Best library for deep learning

This library is the best for deep learning. Designing neural network with this library is very easy. Also, it compute things very fast. It has made the visualization very easy. It has lots of built in features like conv2d network, lstm etc.

It's the best thing to do stuff in deep learning but it require a long learning curve. But once you know how it works then it made your job very easy.

A must use library to develop new algorithm of deep learning as it gives you power to customise everything.

I think if you are developing something that includes deep learning and some other machine learning technique as well, then you should use TensorFlow. It comes with lot of inbuilt functionality which makes thing easy for deep learning algorithm. It comes with inbuilt data handling/processing techniques which is very useful when developing new algorithm and implementing them.

Ti has steep learning curve. Understanding it's concept of tensor is not easy to understand. It takes time to learn it completely. And even after learning it, it takes time to develop or write the implementation of deep learning algorithm as you have to write everything by your own.

I use tensorflow for machine learning apps to find correlations in the market, but the app has let me down and I have since moved on to other libraries, as tensorFlow was simply to difficult to use.

Tensorflow is a good library for machine learning, but only for more experienced developpers.

It is very hyped by the community, but has a teap learning curve and is hard to learn. So the app is not beginner friendly, but also is't the best library for high level machine learning.

TensorFlow helped to Learn and understand the working of any machine learning models. It is simple and easy to use and is very helpful for any new comer to start their machine learning journey.

TensorFlow is easy to install and use. Machine Learning models are easy to code. Graph visualisation will help to understand the model. TensorFlow has very good community to help solve many problems and can easily learn from different tutorial videos. The cloud support is also affordable for model deployment.

Requires a lot of cpu power for model training. Visualisation of model can also be improved.

We use tensorflow LSTMs for sequence classification to mine patterns in log and customer behavior. In our use case, deep models decreased testing loss by 50% over a simple baseline.

Intuitive way to generate networks. Nice visualizations with tensorboard. Good documentation and tutorials. Large supportive community. Easily scaleable.

Many of the error messages can be cryptic and difficult to use when debugging. Frequent errors caused by data type mismatches. It is not easy to iterate quickly with Tensor flow.