\n","\n","These are a few of the notebooks related to Machine Learning, including Google's online Machine Learning course. See the [full course website](https://developers.google.com/machine-learning/crash-course/) for more.\n","- [Intro to Pandas DataFrame](https://colab.research.google.com/github/google/eng-edu/blob/main/ml/cc/exercises/pandas_dataframe_ultraquick_tutorial.ipynb)\n","- [Intro to RAPIDS cuDF to accelerate pandas](https://nvda.ws/rapids-cudf)\n","- [Getting Started with cuML's accelerator mode](https://colab.research.google.com/github/rapidsai-community/showcase/blob/main/getting_started_tutorials/cuml_sklearn_colab_demo.ipynb)\n","\n","
\n","\n","
\n","### Using Accelerated Hardware\n","
\n","\n","- [Train a CNN to classify handwritten digits on the MNIST dataset using Flax NNX API](https://colab.research.google.com/github/google/flax/blob/main/docs_nnx/mnist_tutorial.ipynb)\n","- [Train a Vision Transformer (ViT) for image classification with JAX](https://colab.research.google.com/github/jax-ml/jax-ai-stack/blob/main/docs/source/JAX_Vision_transformer.ipynb)\n","- [Text classification with a transformer language model using JAX](https://colab.research.google.com/github/jax-ml/jax-ai-stack/blob/main/docs/source/JAX_transformer_text_classification.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"P-H6Lw1vyNNd"},"source":["
\n","\n","
\n","\n","### Featured examples\n","\n","
\n","\n","- [Train a miniGPT language model with JAX AI Stack](https://docs.jaxstack.ai/en/latest/JAX_for_LLM_pretraining.html)\n","- [LoRA/QLoRA finetuning for LLM using Tunix](https://github.com/google/tunix/blob/main/examples/qlora_gemma.ipynb)\n","- [Parameter-efficient fine-tuning of Gemma with LoRA and QLoRA](https://keras.io/examples/keras_recipes/parameter_efficient_finetuning_of_gemma_with_lora_and_qlora/)\n","- [Loading Hugging Face Transformers Checkpoints](https://keras.io/keras_hub/guides/hugging_face_keras_integration/)\n","- [8-bit Integer Quantization in Keras](https://keras.io/guides/int8_quantization_in_keras/)\n","- [Float8 training and inference with a simple Transformer model](https://keras.io/examples/keras_recipes/float8_training_and_inference_with_transformer/)\n","- [Pretraining a Transformer from scratch with KerasHub](https://keras.io/keras_hub/guides/transformer_pretraining/)\n","- [Simple MNIST convnet](https://keras.io/examples/vision/mnist_convnet/)\n","- [Image classification from scratch using Keras 3](https://keras.io/examples/vision/image_classification_from_scratch/)\n","- [Image Classification with KerasHub](https://keras.io/keras_hub/guides/classification_with_keras_hub/)\n"]},{"cell_type":"code","source":["for i in range(1, 101):\n"," if i % 3 == 0 and i % 5 == 0:\n"," print(i, \"FizzBuzz\")\n"," elif i % 3 == 0:\n"," print(i, \"Fizz\")\n"," elif i % 5 == 0:\n"," print(i, \"Buzz\")\n"," else:\n"," print(i)"],"metadata":{"id":"hZdCft0hey7w","executionInfo":{"status":"ok","timestamp":1773930248291,"user_tz":-420,"elapsed":11,"user":{"displayName":"","userId":""}},"outputId":"27a8c11f-dff3-408d-e83b-702d68dc7a05","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["1\n","2\n","3 Fizz\n","4\n","5 Buzz\n","6 Fizz\n","7\n","8\n","9 Fizz\n","10 Buzz\n","11\n","12 Fizz\n","13\n","14\n","15 FizzBuzz\n","16\n","17\n","18 Fizz\n","19\n","20 Buzz\n","21 Fizz\n","22\n","23\n","24 Fizz\n","25 Buzz\n","26\n","27 Fizz\n","28\n","29\n","30 FizzBuzz\n","31\n","32\n","33 Fizz\n","34\n","35 Buzz\n","36 Fizz\n","37\n","38\n","39 Fizz\n","40 Buzz\n","41\n","42 Fizz\n","43\n","44\n","45 FizzBuzz\n","46\n","47\n","48 Fizz\n","49\n","50 Buzz\n","51 Fizz\n","52\n","53\n","54 Fizz\n","55 Buzz\n","56\n","57 Fizz\n","58\n","59\n","60 FizzBuzz\n","61\n","62\n","63 Fizz\n","64\n","65 Buzz\n","66 Fizz\n","67\n","68\n","69 Fizz\n","70 Buzz\n","71\n","72 Fizz\n","73\n","74\n","75 FizzBuzz\n","76\n","77\n","78 Fizz\n","79\n","80 Buzz\n","81 Fizz\n","82\n","83\n","84 Fizz\n","85 Buzz\n","86\n","87 Fizz\n","88\n","89\n","90 FizzBuzz\n","91\n","92\n","93 Fizz\n","94\n","95 Buzz\n","96 Fizz\n","97\n","98\n","99 Fizz\n","100 Buzz\n"]}]}],"metadata":{"colab":{"provenance":[{"file_id":"/v2/external/notebooks/intro.ipynb","timestamp":1773930264645}]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"nbformat":4,"nbformat_minor":0}
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