diff --git a/README.md b/README.md index 94912c4..82df105 100644 --- a/README.md +++ b/README.md @@ -2,11 +2,11 @@ Machine Learning Notebooks, 3rd edition ================================= This project aims at teaching you the fundamentals of Machine Learning in -python. It contains the example code and solutions to the exercises in the second edition of my O'Reilly book [Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow (3rd edition)](https://homl.info/er3): +python. It contains the example code and solutions to the exercises in the third edition of my O'Reilly book [Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow (3rd edition)](https://homl.info/er3): -**Note**: If you are looking for the second edition notebooks, check out [ageron/handson-ml2](https://github.com/ageron/handson-ml2). For the first edition, see check out [ageron/handson-ml](https://github.com/ageron/handson-ml). +**Note**: If you are looking for the second edition notebooks, check out [ageron/handson-ml2](https://github.com/ageron/handson-ml2). For the first edition, see [ageron/handson-ml](https://github.com/ageron/handson-ml). ## Quick Start @@ -34,7 +34,7 @@ Read the [Docker instructions](https://github.com/ageron/handson-ml3/tree/main/d ### Want to install this project on your own machine? -Start by installing [Anaconda](https://www.anaconda.com/distribution/) (or [Miniconda](https://docs.conda.io/en/latest/miniconda.html)), [git](https://git-scm.com/downloads), and if you have a TensorFlow-compatible GPU, install the [GPU driver](https://www.nvidia.com/Download/index.aspx), as well as the appropriate version of CUDA and cuDNN (see TensorFlow's documentation for more details). +Start by installing [Anaconda](https://www.anaconda.com/products/distribution) (or [Miniconda](https://docs.conda.io/en/latest/miniconda.html)), [git](https://git-scm.com/downloads), and if you have a TensorFlow-compatible GPU, install the [GPU driver](https://www.nvidia.com/Download/index.aspx), as well as the appropriate version of CUDA and cuDNN (see TensorFlow's documentation for more details). Next, clone this project by opening a terminal and typing the following commands (do not type the first `$` signs on each line, they just indicate that these are terminal commands):