Standard 12 stops to get here

ResNet

Residual Network - a CNN architecture using skip connections to enable training of very deep networks (up to 1000+ layers).

Your route here

12 stops · basics first
  1. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  2. Neural Network ✓ understood

    A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.

  3. Convolution ✓ understood

    A mathematical operation that applies filters/kernels to input data to extract features like edges, textures, and patterns.

  4. Convolutional Neural Network ✓ understood

    A neural network that scans images with small learned filters, reusing the same weights at every position to build up from edges to whole objects.

  5. Activation Function ✓ understood

    A non-linear function applied to neuron outputs that introduces non-linearity, enabling networks to learn complex patterns.

  6. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  7. Training ✓ understood

    The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

  8. Loss Function ✓ understood

    A function that scores how wrong a model's prediction is as a single number, which training then works to make as small as possible.

  9. Gradient Descent ✓ understood

    An optimization method that repeatedly moves a model's parameters a small step in the direction that most reduces the loss.

  10. Backpropagation ✓ understood

    The algorithm for computing gradients of the loss with respect to network weights, enabling training through gradient descent.

  11. Vanishing Gradient ✓ understood

    A problem where gradients become extremely small during backpropagation, preventing deep networks from learning effectively.

  12. Residual Connection ✓ understood

    Skip connections that allow gradients to flow directly through a network, enabling training of very deep networks (ResNet).

  13. ResNet · you are here ✓ understood

Where it sits

Before this

Convolutional Neural Network Residual Connection
ResNet

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Vision & Multimodal VGG A CNN architecture known for its simplicity, using small 3x3 convolutions stacked deeply, influential in computer vision. Vision & Multimodal Inception A CNN architecture (GoogLeNet) using parallel convolutions of different sizes to capture multi-scale features efficiently. Vision & Multimodal EfficientNet A family of CNNs that scale depth, width, and resolution simultaneously using compound scaling for optimal efficiency. Vision & Multimodal Image Classification Assigning a single label or category to an entire image, a fundamental computer vision task. Vision & Multimodal U-Net A CNN architecture with encoder-decoder structure and skip connections, widely used for image segmentation tasks.