\r\n \r\n {citation} \r\n \r\n \r\n - \n
- Notebook 1.1 - Background mathematics:
- Notebook 2.1 - Supervised learning:
- Notebook 3.1 - Shallow networks I:
- Notebook 3.2 - Shallow networks II:
- Notebook 3.3 - Shallow network regions:
- Notebook 3.4 - Activation functions:
- Notebook 4.1 - Composing networks:
- Notebook 4.2 - Clipping functions:
- Notebook 4.3 - Deep networks:
- Notebook 5.1 - Least squares loss:
- Notebook 5.2 - Binary cross-entropy loss:
- Notebook 5.3 - Multiclass cross-entropy loss:
- Notebook 6.1 - Line search:
- Notebook 6.2 - Gradient descent:
- Notebook 6.3 - Stochastic gradient descent:
- Notebook 6.4 - Momentum:
- Notebook 6.5 - Adam:
- Notebook 7.1 - Backpropagation in toy model:
- Notebook 7.2 - Backpropagation:
- Notebook 7.3 - Initialization:
- Notebook 8.1 - MNIST-1D performance:
- Notebook 8.2 - Bias-variance trade-off:
- Notebook 8.3 - Double descent:
- Notebook 8.4 - High-dimensional spaces:
- Notebook 9.1 - L2 regularization:
- Notebook 9.2 - Implicit regularization:
- Notebook 9.3 - Ensembling:
- Notebook 9.4 - Bayesian approach:
- Notebook 9.5 - Augmentation
- Notebook 10.1 - 1D convolution:
- Notebook 10.2 - Convolution for MNIST-1D:
- Notebook 10.3 - 2D convolution:
- Notebook 10.4 - Downsampling & upsampling:
- Notebook 10.5 - Convolution for MNIST:
- \n
- Notebook 11.1 - Shattered gradients:
- Notebook 11.2 - Residual networks:
- Notebook 11.3 - Batch normalization:
- Notebook 12.1 - Self-attention:
- Notebook 12.2 - Multi-head self-attention:
- Notebook 12.3 - Tokenization:
- Notebook 12.4 - Decoding strategies:
- Notebook 13.1 - Encoding graphs:
- Notebook 13.2 - Graph classification :
- Notebook 13.3 - Neighborhood sampling:
- Notebook 13.4 - Graph attention:
- Notebook 15.1 - GAN toy example:
ipynb/colab \n - Notebook 15.2 - Wasserstein distance:
ipynb/colab \n - Notebook 16.1 - 1D normalizing flows:
ipynb/colab \n - Notebook 16.2 - Autoregressive flows:
ipynb/colab \n - Notebook 16.3 - Contraction mappings:
ipynb/colab \n - Notebook 17.1 - Latent variable models:
ipynb/colab \n - Notebook 17.2 - Reparameterization trick:
ipynb/colab \n - Notebook 17.3 - Importance sampling:
ipynb/colab \n - Notebook 18.1 - Diffusion encoder:
ipynb/colab \n - Notebook 18.2 - 1D diffusion model:
ipynb/colab \n - Notebook 18.3 - Reparameterized model:
ipynb/colab \n - Notebook 18.4 - Families of diffusion models:
ipynb/colab \n - Notebook 19.1 - Markov decision processes:
ipynb/colab \n - Notebook 19.2 - Dynamic programming:
ipynb/colab \n - Notebook 19.3 - Monte-Carlo methods:
ipynb/colab \n - Notebook 19.4 - Temporal difference methods:
ipynb/colab \n - Notebook 19.5 - Control variates:
ipynb/colab \n - Notebook 20.1 - Random data:
ipynb/colab \n - Notebook 20.2 - Full-batch gradient descent:
ipynb/colab \n - Notebook 20.3 - Lottery tickets:
ipynb/colab \n - Notebook 20.4 - Adversarial attacks:
ipynb/colab \n - Notebook 21.1 - Bias mitigation:
ipynb/colab \n - Notebook 21.2 - Explainability:
ipynb/colab \n
- \n
- Introduction
PPTX \n - Supervised Learning
PPTX \n - Shallow Neural Networks
PPTX \n - Deep Neural Networks
PPTX \n - Loss Functions
PPTX \n - Fitting Models
PPTX \n - Computing Gradients
PPTX \n - Initialization
PPTX \n - Performance
PPTX \n - Regularization
PPTX \n - Convolutional Networks
PPTX \n - Image Generation
PPTX \n - Transformers and LLMs
PPTX \n
- \n
- Introduction:
PDF /SVG /PPTX \n\n - Supervised learning:
PPTX \n - Shallow neural networks:
PPTX \n - Deep neural networks:
PPTX \n - Loss functions:
\n SVG\n / \n - Training models:
\n SVG\n / \n - Gradients and initialization:
PPTX \n - Measuring performance:
PPTX \n - Regularization:
\n SVG\n / \n - Convolutional networks:
PPTX \n - Residual networks:
PPTX \n - Transformers:
\n SVG / \n - Graph neural networks:
PPTX \n - Unsupervised learning:
\n PPTX \n - GANs:
PPTX \n - Normalizing flows:
PPTX \n - Variational autoencoders:
PPTX \n - Diffusion models:
\n PPTX \n - Deep reinforcement learning:
\n PPTX \n - Why does deep learning work?:
\n PPTX \n - Deep learning and ethics:
- Appendices -
PDF /\n SVG / \n
- \n
- Nature Machine Intelligence
review byGe Wang \n - Amazon
reviews \n - Goodreads
reviews \n - Book
review by Vishal V. \n
- \n
- Borealis AI
interview \n - Shepherd ML book
recommendations \n
-
-
-
- Introduction{" "}
-
- PPTX - -
- -
- Supervised Learning{" "}
-
- PPTX - -
- -
- Shallow Neural Networks{" "}
-
- PPTX - -
- -
- Deep Neural Networks{" "}
-
- PPTX - -
- -
- Loss Functions{" "}
-
- PPTX - -
- -
- Fitting Models{" "}
-
- PPTX - -
- -
- Computing Gradients{" "}
-
- PPTX - -
- -
- Initialization{" "}
-
- PPTX - -
- -
- Performance{" "}
-
- PPTX - -
- -
- Regularization{" "}
-
- PPTX - -
- -
- Convolutional Networks{" "}
-
- PPTX - -
- -
- Image Generation{" "}
-
- PPTX - -
- -
- Transformers and LLMs{" "}
-
- PPTX - -
+ {fullSlides.map((slide, index) => (
+ -
+ {slide.text}{" "}
+
+ PPTX + +
+ ))}
-
-
-
- {" "}
- Introduction:{" "}
-
- PDF - {" "} - /{" "} -- {" "} - SVG - {" "} - /{" "} -- PPTX{" "} - -
-
- -
- {" "}
- Supervised learning:{" "}
-
- PDF - {" "} - /{" "} -- {" "} - SVG - {" "} - /{" "} -- PPTX - -
- -
- {" "}
- Shallow neural networks:{" "}
-
- PDF - {" "} - /{" "} -- {" "} - SVG - {" "} - /{" "} -- PPTX - -
- -
- {" "}
- Deep neural networks:{" "}
-
- PDF - {" "} - /{" "} -- {" "} - SVG - - / -- PPTX - -
- -
- {" "}
- Loss functions:{" "}
-
- PDF - {" "} - /{" "} -- SVG - {" "} - /{" "} -- PPTX - -
- -
- {" "}
- Training models:{" "}
-
- PDF - {" "} - /{" "} -- SVG - {" "} - /{" "} -- PPTX - -
- -
- {" "}
- Gradients and initialization:{" "}
-
- PDF - {" "} - /{" "} -- {" "} - SVG - {" "} - /{" "} -- PPTX - -
- -
- {" "}
- Measuring performance:{" "}
-
- PDF - {" "} - /{" "} -- {" "} - SVG - {" "} - /{" "} -- PPTX - -
- -
- {" "}
- Regularization:{" "}
-
- PDF - {" "} - /{" "} -- SVG - {" "} - /{" "} -- PPTX - -
- -
- {" "}
- Convolutional networks:{" "}
-
- PDF - {" "} - /{" "} -- {" "} - SVG - {" "} - /{" "} -- PPTX - -
- -
- {" "}
- Residual networks:{" "}
-
- PDF - {" "} - /{" "} -- {" "} - SVG - {" "} - /{" "} -- PPTX - -
- -
- {" "}
- Transformers:{" "}
-
- PDF - {" "} - /{" "} -- SVG - {" "} - /{" "} -- PPTX - -
- -
- {" "}
- Graph neural networks:{" "}
-
- PDF - {" "} - /{" "} -- {" "} - SVG - {" "} - /{" "} -- PPTX - -
- -
- {" "}
- Unsupervised learning:{" "}
-
- PDF - {" "} - /{" "} -- {" "} - SVG - {" "} - /{" "} -- PPTX - -
- -
- {" "}
- GANs:{" "}
-
- PDF - {" "} - /{" "} -- {" "} - SVG - {" "} - /{" "} -- PPTX - -
- -
- {" "}
- Normalizing flows:{" "}
-
- PDF - {" "} - /{" "} -- {" "} - SVG - {" "} - /{" "} -- PPTX - -
- -
- {" "}
- Variational autoencoders:{" "}
-
- PDF - {" "} - /{" "} -- {" "} - SVG - {" "} - /{" "} -- PPTX - -
- -
- {" "}
- Diffusion models:{" "}
-
- PDF - {" "} - /{" "} -- {" "} - SVG - {" "} - / -- PPTX - -
- -
- {" "}
- Deep reinforcement learning:{" "}
-
- PDF - {" "} - /{" "} -- {" "} - SVG - {" "} - /{" "} -- PPTX{" "} - -
- -
- {" "}
- Why does deep learning work?:{" "}
-
- PDF - {" "} - /{" "} -- {" "} - SVG - {" "} - /{" "} -- PPTX - -
- -
- {" "}
- Deep learning and ethics:{" "}
-
- PDF - {" "} - /{" "} -- SVG - {" "} - /{" "} -- PPTX - -
- -
- {" "}
- Appendices -{" "}
-
- PDF - {" "} - /{" "} -- SVG - {" "} - /{" "} -- PPTX - -
+ {figures.map((figure, index) => (
+ -
+ {figure.text}:{" "}
+
+ PDF + {" "} + /{" "} ++ {" "} + SVG + {" "} + /{" "} ++ PPTX{" "} + +
+ ))}
-
- {" "}
Nature Machine Intelligence{" "}
{" "} @@ -88,9 +100,7 @@ export default function MediaSection() { Ge Wang
-
-
- {" "}
Amazon{" "}
reviews @@ -110,7 +120,6 @@ export default function MediaSection() { by Vishal V. -
- {" "}
Amazon{" "}
reviews @@ -136,18 +145,14 @@ export default function MediaSection() { Interviews -
-
-
- Borealis AI{" "}
-
- interview - -
- -
- Shepherd ML book{" "}
-
- recommendations - -
+ {interviews.map((interview, index) => (
+ -
+ {interview.text}{" "}
+
+ {interview.linkText} + +
+ ))}
Other articles, blogs, and books that I have written. Most in a similar style and using the same notation as Understanding Deep - Learning.{" "} + Learning. -
- Borealis AI{" "}
-
-
-
- Notebook 1.1 - Background mathematics:
ipynb/colab -
- - Notebook 2.1 - Supervised learning:
ipynb/colab -
- - Notebook 3.1 - Shallow networks I:
ipynb/colab -
- - Notebook 3.2 - Shallow networks II:
ipynb/colab -
- - Notebook 3.3 - Shallow network regions:
ipynb/colab -
- - Notebook 3.4 - Activation functions:
ipynb/colab -
- - Notebook 4.1 - Composing networks:
ipynb/colab -
- - Notebook 4.2 - Clipping functions:
ipynb/colab -
- - Notebook 4.3 - Deep networks:
ipynb/colab -
- - Notebook 5.1 - Least squares loss:
ipynb/colab -
- - Notebook 5.2 - Binary cross-entropy loss:
ipynb/colab -
- - Notebook 5.3 - Multiclass cross-entropy loss:
ipynb/colab -
- - Notebook 6.1 - Line search:
ipynb/colab -
- - Notebook 6.2 - Gradient descent:
ipynb/colab -
- - Notebook 6.3 - Stochastic gradient descent:
ipynb/colab -
- - Notebook 6.4 - Momentum:
ipynb/colab -
- - Notebook 6.5 - Adam:
ipynb/colab -
- - Notebook 7.1 - Backpropagation in toy model:
ipynb/colab -
- - Notebook 7.2 - Backpropagation:
ipynb/colab -
- - Notebook 7.3 - Initialization:
ipynb/colab -
- - Notebook 8.1 - MNIST-1D performance:
ipynb/colab -
- - Notebook 8.2 - Bias-variance trade-off:
ipynb/colab -
- - Notebook 8.3 - Double descent:
ipynb/colab -
- - Notebook 8.4 - High-dimensional spaces:
ipynb/colab -
- - Notebook 9.1 - L2 regularization:
ipynb/colab -
- - Notebook 9.2 - Implicit regularization:
ipynb/colab -
- - Notebook 9.3 - Ensembling:
ipynb/colab -
- - Notebook 9.4 - Bayesian approach:
ipynb/colab -
- - Notebook 9.5 - Augmentation
ipynb/colab -
- - Notebook 10.1 - 1D convolution:
ipynb/colab -
- - Notebook 10.2 - Convolution for MNIST-1D:
ipynb/colab -
- - Notebook 10.3 - 2D convolution:
ipynb/colab -
- - Notebook 10.4 - Downsampling & upsampling:
ipynb/colab -
- - Notebook 10.5 - Convolution for MNIST:
ipynb/colab -
+ {/* render first half of notebooks*/}
+ {notebooks.slice(0, notebooks.length / 2).map((notebook, index) => (
+ -
+ {notebook.text}:{" "}
+
ipynb/colab +
+ ))}
-
-
- Notebook 11.1 - Shattered gradients:
ipynb/colab -
- - Notebook 11.2 - Residual networks:
ipynb/colab -
- - Notebook 11.3 - Batch normalization:
ipynb/colab -
- - Notebook 12.1 - Self-attention:
ipynb/colab -
- - Notebook 12.2 - Multi-head self-attention:
ipynb/colab -
- - Notebook 12.3 - Tokenization:
ipynb/colab -
- - Notebook 12.4 - Decoding strategies:
ipynb/colab -
- - Notebook 13.1 - Encoding graphs:
ipynb/colab -
- - Notebook 13.2 - Graph classification :
ipynb/colab -
- - Notebook 13.3 - Neighborhood sampling:
ipynb/colab -
- - Notebook 13.4 - Graph attention:
ipynb/colab -
- - Notebook 15.1 - GAN toy example:
ipynb/colab
- - Notebook 15.2 - Wasserstein distance:
ipynb/colab
- - Notebook 16.1 - 1D normalizing flows:
ipynb/colab
- - Notebook 16.2 - Autoregressive flows:
ipynb/colab
- - Notebook 16.3 - Contraction mappings:
ipynb/colab
- - Notebook 17.1 - Latent variable models:
ipynb/colab
- - Notebook 17.2 - Reparameterization trick:
ipynb/colab
- - Notebook 17.3 - Importance sampling:
ipynb/colab
- - Notebook 18.1 - Diffusion encoder:
ipynb/colab
- - Notebook 18.2 - 1D diffusion model:
ipynb/colab
- - Notebook 18.3 - Reparameterized model:
ipynb/colab
- - Notebook 18.4 - Families of diffusion models:
ipynb/colab
- - Notebook 19.1 - Markov decision processes:
ipynb/colab
- - Notebook 19.2 - Dynamic programming:
ipynb/colab
- - Notebook 19.3 - Monte-Carlo methods:
ipynb/colab
- - Notebook 19.4 - Temporal difference methods:
ipynb/colab
- - Notebook 19.5 - Control variates:
ipynb/colab
- - Notebook 20.1 - Random data:
ipynb/colab
- - Notebook 20.2 - Full-batch gradient descent:
ipynb/colab
- - Notebook 20.3 - Lottery tickets:
ipynb/colab
- - Notebook 20.4 - Adversarial attacks:
ipynb/colab
- - Notebook 21.1 - Bias mitigation:
ipynb/colab
- - Notebook 21.2 - Explainability:
ipynb/colab
+ -
+ {notebook.text}:{" "}
+
ipynb/colab +
+ ))}
-
+ {/* render second half of notebooks*/}
+ {notebooks.slice(notebooks.length / 2).map((notebook, index) => (
+