What are the key advantages of TensorFlow over Caffe in machine learning?
The question is about Tensorflow
The main features that give TensorFlow an advantage over Caffe in machine learning are its flexibility, scalability, and support from the ecosystem. Many other neural networks, besides convolutional networks, can be supported by a versatile architecture in TensorFlow, to become suitable for diverse tasks such as NLP, reinforcement learning, and generative models. Caffe was designed and optimized basically for image processing tasks. Ranging from development to deployment, this state-of-the-art ecosystem includes TensorFlow Serving for deployment, TensorFlow Lite for mobile, and TensorBoard for visualization. Besides a strong community and extensive documentation, TensorFlow natively supports distributed training across multiple GPUs and TPUs, making it far more scalable and adaptable for complex applications in production.
