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W1D2T1 & Mentorship Program #377

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Jul 16, 2024
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W1D2T1 & Mentorship Program #377

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80d3aab
cpu remove
glibesyck Jul 16, 2024
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glibesyck Jul 16, 2024
393934c
Process tutorial notebooks
actions-user Jul 16, 2024
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5 changes: 1 addition & 4 deletions projects/professional_development/mentorship_program.md
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Expand Up @@ -38,8 +38,5 @@ I’m a student, how can I make the most out of my mentoring experience?

**Keep a respectful conversation**: mentoring is a social interaction. You may construct a long-lasting relationship with your mentor and your peers. Thus, be kind and have fun, as you’ll be discussing your dreams and career goals, as well as the efforts you’ve made so far to follow them.

---

# Find additional Professional Development resources through our website

https://neuromatch.io/resources/
**Find additional Professional Development resources through [our website](https://neuromatch.io/resources/)**
17 changes: 7 additions & 10 deletions tutorials/W1D2_ComparingTasks/W1D2_Tutorial1.ipynb
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Expand Up @@ -49,8 +49,6 @@
"2. Train a network to accomplish these tasks and compare the performance of these networks.\n",
"3. Measure how well different representations generalize\n",
"\n",
"**Important note**: this tutorial leverages GPU acceleration. Using a GPU runtime in colab will make the the tutorial run 10x faster.\n",
"\n",
"Let's get started.\n"
]
},
Expand Down Expand Up @@ -1396,8 +1394,8 @@
" for batch_idx, (X, y) in enumerate(sampled_val_loader):\n",
" predictions = model(X)\n",
" _, predicted_classes = torch.max(predictions, 1)\n",
" all_preds.extend(predicted_classes.cpu().numpy())\n",
" all_labels.extend(y.cpu().numpy())\n",
" all_preds.extend(predicted_classes.numpy())\n",
" all_labels.extend(y.numpy())\n",
"\n",
" #Compute confusion matrix\n",
" conf_matrix = confusion_matrix(all_labels, all_preds)\n",
Expand Down Expand Up @@ -1570,8 +1568,7 @@
"\n",
"- $N$ is the number of samples\n",
"- $y_i$ is the true label for the $i^{th}$ sample, the number of on pixels\n",
"- $\\mu_i = \\text{CNN}(x_i)$ is the output of the model for the $i^{th}$ sample\n",
"- $f$ is the ReLU activation function"
"- $\\mu_i = \\text{CNN}(x_i)$ is the output of the model for the $i^{th}$ sample"
]
},
{
Expand Down Expand Up @@ -2148,9 +2145,9 @@
" for batch_idx, (X, _) in enumerate(sampled_val_loader):\n",
" if batch_idx == 0:\n",
" outputs = model(X)\n",
" orig = X.cpu().numpy()\n",
" orig = X.numpy()\n",
" original_images.extend(orig)\n",
" recon = outputs.cpu().numpy()\n",
" recon = outputs.numpy()\n",
" reconstructed_images.extend(recon)\n",
" plot_reconstructions(original_images, reconstructed_images, N_train_data, epochs_max_autoencoder)\n",
" break\n",
Expand Down Expand Up @@ -2496,9 +2493,9 @@
" for batch_idx, (X, _) in enumerate(sampled_val_loader):\n",
" if batch_idx == 0: # Only visualize the first batch for simplicity\n",
" outputs = model(X)\n",
" orig = X.cpu().numpy()\n",
" orig = X.numpy()\n",
" original_images.extend(orig)\n",
" recon = outputs.cpu().numpy()\n",
" recon = outputs.numpy()\n",
" reconstructed_images.extend(recon)\n",
" fig = plt.figure(figsize=(8, 4))\n",
" rows, cols = 2, 6\n",
Expand Down
17 changes: 7 additions & 10 deletions tutorials/W1D2_ComparingTasks/instructor/W1D2_Tutorial1.ipynb
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Expand Up @@ -49,8 +49,6 @@
"2. Train a network to accomplish these tasks and compare the performance of these networks.\n",
"3. Measure how well different representations generalize\n",
"\n",
"**Important note**: this tutorial leverages GPU acceleration. Using a GPU runtime in colab will make the the tutorial run 10x faster.\n",
"\n",
"Let's get started.\n"
]
},
Expand Down Expand Up @@ -1398,8 +1396,8 @@
" for batch_idx, (X, y) in enumerate(sampled_val_loader):\n",
" predictions = model(X)\n",
" _, predicted_classes = torch.max(predictions, 1)\n",
" all_preds.extend(predicted_classes.cpu().numpy())\n",
" all_labels.extend(y.cpu().numpy())\n",
" all_preds.extend(predicted_classes.numpy())\n",
" all_labels.extend(y.numpy())\n",
"\n",
" #Compute confusion matrix\n",
" conf_matrix = confusion_matrix(all_labels, all_preds)\n",
Expand Down Expand Up @@ -1572,8 +1570,7 @@
"\n",
"- $N$ is the number of samples\n",
"- $y_i$ is the true label for the $i^{th}$ sample, the number of on pixels\n",
"- $\\mu_i = \\text{CNN}(x_i)$ is the output of the model for the $i^{th}$ sample\n",
"- $f$ is the ReLU activation function"
"- $\\mu_i = \\text{CNN}(x_i)$ is the output of the model for the $i^{th}$ sample"
]
},
{
Expand Down Expand Up @@ -2154,9 +2151,9 @@
" for batch_idx, (X, _) in enumerate(sampled_val_loader):\n",
" if batch_idx == 0:\n",
" outputs = model(X)\n",
" orig = X.cpu().numpy()\n",
" orig = X.numpy()\n",
" original_images.extend(orig)\n",
" recon = outputs.cpu().numpy()\n",
" recon = outputs.numpy()\n",
" reconstructed_images.extend(recon)\n",
" plot_reconstructions(original_images, reconstructed_images, N_train_data, epochs_max_autoencoder)\n",
" break\n",
Expand Down Expand Up @@ -2502,9 +2499,9 @@
" for batch_idx, (X, _) in enumerate(sampled_val_loader):\n",
" if batch_idx == 0: # Only visualize the first batch for simplicity\n",
" outputs = model(X)\n",
" orig = X.cpu().numpy()\n",
" orig = X.numpy()\n",
" original_images.extend(orig)\n",
" recon = outputs.cpu().numpy()\n",
" recon = outputs.numpy()\n",
" reconstructed_images.extend(recon)\n",
" fig = plt.figure(figsize=(8, 4))\n",
" rows, cols = 2, 6\n",
Expand Down
17 changes: 7 additions & 10 deletions tutorials/W1D2_ComparingTasks/student/W1D2_Tutorial1.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -49,8 +49,6 @@
"2. Train a network to accomplish these tasks and compare the performance of these networks.\n",
"3. Measure how well different representations generalize\n",
"\n",
"**Important note**: this tutorial leverages GPU acceleration. Using a GPU runtime in colab will make the the tutorial run 10x faster.\n",
"\n",
"Let's get started.\n"
]
},
Expand Down Expand Up @@ -1390,8 +1388,8 @@
" for batch_idx, (X, y) in enumerate(sampled_val_loader):\n",
" predictions = model(X)\n",
" _, predicted_classes = torch.max(predictions, 1)\n",
" all_preds.extend(predicted_classes.cpu().numpy())\n",
" all_labels.extend(y.cpu().numpy())\n",
" all_preds.extend(predicted_classes.numpy())\n",
" all_labels.extend(y.numpy())\n",
"\n",
" #Compute confusion matrix\n",
" conf_matrix = confusion_matrix(all_labels, all_preds)\n",
Expand Down Expand Up @@ -1556,8 +1554,7 @@
"\n",
"- $N$ is the number of samples\n",
"- $y_i$ is the true label for the $i^{th}$ sample, the number of on pixels\n",
"- $\\mu_i = \\text{CNN}(x_i)$ is the output of the model for the $i^{th}$ sample\n",
"- $f$ is the ReLU activation function"
"- $\\mu_i = \\text{CNN}(x_i)$ is the output of the model for the $i^{th}$ sample"
]
},
{
Expand Down Expand Up @@ -2118,9 +2115,9 @@
" for batch_idx, (X, _) in enumerate(sampled_val_loader):\n",
" if batch_idx == 0:\n",
" outputs = model(X)\n",
" orig = X.cpu().numpy()\n",
" orig = X.numpy()\n",
" original_images.extend(orig)\n",
" recon = outputs.cpu().numpy()\n",
" recon = outputs.numpy()\n",
" reconstructed_images.extend(recon)\n",
" plot_reconstructions(original_images, reconstructed_images, N_train_data, epochs_max_autoencoder)\n",
" break\n",
Expand Down Expand Up @@ -2459,9 +2456,9 @@
" for batch_idx, (X, _) in enumerate(sampled_val_loader):\n",
" if batch_idx == 0: # Only visualize the first batch for simplicity\n",
" outputs = model(X)\n",
" orig = X.cpu().numpy()\n",
" orig = X.numpy()\n",
" original_images.extend(orig)\n",
" recon = outputs.cpu().numpy()\n",
" recon = outputs.numpy()\n",
" reconstructed_images.extend(recon)\n",
" fig = plt.figure(figsize=(8, 4))\n",
" rows, cols = 2, 6\n",
Expand Down