[MUSIC] Hi, everyone, this is! Shannon, Ephraim, Ed Eureka. And in today’s session, we will talk about three most popular deep learning frameworks and their use cases, The software industry nowadays is moving towards machine intelligence machine learning has become necessary in every sector as a way of making machines intelligent now in a simpler way, machine learning is a set of algorithms that parse data learn from them and then apply what they have learnt to make intelligent decisions. Deep learning is gaining much popularity due to its supremacy in terms of accuracy when trained with huge amount of data now. Whether you want to start applying it to your business base, your next side project on it or simply gain marketable skills, picking the right deep learning framework to learn is the essential first step towards reaching your goal, so the three most popular frameworks for deep learning that we are going to discuss today are carers Tensorflow and Pi touch now? Carri’s is basically an open source neural network library written in Python. It is also capable of running on top of tensorflow. Microsoft, Cognitive Toolkit or Tiano it is designed to enable fast experimentation with deep neural networks. It also focuses on being user-friendly modular and extensible, Not tensorflow is an open source software library for dataflow programming across a range of tasks. It is also a symbolic math library and is used for machine. Learning applications such as neural networks. Next up is the PI torch. It is an open source machine learning library for python based on torch and is used for applications such as natural language processing. It is primarily developed by Facebook’s artificial intelligence research group and also uber spyro software for probabilistic programming, is built on it now. All the three frameworks are related to each other and perform similar tasks, But let’s have a look at the meters that distinguish them from one another. So the first one is the level of API. Now, when we consider the level of API. Caris has a high level, whereas PI Torch consists of a low level API and tensorflow is basically the framework that provides both high and low apis. Kerris is a high level API that is capable of running on top of tensorflow cnpk piano or MX myth. It has gained favor for its ease of use and syntactic simplicity, facilitating fast development on the other hand. Pi Torch is just a lower-level API that is focused on direct work with Eddy expressions. It has gained immense interest in the last year. Becoming a preferred solution for academic research and applications of deep learning requiring optimizing custom expressions. Next parameter is the speed now. The speed is comparatively slower in case of Karass, whereas tensorflow and Pi Toge provide a similar pace, which is suitable for high performance, but gains in computational efficiency of higher performing frameworks will be outweighed by the fast development environment and the ease of experimentation that Kerris offers now moving on the next parameter is the architecture in case of architecture now. Karis has a simple architecture. It is more readable and concise. It is simple and easy to use, which is why most of the beginners prefer to use Scarers when compared to the other two tensorflow on the other hand is not very easy to use and has a complicated architecture. That might not be very helpful for the beginners now. Pi Torch on the other hand, has a very complex architecture and also the readability is less when compared to Kari’s. The next parameter of comparison is the ease of code now. One advantage of using Kerris is that there is single line of code for implementing it, which makes it a preferable framework for the coders, but tensorflow provides a reduced size model along with high accuracy. Pi Torch on the other hand consists of more number of lines in code and it is not so simple when compared to the other two. The next one is the debugging now in Cara’s. There is usually very less frequent need to debug simple networks, and it offers a more direct unconjugated debugging experience regardless of model complexity, but in case of tensorflow, it is quite difficult to perform debugging python on the other hand has better debugging capabilities. It has fewer opportunities to go wrong, but once something goes wrong, it is difficult to pin down the exact line that causes a trouble moving on the next parameter. Is the community support now? Kari’s has got a smaller community support. When it comes to troubleshooting any problem or any error, but tensorflow on the other hand is backed by a large community of tech companies. Pi Torch also has got a strong community support. Now moving on to the datasets. Cari’s is mostly used for small datasets as it is comparatively slower on the other hand, tensorflow and Pi Torch are preferred for high performance models and large datasets because of better training duration. Final parameter of comparison includes the popularity. Now all these three frameworks have gained quite a lot of popularity in the recent times, But Kari’s has topped the list, followed by tensorflow and Pi Torch. It has gained immense popularity due to its simplicity when compared to the other two frameworks. We have seen the various parameters that distinguish the frameworks, but there is no straight answer to which one is actually better. The choice ultimately comes down to your technical background needs and expectations. So let’s move on and have a look at. What are the suitable situations where these frameworks should be used? Carri’s is mostly preferred in case of rapid prototyping, So if you want to quickly build and test a neural network with minimal lines of code, go for Kari’s. Also, it is mostly suitable for small sized data sets and best for newbies as it is simple and easy to understand now. Tensorflow is mostly preferred for large datasets, and also where high performance is mandatory. Also, Tensorflow provides advanced operations and all general-purpose functionalities for building deep learning models. Now in Pi Torch, you can implement almost everything that you want to, which makes it pretty flexible for use. Also, it provides a better training duration and debugging capabilities when compared to the rest so now that we have learnt about the top three deep learning frameworks to do let us know which one out of the three serves your purpose better. Don’t forget to share your opinion in the comment section below till then, thank you and happy learning. I hope you have enjoyed listening to this video. Please be kind enough to like it and you can comment any of your doubts and queries and we will reply them at the earliest. Do look out for more videos in our playlist and subscribe to any Rekha channel to learn more happy learning.