Throughout this chapter, you have learned the basics of installing, configuring, and using TensorFlow and PyTorch on your Conda environment. You have also learned how to work with both frameworks on Google Colaboratory. You learned five basic steps to implement machine learning on Python. You now know how the dataset structures should look on both TensorFlow and PyTorch, along with with their locations after download.
You can now start working either on PyCharm locally, harnessing a local GPU for machine learning with both TensorFlow and PyCharm, or do the same on Google Colab. Both GPUs and TPUs with TensorFlow can be your portable interface from now on. Also, you are now familiar with the use of PyTorch on Google Colab by default for GPUs as well. You can now revisit the computational exercises discussed earlier to understand their significance with a machine learning...
