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It\ncontains the \"hello, world\" of machine learning programs: [[https://en.wikipedia.org/wiki/MNIST_database#:~:text=The%20MNIST%20database%20(Modified%20National,training%20various%20image%20processing%20systems.][MNIST]] handwritten\nimage classification. High-level frameworks like Keras make it easy to build a\nmodel with 97% accuracy in under a hundred lines of code. \n** Learning Goals\n- [X] Understand [[https://youtu.be/VMj-3S1tku0?si=DP_3O9cJaTAkWWzN][neural networks]] at a [[https://youtu.be/aircAruvnKk?si=3B7fAkIg_L13WQvo][high-level]]\n  - [X] Neurons modeled as a mathematical / functional graph\n  - [X] Representation as [[https://www.khanacademy.org/math/linear-algebra/vectors-and-spaces/linear-combinations/v/linear-combinations-and-span][linear combinations]] and basic [[https://youtu.be/fNk_zzaMoSs?si=RyxJYfhLcry4wyww][linear algebra]]\n    - [X] Weights, biases, activation functions\n    - [X] [[https://youtu.be/Ilg3gGewQ5U?si=ECiAVCVOcOvG_ZUw][Back-propagation]], [[https://youtu.be/tIeHLnjs5U8?si=oYLibvjf4u0W7gqr][differentiation]], [[https://youtu.be/AXqhWeUEtQU?si=wFsFBSyuqFAewbd5][partial]] [[https://youtu.be/ly4S0oi3Yz8?si=ARonsRGt2M-QT_MP][derivatives]], [[https://youtu.be/IHZwWFHWa-w?si=27mzINUeHkHVHMAz][gradient descent]]\n    - [X] Hyperparameters, regularization\n- [ ] Kinds of Networks and their applications\n  - [ ] Deep Networks, GANs, RNNs, CNNs, Transformers, GPTs\n- [ ] Tuning and Visualization Tools\n- [ ] Data\n  - [ ] Arrays, Tensors\n  - [ ] Training, Validation, Test Data\n  - [ ] Manipulation and Cleaning\n  - [ ] Statistics and Feature Selection\n- [-] Tools and Libraries\n  - [-] Python\n    - [X] [[https://keras.io/][Keras]]\n    - [X] [[https://pytorch.org/][Pytorch]]\n    - [ ] [[https://www.tensorflow.org/][Tensorflow]]\n    - [ ] [[https://jax.readthedocs.io/en/latest/quickstart.html][JAX]]\n    - [X] [[https://numpy.org/doc/stable/user/basics.html][Numpy]]\n    - [ ] [[https://pandas.pydata.org/docs/user_guide/10min.html][Pandas]]\n    - [ ] Scipy\n    - [ ] sklearn\n    - [ ] [[https://matplotlib.org/][Matplotlib]]\n  - [ ] Clojure\n    - [ ] [[https://neanderthal.uncomplicate.org/][Neanderthal]]\n    - [ ] Metamorph\n    - [ ] [[https://github.com/scicloj][Scicloj]]\n    - [ ] tech.ml.dataset / [[https://github.com/scicloj/tablecloth][Tablecloth]]\n** More Resources\n- Calculus\n  - [[https://calculusmadeeasy.org/][Made Easy]]\n    - [[https://calculusmadeeasy.org/16.html][Partial Differentiation]]\n  - [[https://ocw.mit.edu/ans7870/resources/Strang/Edited/Calculus/Calculus.pdf][Book (Strang)]]\n  - [[https://www.wolfram.com/wolfram-u/courses/mathematics/introduction-to-calculus/][Wolfram]]\n- Linear Algebra\n  - [[https://aiprobook.com/numerical-linear-algebra-for-programmers/][Clojure Book]]\n- Deep Learning\n  - [[https://www.deeplearningbook.org/][MIT Book]]\n  - [[https://aiprobook.com/deep-learning-for-programmers/][Clojure Book]]\n- [[https://youtu.be/SmZmBKc7Lrs?si=d-DnSbmRnzJ0PgHN][Backpropagation]]\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchristianromney%2Fmnist-keras","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchristianromney%2Fmnist-keras","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchristianromney%2Fmnist-keras/lists"}