Concrete-ml

Feature
* Add SGD regressor ([`abb143c`](https://github.com/zama-ai/concrete-ml/commit/abb143c0852f7a37a6de916bc119147096c0067b))

Fix
* Fix shape output mismatch for KNNClassifier ([`6de7c6e`](https://github.com/zama-ai/concrete-ml/commit/6de7c6e85f668193d026763530507f5afe8f50f1))

Feature
* Enable import of fitted linear sklearn models ([`771c7ff`](https://github.com/zama-ai/concrete-ml/commit/771c7ffe05ef5c88ae5f3deec68fb1cb0cec80f8))
* Support QAT models in hybrid model ([`526b000`](https://github.com/zama-ai/concrete-ml/commit/526b000cb13291ea4f038c9dd76db151fb8f2729))
* Expose statuses to compile torch ([`8abddf6`](https://github.com/zama-ai/concrete-ml/commit/8abddf6afe84983e952a1ca9d40e90dc05e66e21))
* Add KNN classifier in CML ([`1c33ec8`](https://github.com/zama-ai/concrete-ml/commit/1c33ec8121f44be330387508f82c7b6dd30cf843))
* Add power of two scaling adapter for roundPBS ([`546fac9`](https://github.com/zama-ai/concrete-ml/commit/546fac9cbc060b020224a997c2cd2c4d1a7c67c5))
* Add hybrid FHE models ([`be6aa6e`](https://github.com/zama-ai/concrete-ml/commit/be6aa6e7e3fc963b9fdef4fa73ec0598606fb50e))

Fix
* Fix confusing print in CNN tutorial of advanced-examples ([`9136c47`](https://github.com/zama-ai/concrete-ml/commit/9136c47e5a8c5dbfaa0b7f2b736da35ba77bdbdd))
* Fix path parsing and default in hybrid serving ([`afd049a`](https://github.com/zama-ai/concrete-ml/commit/afd049ae5394fc2ba37ac6230cfece5996ffc373))
* Fix flaky padding test ([`6aaf5f0`](https://github.com/zama-ai/concrete-ml/commit/6aaf5f03868dc58e13fa9f04147b6ad8354baae0))
* Fix issues with OMP library ([`2b61846`](https://github.com/zama-ai/concrete-ml/commit/2b61846fd8da94ccc84757d5bb358a66c1f4b0e9))
* Make sure structured pruning and unstructured pruning work well together ([`ada18ab`](https://github.com/zama-ai/concrete-ml/commit/ada18abc54121cf21008fcaaac2f21f783f0fe7e))
* Fix structured pruning crash not caught by test ([`cafd8d1`](https://github.com/zama-ai/concrete-ml/commit/cafd8d140e0077b2843136925187d80c9588aa0c))
* Fix bad top1 accuracy in cifar_brevitas_training use case ([`f0a984e`](https://github.com/zama-ai/concrete-ml/commit/f0a984e7031506f0730014e2daddaca6bc3b6fa9))
* Fix flaky double_fit test ([`3da6408`](https://github.com/zama-ai/concrete-ml/commit/3da6408cc1df1ee382fca7d299ee8b9f0fa342ef))
* Remove workaround for simulating linear models ([`3f622bc`](https://github.com/zama-ai/concrete-ml/commit/3f622bc51d67bd1407d0577bb45b44f8d053c1e1))
* Re-compute quantization params when re-fitting linear models ([`3bad62e`](https://github.com/zama-ai/concrete-ml/commit/3bad62e7490493ee999e6f01c7316f4b91cbcb28))

Documentation
* Fix and improve credit scoring use case example ([`e4db376`](https://github.com/zama-ai/concrete-ml/commit/e4db376757af57b382de18dbf7fa55dbbc69be06))
* Update contribution part ([`f2822d1`](https://github.com/zama-ai/concrete-ml/commit/f2822d1a9db6b4f2590c68aaf2d30b2bd8aac3a7))
* Document KNN, PoT, Hybrid models ([`68a0b4c`](https://github.com/zama-ai/concrete-ml/commit/68a0b4ca7fd906fa14e6652453d27a6485158c6c))
* Update mnist CNN ([`f80c90b`](https://github.com/zama-ai/concrete-ml/commit/f80c90bd98afb85cc5a089b1c60fbf609a3817b3))
* Update mnist Fully Connected example with PoT + rounding ([`6e3d003`](https://github.com/zama-ai/concrete-ml/commit/6e3d003c8f11780faafb45222404e8ebdcc7c576))
* Update cifar_brevitas_training accuracy using representative calibration set ([`39480ef`](https://github.com/zama-ai/concrete-ml/commit/39480ef07c322172f4d9894e11a79876f029cb1e))
* Correct n_bits markdown value in the LLM use case notebook ([`0cf1174`](https://github.com/zama-ai/concrete-ml/commit/0cf117460e8c6f647629e2ff2ad4c1004e2dc53c))

Feature
* Add hybrid FHE models ([`be6aa6e`](https://github.com/zama-ai/concrete-ml/commit/be6aa6e7e3fc963b9fdef4fa73ec0598606fb50e))

Fix
* Flaky test double_fit ([`3da6408`](https://github.com/zama-ai/concrete-ml/commit/3da6408cc1df1ee382fca7d299ee8b9f0fa342ef))
* Re-compute quantization parameters when re-fitting linear models ([`3bad62e`](https://github.com/zama-ai/concrete-ml/commit/3bad62e7490493ee999e6f01c7316f4b91cbcb28))

Documentation
* Add link to deployment use case examples ([`f9333b3`](https://github.com/zama-ai/concrete-ml/commit/f9333b3e66312f94e0386956af9f4b39907781b9))
* Correct mention of n_bits in LLM use case notebook text ([`0cf1174`](https://github.com/zama-ai/concrete-ml/commit/0cf117460e8c6f647629e2ff2ad4c1004e2dc53c))

Feature
* Support multi-input QM with inputs of different shapes ([`6e1315f`](https://github.com/zama-ai/concrete-ml/commit/6e1315f2f14030dfb9ee7285722775f25a8048f0))
* Add a function to build a quantized module from a model ([`34e5f68`](https://github.com/zama-ai/concrete-ml/commit/34e5f68530f81d6ee3ff74c9977da1a69e8c5455))
* Expose scikit-learn training attributes in built-in models ([`37ab8c0`](https://github.com/zama-ai/concrete-ml/commit/37ab8c056c0f5711110d7ca96935c5410ccb37e4))
* Add serialization to QNN models ([`33312a8`](https://github.com/zama-ai/concrete-ml/commit/33312a8cf41fc8f14ca6bc70feef761c9ba84bf4))

Documentation
* Add example notebook on LinearSVR ([`f42f0e6`](https://github.com/zama-ai/concrete-ml/commit/f42f0e6b31920de583323e29606813f8c0ee6b44))
* Add svm classifier tutorial ([`49e13ec`](https://github.com/zama-ai/concrete-ml/commit/49e13ecfc47fae325bb352f84ecea2333087752b))
* Add DecisionTreeRegressor to advanced_examples ([`148e626`](https://github.com/zama-ai/concrete-ml/commit/148e626f90c1ad9a58f959af9986692b6cacbf97))
* Add regressor comparison tutorial ([`6c7cbf7`](https://github.com/zama-ai/concrete-ml/commit/6c7cbf7a0be7acb4ab1ea85622b314ed2ac76da2))