setting up deep-learning workstation before you can get started developing deep-learning applicationsyou need to set up your workstation it' highly recommendedalthough not strictly necessarythat you run deep-learning code on modern nvidia gpu some applications--in particularimage processing with convolutional networks and sequence processing with recurrent neural networks--will be excruciatingly slow on cpueven fast multicore cpu and even for applications that can realistically be run on cpuyou'll generally see speed increase by factor or or by using modern gpu if you don' want to install gpu on your machineyou can alternatively consider running your experiments on an aws ec gpu instance or on google cloud platform but note that cloud gpu instances can become expensive over time whether you're running locally or in the cloudit' better to be using unix workstation although it' technically possible to use keras on windows (all three keras backends support windows)we don' recommend it in the installation instructions in appendix awe'll consider an ubuntu machine if you're windows userthe simplest solution to get everything running is to set up an ubuntu dual boot on your machine it may seem like hasslebut using ubuntu will save you lot of time and trouble in the long run note that in order to use kerasyou need to install tensorflow or cntk or theano (or all of themif you want to be able to switch back and forth among the three backendsin this bookwe'll focus on tensorflowwith some light instructions relative to theano we won' cover cntk jupyter notebooksthe preferred way to run deep-learning experiments jupyter notebooks are great way to run deep-learning experiments--in particularthe many code examples in this book they're widely used in the data-science and machine-learning communities notebook is file generated by the jupyter notebook app (execute python code with rich text-editing capabilities for annotating what you're doing notebook also allows you to break up long experiments into smaller pieces that can be executed independentlywhich makes development interactive and means you don' have to rerun all of your previous code if something goes wrong late in an experiment we recommend using jupyter notebooks to get started with kerasalthough that isn' requirementyou can also run standalone python scripts or run code from within an ide such as pycharm all the code examples in this book are available as open source notebooksyou can download them from the book' website at www manning com/books/deep-learning-with-python licensed to