TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
I prefer Pytorch overall, recent models are often only available with Pytorch Pytorch is also easier to use and it is often easier to find support for Pytorch code nowadays than TensorFlow Also it seems like lots of Google internal resource uses JAX. I mostly uses TensorFlow to …
Can't seem to choose any deep learning platform in the above, so I'll list it here: 1. Apache MXNet: this has been used for one of our main algorithms for search as an end-to-end pipeline. We chose this because of the Scala bindings, which makes it easier to integrate with out …
Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, …
TensorFlow provides a wide range of algorithms with more detail and customization options compared to others. Also, the library is advanced and updates regularly for optimization and new functions.
Most of the machine learning platforms these days support integration with R and Python libraries. So, the use of reusable libraries is not an issue. TensorFlow performs well in cloud hosting and support for GPU/TPU. However, where it lacks compared to Azure is a graphical …
Thought about alternatives like scikit-learn, xgboost, pytorch, caffe2, fastai exist, but they don't offer as many tools and functionality as TensorFlow does. It is better to inanest in a eco-system which is very active and well maintained by giants. Being open source, one can …
Theano is a Python library and is good for making algorithms from scratch. It is an alternative to Tensor flow. We used tensor flow because it is open source Java source and easy to learn and use.
TensorFlow is developed and maintained by Google. It's the engine behind a lot of …
There are lots of competitors with this library, but I think TensorFlow is the best thing for deep learning. Although it has a sharp learning curve, it's worth learning. It easy to deploy its model on Android. Keras is very good option too it, easy. In Keras, writing the neural …
I have used Keras and MATLAB along with this. Also used Caffe and pyTorch sometimes, but all of them are not as powerful as TensorFlow. Keras is in good competition with TensorFlow but Keras won't allow you a lot of customization in your algorithms. And TensorFlow gives you the …
One major advantage of TensorFlow over Keras and other deep learning libraries is that it is the most powerful. It gives you power to write your own full customised algorithm that is not available in Keras. And it is fast too as compared to another tool as it can perform better …
I have used Theano to develop machine learning models, like writing the neural network. TensorFlow has reinforcement learning support and lot more algorithms while Theano does come with lots of prebuilt tools. TensorFlow provides data visualisation tools and it is possible to …
TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).
Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.
Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.
Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice