with 441 additions and 441 deletions
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doc/pub/week39/html/week39-reveal.html
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@@ -2246,9 +2246,9 @@ theta = np.random.randn(2,1)
for epoch in range(n_epochs):
# Can you figure out a better way of setting up the contributions to each batch?
for i in range(m):
random_index = np.random.randint(m)
xi = X[random_index*M:random_index*M+M]
yi = y[random_index*M:random_index*M+M]
random_index = M*np.random.randint(m)
xi = X[random_index:random_index+M]
yi = y[random_index:random_index+M]
gradients = (2.0/M)* xi.T @ ((xi @ theta)-yi)
eta = learning_schedule(epoch*m+i)
theta = theta - eta*gradients
doc/pub/week39/html/week39-solarized.html
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@@ -2133,9 +2133,9 @@ theta = np.random.randn(2,1)
for epoch in range(n_epochs):
# Can you figure out a better way of setting up the contributions to each batch?
for i in range(m):
random_index = np.random.randint(m)
xi = X[random_index*M:random_index*M+M]
yi = y[random_index*M:random_index*M+M]
random_index = M*np.random.randint(m)
xi = X[random_index:random_index+M]
yi = y[random_index:random_index+M]
gradients = (2.0/M)* xi.T @ ((xi @ theta)-yi)
eta = learning_schedule(epoch*m+i)
theta = theta - eta*gradients
doc/pub/week39/html/week39.html
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@@ -2210,9 +2210,9 @@ theta = np.random.randn(2,1)
for epoch in range(n_epochs):
# Can you figure out a better way of setting up the contributions to each batch?
for i in range(m):
random_index = np.random.randint(m)
xi = X[random_index*M:random_index*M+M]
yi = y[random_index*M:random_index*M+M]
random_index = M*np.random.randint(m)
xi = X[random_index:random_index+M]
yi = y[random_index:random_index+M]
gradients = (2.0/M)* xi.T @ ((xi @ theta)-yi)
eta = learning_schedule(epoch*m+i)
theta = theta - eta*gradients
doc/pub/week39/ipynb/ipynb-week39-src.tar.gz
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doc/pub/week39/ipynb/week39.ipynb
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doc/pub/week40/html/._week40-bs012.html
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@@ -365,9 +365,9 @@ theta <span style="color: #666666">=</span> np<span style="color: #666666">.</sp
<span style="color: #008000; font-weight: bold">for</span> epoch <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(n_epochs):
<span style="color: #408080; font-style: italic"># Can you figure out a better way of setting up the contributions to each batch?</span>
<span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(m):
random_index <span style="color: #666666">=</span> np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>randint(m)
xi <span style="color: #666666">=</span> X[random_index<span style="color: #666666">*</span>M:random_index<span style="color: #666666">*</span>M<span style="color: #666666">+</span>M]
yi <span style="color: #666666">=</span> y[random_index<span style="color: #666666">*</span>M:random_index<span style="color: #666666">*</span>M<span style="color: #666666">+</span>M]
random_index <span style="color: #666666">=</span> M<span style="color: #666666">*</span>np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>randint(m)
xi <span style="color: #666666">=</span> X[random_index:random_index<span style="color: #666666">+</span>M]
yi <span style="color: #666666">=</span> y[random_index:random_index<span style="color: #666666">+</span>M]
gradients <span style="color: #666666">=</span> (<span style="color: #666666">2.0/</span>M)<span style="color: #666666">*</span> xi<span style="color: #666666">.</span>T <span style="color: #666666">@</span> ((xi <span style="color: #666666">@</span> theta)<span style="color: #666666">-</span>yi)
eta <span style="color: #666666">=</span> learning_schedule(epoch<span style="color: #666666">*</span>m<span style="color: #666666">+</span>i)
theta <span style="color: #666666">=</span> theta <span style="color: #666666">-</span> eta<span style="color: #666666">*</span>gradients
doc/pub/week40/html/week40-reveal.html
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@@ -547,9 +547,9 @@ theta = np.random.randn(<span style="color: #B452CD">2</span>,<span style="color
<span style="color: #8B008B; font-weight: bold">for</span> epoch <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(n_epochs):
<span style="color: #228B22"># Can you figure out a better way of setting up the contributions to each batch?</span>
<span style="color: #8B008B; font-weight: bold">for</span> i <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(m):
random_index = np.random.randint(m)
xi = X[random_index*M:random_index*M+M]
yi = y[random_index*M:random_index*M+M]
random_index = M*np.random.randint(m)
xi = X[random_index:random_index+M]
yi = y[random_index:random_index+M]
gradients = (<span style="color: #B452CD">2.0</span>/M)* xi.T @ ((xi @ theta)-yi)
eta = learning_schedule(epoch*m+i)
theta = theta - eta*gradients
doc/pub/week40/html/week40-solarized.html
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@@ -594,9 +594,9 @@ theta = np.random.randn(<span style="color: #B452CD">2</span>,<span style="color
<span style="color: #8B008B; font-weight: bold">for</span> epoch <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(n_epochs):
<span style="color: #228B22"># Can you figure out a better way of setting up the contributions to each batch?</span>
<span style="color: #8B008B; font-weight: bold">for</span> i <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(m):
random_index = np.random.randint(m)
xi = X[random_index*M:random_index*M+M]
yi = y[random_index*M:random_index*M+M]
random_index = M*np.random.randint(m)
xi = X[random_index:random_index+M]
yi = y[random_index:random_index+M]
gradients = (<span style="color: #B452CD">2.0</span>/M)* xi.T @ ((xi @ theta)-yi)
eta = learning_schedule(epoch*m+i)
theta = theta - eta*gradients
doc/pub/week40/html/week40.html
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@@ -671,9 +671,9 @@ theta <span style="color: #666666">=</span> np<span style="color: #666666">.</sp
<span style="color: #008000; font-weight: bold">for</span> epoch <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(n_epochs):
<span style="color: #408080; font-style: italic"># Can you figure out a better way of setting up the contributions to each batch?</span>
<span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(m):
random_index <span style="color: #666666">=</span> np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>randint(m)
xi <span style="color: #666666">=</span> X[random_index<span style="color: #666666">*</span>M:random_index<span style="color: #666666">*</span>M<span style="color: #666666">+</span>M]
yi <span style="color: #666666">=</span> y[random_index<span style="color: #666666">*</span>M:random_index<span style="color: #666666">*</span>M<span style="color: #666666">+</span>M]
random_index <span style="color: #666666">=</span> M<span style="color: #666666">*</span>np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>randint(m)
xi <span style="color: #666666">=</span> X[random_index:random_index<span style="color: #666666">+</span>M]
yi <span style="color: #666666">=</span> y[random_index:random_index<span style="color: #666666">+</span>M]
gradients <span style="color: #666666">=</span> (<span style="color: #666666">2.0/</span>M)<span style="color: #666666">*</span> xi<span style="color: #666666">.</span>T <span style="color: #666666">@</span> ((xi <span style="color: #666666">@</span> theta)<span style="color: #666666">-</span>yi)
eta <span style="color: #666666">=</span> learning_schedule(epoch<span style="color: #666666">*</span>m<span style="color: #666666">+</span>i)
theta <span style="color: #666666">=</span> theta <span style="color: #666666">-</span> eta<span style="color: #666666">*</span>gradients
doc/pub/week40/ipynb/ipynb-week40-src.tar.gz
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doc/pub/week40/ipynb/week40.ipynb
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doc/src/week39/week39.do.txt
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@@ -1558,9 +1558,9 @@ theta = np.random.randn(2,1)
for epoch in range(n_epochs):
# Can you figure out a better way of setting up the contributions to each batch?
for i in range(m):
random_index = np.random.randint(m)
xi = X[random_index*M:random_index*M+M]
yi = y[random_index*M:random_index*M+M]
random_index = M*np.random.randint(m)
xi = X[random_index:random_index+M]
yi = y[random_index:random_index+M]
gradients = (2.0/M)* xi.T @ ((xi @ theta)-yi)
eta = learning_schedule(epoch*m+i)
theta = theta - eta*gradients
doc/src/week40/week40.do.txt
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@@ -279,9 +279,9 @@ theta = np.random.randn(2,1)
for epoch in range(n_epochs):
# Can you figure out a better way of setting up the contributions to each batch?
for i in range(m):
random_index = np.random.randint(m)
xi = X[random_index*M:random_index*M+M]
yi = y[random_index*M:random_index*M+M]
random_index = M*np.random.randint(m)
xi = X[random_index:random_index+M]
yi = y[random_index:random_index+M]
gradients = (2.0/M)* xi.T @ ((xi @ theta)-yi)
eta = learning_schedule(epoch*m+i)
theta = theta - eta*gradients