Tensorflow is built for deep-divers
March 23, 2022
Tensorflow is built for deep-divers

Score 7 out of 10
Vetted Review
Verified User
Overall Satisfaction with TensorFlow
Tensorflow is a good intermediate level for building neural networks, or more generally, differentiable programming. Tensorflow v1 and Tensorflow v2 have very significant architectural differences: v1 is about defining a computational graph, upon which operations are performed (like "do one step of backprop" or "batch-evaluate on this data"), while v2 does more computations "live" and is built more like, essentially, a heavy-duty calculator with a differentiable history. v2 is tightly integrated with Keras, so if you intend to use industry-standard layers and architectures from Keras, then Tensorflow is probably your best bet. Both v1 and v2 allow you to define your own layers, or do other differentiable programming tasks; for instance, differentiable physics engines have been written in Tensorflow.
- Integrating with Keras.
- Working on CPU/GPU/TPU neutrally.
- Exporting to TFLite for browsers or edge computing.
- The massive changes between v1 and v2 can be confusing when looking at examples online.
- TensorFlow is losing market ground to PyTorch and JAX.
- Flexible differentiation setup that permits backprop through backprop.
- Portability and platform agnosticism.
- It's been an efficient tool for our research objectives.
Do you think TensorFlow delivers good value for the price?
Yes
Are you happy with TensorFlow's feature set?
Yes
Did TensorFlow live up to sales and marketing promises?
Yes
Did implementation of TensorFlow go as expected?
Yes
Would you buy TensorFlow again?
Yes