The TensorFlow Tug of War: Pros vs Cons
Google created the open-source machine learning framework known as TensorFlow. For developing, evaluating, and implementing machine learning models, it is frequently employed.
Pros and cons of using TensorFlow:
Pros:
- TensorFlow is a popular and widely used machine learning framework, so there is a large and active community of users and developers who can provide support and resources.
- TensorFlow has a flexible architecture that allows you to build as well as deploy machine learning models on a variety of platforms, including desktop, mobile, and web applications.
- TensorFlow has a large number of pre-built libraries and functions for tasks such as image and text classification, object detection, and natural language processing.
- TensorFlow allows you to easily scale your machine learning models across multiple devices and servers using distributed training.
- Provides a good debugging tool by executing graph subcomponents that make it easy to add and remove discrete data from edges.
- Libraries can be installed on a variety of hardware, including PCs with complicated configurations and cellular devices, as well as highly parallel neural networks that group vast distributed systems.
- TensorFlow makes it simple to share a trained model.
Cons:
- TensorFlow can be complex to learn and use, especially for beginners. It requires a good understanding of machine learning concepts and math.
- TensorFlow can be slower than other machine learning frameworks, especially when running on a single CPU.
- TensorFlow does not provide as much support for some machine learning tasks, such as unsupervised learning and reinforcement learning, as other frameworks.
- TensorFlow can be memory-intensive, especially when training large models, and may require a powerful GPU to run efficiently.
- Far behind its competitors in terms of utilization and speed.
- NVIDIA GPUs are the only ones that are currently supported.
- Python is the sole fully supported language, which is a disadvantage given the growth of alternative deep learning languages.
- Despite being more capable and superior for deep learning, TensorFlow is ineffective for smaller tasks.
Don’t miss out on the detailed story: TensorFlow Pros vs Cons