Python Deep Learning: PyTorch vs Tensorflow (Summary)

In this video course, you’ve had an introduction to PyTorch and TensorFlow, seen who uses them and what APIs they support, and learned how to choose PyTorch vs TensorFlow for your project. You’ve seen the different programming languages, tools, datasets, and models that each one supports, and learned how to pick which one is best for your unique style and project.

In this video course, you learned:

• What the differences are between PyTorch and TensorFlow
• How to use tensors to do computation in each
• Which platform is best for different kinds of projects
• What tools and data are supported by each

Now that you’ve decided which library to use, you’re ready to start building neural networks with them. Check out the links in Further Reading for ideas.

Further Reading

The following tutorials are a great way to get hands-on practice with PyTorch and TensorFlow:

• Practical Text Classification With Python and Keras teaches you to build a natural language processing application with PyTorch.
• Setting Up Python for Machine Learning on Windows has information on installing PyTorch and Keras on Windows.
• Pure Python vs NumPy vs TensorFlow Performance Comparison teaches you how to do gradient descent using TensorFlow and NumPy and how to benchmark your code.
• Python Context Managers and the “with” Statement will help you understand why you need to use with tf.compat.v1.Session() as session in TensorFlow 1.0.
• Generative Adversarial Networks: Build Your First Models will walk you through using PyTorch to build a generative adversarial network to generate handwritten digits!
• The Machine Learning in Python series is a great source for more project ideas, like building a speech recognition engine or performing face recognition.