AI Development Simplified: The Power...
December 26, 2024
You must have heard terms like Artificial Intelligence, Machine Learning, Deep Learning, etc., recently. Because the world is getting smarter, and so are the devices around us. First came phones, and then came smartphones, first came TVs, and then came smart TVs, first came speakers, and then smart speakers. In short, every device is getting smarter with Artificial Intelligence.
The reason behind those devices are getting smart is their intelligence, Artificial Intelligence! This era is all about artificial intelligence, machine learning, and deep learning. It is even said that Machine Learning is the future. But, among all these, Deep Learning is quite popular these days. But, have you wondered what Deep Learning is? What frameworks to use to implement Deep learning? What if you want deep learning on your app? How to hire a Deep Learning developer? All of your answers are here.
What is Deep Learning?
Deep Learning is a part of Machine Learning methods that are based on artificial neural networks. Deep learning can be supervised, semi-supervised, or unsupervised. Deep Learning is just an AI function that mimics human intelligence in terms of identifying objects, recognizing speech, translating various languages, and other human decisions. In short, Deep Learning is a concept that resembles human intelligence.
Deep Learning Frameworks
There are a lot of deep learning frameworks in this market. Many deep learning developers use different frameworks, but some deep learning frameworks have become popular, and many people use those frameworks to build their projects. Tensorflow and PyTorch are the two most famous deep learning frameworks. Deep Learning with TensorFlow is quite easy, and PyTorch also goes on the same note.
Sometimes people get confused between choosing these two frameworks, and that’s why we have compiled its pros’ list and written this blog to help you choose the best Deep Learning framework.
PyTorch Framework
PyTorch is a famous Deep Learning framework that is based on the Torch library. It will help you throughout from research to production. PyTorch is developed by Facebook’s AI research lab, so there are no questions on its reliability. Moreover, you don’t have to pay anything to use the PyTorch Framework on your application project, as it is open-source.
Why Choose PyTorch Development?
PyTorch is Pythonic
If you are a Python developer, you will adapt to this framework very easily as PyTorch is completely Pythonic. Any Python developer will feel like home while working with the PyTorch framework. The PyTorch framework is quite easy to understand, and it feels like in-line with Python code. So, as a Python developer, you can quickly learn this Deep Learning framework as it already has Python classes like loss functions, optimizers, data loaders, and many more.
Easy to learn and use
The PyTorch framework lets you code very easily, and it has Python resembling code style. When you compare PyTorch with TensorFlow, PyTorch is a winner. Pytorch is relatively easy to learn, while TensorFlow will demand some struggle to learn. PyTorch is so easy that it almost feels like Python’s extension. So, if you are already familiar with Python development, PyTorch development will be so easy for you to learn and program.
Lots of Libraries
A library makes development work a lot faster. You don’t have to code specific functions or methods when you have a library included. The Pytorch framework comes with a lot of useful built-in libraries. There are libraries for a lot of purposes, like for Computer Vision, Natural Language Processing lIbraries; below are some PyTorch Libraries for different purpose programming.
- pro_gan_pytorch library for ProGAN features and to implement a generative adversarial network.
- BoTorch Library for Bayesian Optimizations.
- skorch Library for scikit-learn code that is used with the PyTorch framework
- OpenNMT-py library for the neural machine translation system.
- MUSE library multi-lingual word embeddings.
Data Parallelism
PyTorch allows you to perform data parallelism on your data, which has enabled deep learning with Python and PyTorch. Data Parallelism allows you to divide a lot of data into batches, and hence data processing becomes easy. It can also shift the load from CPU to GPU and enables faster data processing. torch.nn.DataParallel class is used to implement Data Parallelism in the PyTorch framework.
Implementing data parallelism is quite easy, and even a rising programmer can implement it if given enough attention to it. So, it will lessen the burden on your CPU and make your data process faster. All thanks to data parallelism in the PyTorch framework.
Great Community
Another great reason why one should choose Pytorch development is its great community. A lot of PyTorch developers are always there to help you whenever you have stuck anywhere while coding PyTorch. This community is also good to share and gain knowledge on programming and stuff. You will see amazing posts that increase your knowledge and make friends with other PyTorch programmers while voluntarily solving each other’s errors. As we have completed the Deep Learning PyTorch framework, it’s time to have a look at the TensorFlow Deep Learning framework and see its advantages one by one.TensorFlow Framework
The TensorFlow framework is an end-to-end open-source data science platform that is used especially for deep learning. Deep Learning with TensorFlow can be quite easy and allows one to implement smart functions on their app. TensorFlow is mainly used to train models and for inference of neural networks.
You can train different models and make them learn through the TensorFlow framework. Later, the model can make decisions or patterns based on the trained model and from the user inputs. Let’s have a look at the reasons to choose the TensorFlow framework for deep learning.
Why Choose TensorFlow
House of Google
There is no doubt about TensorFlow’s performance and capability as it hails from the House of Google. Tensorflow will perform great and provide you with a satisfactory output for deep learning projects, so you can trust it to start Tensorflow development for your project. Tensorflow is developed by the team at Google, and you can expect long time support from them in the future. It won’t end its support anytime in the near future, so you can use it for your next project.
Tensorboard for Visualization
If you want to visualize your data gained from the trained model, you can use the Tensorboard tool. This tool is used for data monitoring and data visualization. If you want to see your data model in action, then Tensorboard is quite a nice tool for it. In short, the Tensorboard tool will help you monitor and visualize your data collected from the model, which will help you progress further in your deep learning project.
Tensorflow.js for deep learning in Browser
Tensorflow has launched its machine learning library for JavaScript developers as well. You can develop Machine Learning models in JavaScript and use them in the browser to train different models. If your project is a web app, then you can use tensorflow.js to train models in the browser via Tensrflow.js development. Training models has been easier with the introduction of Tensorflow.js. So, you can implement deep learning in the browser as well, and the credit goes to the almighty Google.
Tensorflow Lite
If you want to run your training models and make your app learn from the users via a mobile app, then Tensorflow has a solution for that as well. You can use Tensorflow Lite to implement deep learning in mobile apps and devices. Deep Learning and Model training has been easier with TensorFlow Lite. Apart from just mobile devices, you can implement deep learning on embedded devices with Tensorflow Lite. So, the Tensorflow Lite framework is making things easier for Mobile Devices and embedded devices.
Community Support
Just like the PyTorch framework, you get a wholesome community for Tensorflow as well. There is a large Tensorflow developers’ community across the world, and they all help each other with bugs and errors. When you can’t solve an error, simply post it on the Tensorflow community and see the magic done. You will get a lot of volunteers who will help you solve that error and help you move forward with your Deep Learning development project.
So, picking a winner is very hard, as both these technologies are advanced in their own way. To sort out things for you, we have made a quick list that will help you choose the right deep learning framework.
Choose the PyTorch Framework if
- You want to build an app that focuses on performance and splits the CPU load with GPU, you can choose PyTorch, as it allows data parallelism.
- You want to leverage the usage of built-in libraries and make your development faster.
Choose the TensorFlow Framework if
- You want to implement Machine Learning in Mobile apps, web browsers, and other embedded devices.
- You want to visualize your data and the training model.
So, picking the right technology totally depends on your requirements, and one should pick one right Machine Learning development framework based on their requirement only. If you are still unsure about the right framework, you should contact us or drop an email at sales@inexture.com, and we will help you pick the right deep learning framework for your upcoming project.
You can schedule a free consulting session with one of the best Machine Learning Development companies by contacting us. Just contact us, and we will arrange a free consulting session with our experienced Machine Learning experts who have been working on cool machine learning and deep learning projects for years. Go ahead and get ready for the future with the best Deep Learning development services with INEXTURE!
