added colab links

Ch02_DNN_regression/README.md

@@ -21,22 +21,22 @@ ISBN-13: 9781718503922
 2. **Dense Neural Networks for Regression**  
    Explores regression problems and digital twins, focusing on continuous-value prediction with multi-layer networks.
 
->   - [**Code 2-1: Regressing 1D Data with a Single Neuron**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch02_DNN_regression/ec02_1_neuron_reg_1d/neuron_reg_1d.ipynb)  
+>   - [**Code 2-1: Regressing 1D Data with a Single Neuron**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch02_DNN_regression/ec02_1_neuron_reg_1d/neuron_reg_1d.ipynb) <a href="https://colab.research.google.com/github/DeepTrackAI/DeepLearningCrashCourse/blob/main/Ch02_DNN_regression/ec02_1_neuron_reg_1d/neuron_reg_1d.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>  
 >     Implements a single neuron to perform linear regression on 1D data. The example introduces weight updates using stochastic gradient descent to minimize prediction errors.
 >
->   - [**Code 2-2: Regressing 2D Data with a Single Neuron**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch02_DNN_regression/ec02_2_neuron_reg_2d/neuron_reg_2d.ipynb)  
+>   - [**Code 2-2: Regressing 2D Data with a Single Neuron**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch02_DNN_regression/ec02_2_neuron_reg_2d/neuron_reg_2d.ipynb) <a href="https://colab.research.google.com/github/DeepTrackAI/DeepLearningCrashCourse/blob/main/Ch02_DNN_regression/ec02_2_neuron_reg_2d/neuron_reg_2d.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>  
 >     Extends the single-neuron approach to multidimensional data, demonstrating the scalar product between input and weights to predict outputs in 2D data.
 >
->   - [**Code 2-3: Regressing 2D Data with a Two-Layer Neural Network**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch02_DNN_regression/ec02_3_dnn2_reg/dnn2_reg.ipynb)  
+>   - [**Code 2-3: Regressing 2D Data with a Two-Layer Neural Network**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch02_DNN_regression/ec02_3_dnn2_reg/dnn2_reg.ipynb) <a href="https://colab.research.google.com/github/DeepTrackAI/DeepLearningCrashCourse/blob/main/Ch02_DNN_regression/ec02_3_dnn2_reg/dnn2_reg.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>  
 >     Explores two-layer dense neural networks to model linear functions in 2D data using backpropagation for weight updates and training.
 >
->   - [**Code 2-4: Fitting Data with a Neural Network Trained Using Batch Training**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch02_DNN_regression/ec02_4_dnn2_reg_batches/dnn2_reg_batches.ipynb)  
+>   - [**Code 2-4: Fitting Data with a Neural Network Trained Using Batch Training**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch02_DNN_regression/ec02_4_dnn2_reg_batches/dnn2_reg_batches.ipynb) <a href="https://colab.research.google.com/github/DeepTrackAI/DeepLearningCrashCourse/blob/main/Ch02_DNN_regression/ec02_4_dnn2_reg_batches/dnn2_reg_batches.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>  
 >     Demonstrates mini-batch training for 2D regression tasks, incorporating batch randomization to improve stability and avoid local minima.
 >
->   - [**Code 2-5: Training a Neural Network Splitting the Data**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch02_DNN_regression/ec02_5_dnn2_reg_split/dnn2_reg_split.ipynb)  
+>   - [**Code 2-5: Training a Neural Network Splitting the Data**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch02_DNN_regression/ec02_5_dnn2_reg_split/dnn2_reg_split.ipynb) <a href="https://colab.research.google.com/github/DeepTrackAI/DeepLearningCrashCourse/blob/main/Ch02_DNN_regression/ec02_5_dnn2_reg_split/dnn2_reg_split.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>  
 >     Divides data into training, validation, and test sets to train and validate a two-layer network, monitoring metrics to identify overfitting.
 >
->   - [**Code 2-A: Simulating the Forces Acting on an Optically Trapped Particle**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch02_DNN_regression/ec02_A_optical_forces/optical_forces.ipynb)  
+>   - [**Code 2-A: Simulating the Forces Acting on an Optically Trapped Particle**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch02_DNN_regression/ec02_A_optical_forces/optical_forces.ipynb) <a href="https://colab.research.google.com/github/DeepTrackAI/DeepLearningCrashCourse/blob/main/Ch02_DNN_regression/ec02_A_optical_forces/optical_forces.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>  
 >     Uses a dense neural network to emulate optical forces on a microsphere in optical tweezers, creating a digital twin to model real-world systems efficiently.
 
 3. [Convolutional Neural Networks for Image Analysis](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch03_CNN)  

Ch03_CNN/README.md

@@ -23,19 +23,19 @@ ISBN-13: 9781718503922
 3. **Convolutional Neural Networks for Image Analysis**  
    Covers convolutional neural networks (CNNs) and their application to tasks such as image classification, localization, style transfer, and DeepDream.
 
->   - [**Code 3-1: Implementing Neural Networks in PyTorch**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch03_CNN/ec03_1_cnn/cnn.ipynb)  
+>   - [**Code 3-1: Implementing Neural Networks in PyTorch**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch03_CNN/ec03_1_cnn/cnn.ipynb) <a href="https://colab.research.google.com/github/DeepTrackAI/DeepLearningCrashCourse/blob/main/Ch03_CNN/ec03_1_cnn/cnn.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>  
 >     Demonstrates the basics of convolutional neural networks, including defining convolutional layers, activation functions (ReLU), pooling/upsampling layers, and stacking them into a deeper architecture for image transformation or classification tasks. This notebook also illustrates how to use PyTorch in general.
 >     
->   - [**Code 3-A: Classifying Blood Smears with a Convolutional Neural Network**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch03_CNN/ec03_A_blood_smears/blood_smears.ipynb)  
+>   - [**Code 3-A: Classifying Blood Smears with a Convolutional Neural Network**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch03_CNN/ec03_A_blood_smears/blood_smears.ipynb) <a href="https://colab.research.google.com/github/DeepTrackAI/DeepLearningCrashCourse/blob/main/Ch03_CNN/ec03_A_blood_smears/blood_smears.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>  
 >     Walks you through loading a malaria dataset, preprocessing images, and training a CNN to distinguish parasitized from uninfected blood cells. It also shows how to generate training logs, measure accuracy, plot ROC curves, and visualize CNN heatmaps and activations to confirm the network’s attention on infected regions.
 >
->   - [**Code 3-B: Localizing Microscopic Particles with a Convolutional Neural Network**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch03_CNN/ec03_B_particle_localization/particle_localization.ipynb)  
+>   - [**Code 3-B: Localizing Microscopic Particles with a Convolutional Neural Network**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch03_CNN/ec03_B_particle_localization/particle_localization.ipynb) <a href="https://colab.research.google.com/github/DeepTrackAI/DeepLearningCrashCourse/blob/main/Ch03_CNN/ec03_B_particle_localization/particle_localization.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>  
 >     Focuses on regression tasks (rather than classification) by predicting the (x,y) position of a trapped microparticle in noisy microscope images. It demonstrates how to manually annotate data or use simulated data for training, and includes hooking into intermediate activations to understand how the network learns positional features.
 >
->   - [**Code 3-C: Creating DeepDreams**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch03_CNN/ec03_C_deepdream/deepdream.ipynb)  
+>   - [**Code 3-C: Creating DeepDreams**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch03_CNN/ec03_C_deepdream/deepdream.ipynb) <a href="https://colab.research.google.com/github/DeepTrackAI/DeepLearningCrashCourse/blob/main/Ch03_CNN/ec03_C_deepdream/deepdream.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>  
 >     Uses a pre-trained VGG16 model to generate surreal DeepDream visuals, where an image is iteratively adjusted via gradient ascent to amplify the features that specific CNN layers have learned. It implements forward hooks to capture layer activations and shows how to produce dream-like images revealing what the model sees.
 >
->   - [**Code 3-D: Transferring Image Styles**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch03_CNN/ec03_D_style_transfer/style_transfer.ipynb)  
+>   - [**Code 3-D: Transferring Image Styles**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch03_CNN/ec03_D_style_transfer/style_transfer.ipynb) <a href="https://colab.research.google.com/github/DeepTrackAI/DeepLearningCrashCourse/blob/main/Ch03_CNN/ec03_D_style_transfer/style_transfer.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>  
 >     Implements neural style transfer, blending the higher-level content of one image with the lower-level textures and brush strokes of another (for example, re-painting a microscopy image in the style of a famous artwork). It demonstrates the use of Gram matrices, L-BFGS optimization, and carefully chosen layers to balance content and style.
 
 4. [Encoders–Decoders for Latent Space Manipulation](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch04_AE)  

Ch04_AE/README.md

@@ -25,16 +25,16 @@ ISBN-13: 9781718503922
 4. **Encoders–Decoders for Latent Space Manipulation**  
    Focuses on autoencoders, variational autoencoders, Wasserstein autoencoders, and anomaly detection, enabling data compression and generation.
 
->   - [**Code 4-1: Denoising Images with a Denoising Encoder-Decoder**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch04_AE/ec04_1_denoising/denoising.ipynb)  
+>   - [**Code 4-1: Denoising Images with a Denoising Encoder-Decoder**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch04_AE/ec04_1_denoising/denoising.ipynb) <a href="https://colab.research.google.com/github/DeepTrackAI/DeepLearningCrashCourse/blob/main/Ch04_AE/ec04_1_denoising/denoising.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>  
 >     Implements a denoising encoder–decoder that cleans noisy microscopy images. It illustrates how to simulate noisy vs. clean pairs, train a small convolutional model to learn noise removal, and verify that the network isn’t simply memorizing one output (checking for mode collapse).
 >
->   - [**Code 4-A: Generating Digit Images with Variational Autoencoders**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch04_AE/ec04_A_vae_mnist/vae_mnist.ipynb)  
+>   - [**Code 4-A: Generating Digit Images with Variational Autoencoders**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch04_AE/ec04_A_vae_mnist/vae_mnist.ipynb) <a href="https://colab.research.google.com/github/DeepTrackAI/DeepLearningCrashCourse/blob/main/Ch04_AE/ec04_A_vae_mnist/vae_mnist.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>  
 >     Shows how to implement a variational autoencoder (VAE) on MNIST digits. The encoder outputs a mean and variance for latent variables, and the decoder reconstructs digits from sampled latent points. The notebook demonstrates random sampling to create new digit images and visualizes how the VAE organizes digits in the latent space.
 >
->   - [**Code 4-B: Morphing Images with Wasserstein Autoencoders**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch04_AE/ec04_B_wae_fashionmnist/wae_fashionmnist.ipynb)  
+>   - [**Code 4-B: Morphing Images with Wasserstein Autoencoders**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch04_AE/ec04_B_wae_fashionmnist/wae_fashionmnist.ipynb) <a href="https://colab.research.google.com/github/DeepTrackAI/DeepLearningCrashCourse/blob/main/Ch04_AE/ec04_B_wae_fashionmnist/wae_fashionmnist.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>  
 >     Explores a Wasserstein autoencoder (WAE) trained on Fashion-MNIST. You’ll see how to decode random latent points to generate clothing/accessory images and how to interpolate (morph) one image into another in latent space, showcasing the smooth transitions that WAEs learn.
 >
->   - [**Code 4-C: Detecting ECG Anomalies with an Autoencoder**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch04_AE/ec04_C_anomaly_detection/anomaly_detection.ipynb)  
+>   - [**Code 4-C: Detecting ECG Anomalies with an Autoencoder**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch04_AE/ec04_C_anomaly_detection/anomaly_detection.ipynb) <a href="https://colab.research.google.com/github/DeepTrackAI/DeepLearningCrashCourse/blob/main/Ch04_AE/ec04_C_anomaly_detection/anomaly_detection.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>  
 >     Uses a 1D convolutional autoencoder to detect abnormal heartbeats in ECG data. It’s trained exclusively on normal ECGs so that reconstruction error (or distance in latent space) highlights anomalies that deviate from learned “normal” patterns. Demonstrates how to set a threshold for detecting anomalous signals and compares two approaches: reconstruction-based vs. neighbor-based in latent space.
 
 5. [U-Nets for Image Transformation](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch05_UNet)  

Ch05_UNet/README.md

@@ -27,13 +27,13 @@ ISBN-13: 9781718503922
 5. **U-Nets for Image Transformation**  
    Discusses U-Net architectures for image segmentation, cell counting, and various biomedical imaging applications.
 
->   - [**Code 5-1: Segmenting Biological Tissue Images with a U-Net**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch05_UNet/ec05_1_unet/unet.ipynb)  
+>   - [**Code 5-1: Segmenting Biological Tissue Images with a U-Net**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch05_UNet/ec05_1_unet/unet.ipynb) <a href="https://colab.research.google.com/github/DeepTrackAI/DeepLearningCrashCourse/blob/main/Ch05_UNet/ec05_1_unet/unet.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>  
 >     Demonstrates how to build and train a U-Net to segment internal cell structures (for example, mitochondria) in electron micrographs. It covers creating pipelines for raw images and labeled masks, using skip connections for detail retention, applying early stopping to avoid overfitting, and evaluating performance via the Jaccard Index (IoU). The notebook also demonstrates data augmentation to improve segmentation robustness.
 >
->   - [**Code 5-A: Detecting Quantum Dots in Fluorescence Images with a U-Net**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch05_UNet/ec05_A_qdots_localization/qdots_localization.ipynb)  
+>   - [**Code 5-A: Detecting Quantum Dots in Fluorescence Images with a U-Net**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch05_UNet/ec05_A_qdots_localization/qdots_localization.ipynb) <a href="https://colab.research.google.com/github/DeepTrackAI/DeepLearningCrashCourse/blob/main/Ch05_UNet/ec05_A_qdots_localization/qdots_localization.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>  
 >     Uses a U-Net to localize fluorescent quantum dots in noisy microscopy images. It simulates realistic training data with random positions, intensities, and added noise, and pairs them with masks indicating quantum dot locations. After training on these simulations, the U-Net is tested on real experimental images. You’ll see how accurately it can mark quantum dots by generating centroid-based masks.
 >
->   - [**Code 5-B: Counting Cells with a U-Net**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch05_UNet/ec05_B_cell_counting/cell_counting.ipynb)  
+>   - [**Code 5-B: Counting Cells with a U-Net**](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch05_UNet/ec05_B_cell_counting/cell_counting.ipynb) <a href="https://colab.research.google.com/github/DeepTrackAI/DeepLearningCrashCourse/blob/main/Ch05_UNet/ec05_B_cell_counting/cell_counting.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>  
 >     Applies a U-Net to create binary masks of cell nuclei, then uses connected-component labeling to count how many nuclei the mask contains. After simulating or loading real images of stained nuclei, the notebook trains a single-channel output U-Net using a binary cross-entropy loss. Accuracy is measured by comparing predicted cell counts with ground truth, reporting mean absolute and percentage errors. This pipeline automates cell counting and quantifies how close the predictions are to actual counts.
 
 6. [Self-Supervised Learning to Exploit Symmetries](https://github.com/DeepTrackAI/DeepLearningCrashCourse/tree/main/Ch06_SelfSupervised)