Welcome to This Course on Deep Learning: An Introduction to Karis

I'm Mia and I'm very excited to be teaching you all about deep learning through this course. We'll be covering Kara's as a powerful tool to add to your arsenal. Curren is a high-level deep learning framework that will help us understand what it means to be a high-level framework compared to lower-level frameworks like Theano.

Building a neural network in Theano can take many lines of code and requires a deep understanding of how they work internally. In contrast, building the same network in Karis only takes a few lines of code and is much quicker. Kara's is an open-source deep learning library that enables fast experimentation with neural networks, running on top of other frameworks like TensorFlow, Keras, or CNTK. It was created by Francie and Francois Chawla.

Compared to lower-level libraries like TensorFlow, Karis allows you to build industry-ready models in no time with much less code than before. This also enables quickly and easily checking if a neural network will get your results off. Additionally, you can do any architecture you can imagine, from simple networks to more complex ones like encoders, convolutional or recurrent neural networks, and chaos models. These models can be deployed across a wide range of platforms like Android iOS where apps are created.

Karis is now fully integrated into TensorFlow 2.0, so you can use the best of both worlds as needed and in the same code pipeline. This means that if you dive deep into deep learning, you'll find yourself needing to use low-level features for instance to have a final control over all your network's applies gradients, you could use TensorFlow and tweak whatever you need.

Now that we know better what Kara says and why to use it, perhaps we shall discuss when and why to use neural networks in the first place. Neural networks are good feature structures since they've learned the best way to make sense of unstructured data previously. It was the domain where experimentation and heuristics were used to extract relevant features of data.

Neural networks can learn the best features under combination, from feature engineering themselves, which is why they're so useful. However, what are some structured data? Structured data is data that is not easily put into a table for instance, sound, video images etc. It's also the type of data where performing feature engineering can be more challenging.

That's why leaving this task to neural networks is a good idea if you're dealing with unstructured data. You don't need to interpret the results and your problem can benefit from a known architecture. Then, you probably should use neural networks for instance when classifying images of cats and dogs. Images are unstructured data where we don't care as much about why the network knows it is a cat or a dog.

We can benefit from a convolutional neural network. It's wise to use neural networks if you're learning about their usefulness later on in this course. Now that we've covered the basics of Kara's, let's move on to review.

"WEBVTTKind: captionsLanguage: enwelcome to this course on deep learning I'm Mia and I'm very excited to be teaching you Karis here on betacam this course will have Kara's as a powerful tool to your arsenal Curren is a high-level deep learning framework to understand what is meant by that we can compare it to a lower level framework like TN building a neural network in theano can take many lines of code and it requires a deep understanding of how they work internally building and training this very same network in Karis only takes a few lines of code much quicker right girls is an open source deep learning library that enables fast experimentation with neural networks it runs on top of other frameworks like tential 30 on o or c NT k and it was created by Francie I researcher Francois Chawla while you scare us instead of slaughter low level libraries like tensorflow with Karis you can build industry ready models in no time with much less cool than Tia as we saw before and a higher extraction than that offer by tensorflow this allows for quickly and easily checking if a neural network will get your province off in addition you can do any architecture you can imagine from simple networks to more complex ones like our encoders convolutional or recurrent neural networks chaos models can also be deployed across a wide range of platforms like Android iOS where apps etc it is the best moment to be learning Charis Charis is now fully integrated into tension flow 2.0 so you can use the best of both worlds as needed and in the same code pipeline if as you dive deep into deep learning you find yourself needing to use low-level features for instance to have a final control of all your network applies gradients you could use tensor flow and tweak whatever you need now that you know better what Kara says and why to use it perhaps we shall discuss when and why to use neural networks in the first place neural networks are good feature structures since they've learned the best way to make sense of unstructured tape previously it was the domain Esper that had to set rules based on experimentation and heuristics to extract the relevant features of data neural networks can learn the best features under combination the camper from feature engineering themselves and that's why they are so useful but what are some structured data structured data is data that is not easily put into a table for instance sound video images etc it is also the type of data were performing feature engineering can be more challenging and that's why leaving this task to neural networks is a good idea if you are dealing we don't structured there you don't need to interpret the results and your problem can benefit from a known architecture then you probably should use neon networks for instance when classifying images of cats and dogs images are unstructured data we don't care as much about why the network knows it is a cat or a dog and we can benefit from a convolutional neural network so it is wise to use neural networks you will learn about the usefulness of convolutional neural networks later on in the course it is now time to reviewwelcome to this course on deep learning I'm Mia and I'm very excited to be teaching you Karis here on betacam this course will have Kara's as a powerful tool to your arsenal Curren is a high-level deep learning framework to understand what is meant by that we can compare it to a lower level framework like TN building a neural network in theano can take many lines of code and it requires a deep understanding of how they work internally building and training this very same network in Karis only takes a few lines of code much quicker right girls is an open source deep learning library that enables fast experimentation with neural networks it runs on top of other frameworks like tential 30 on o or c NT k and it was created by Francie I researcher Francois Chawla while you scare us instead of slaughter low level libraries like tensorflow with Karis you can build industry ready models in no time with much less cool than Tia as we saw before and a higher extraction than that offer by tensorflow this allows for quickly and easily checking if a neural network will get your province off in addition you can do any architecture you can imagine from simple networks to more complex ones like our encoders convolutional or recurrent neural networks chaos models can also be deployed across a wide range of platforms like Android iOS where apps etc it is the best moment to be learning Charis Charis is now fully integrated into tension flow 2.0 so you can use the best of both worlds as needed and in the same code pipeline if as you dive deep into deep learning you find yourself needing to use low-level features for instance to have a final control of all your network applies gradients you could use tensor flow and tweak whatever you need now that you know better what Kara says and why to use it perhaps we shall discuss when and why to use neural networks in the first place neural networks are good feature structures since they've learned the best way to make sense of unstructured tape previously it was the domain Esper that had to set rules based on experimentation and heuristics to extract the relevant features of data neural networks can learn the best features under combination the camper from feature engineering themselves and that's why they are so useful but what are some structured data structured data is data that is not easily put into a table for instance sound video images etc it is also the type of data were performing feature engineering can be more challenging and that's why leaving this task to neural networks is a good idea if you are dealing we don't structured there you don't need to interpret the results and your problem can benefit from a known architecture then you probably should use neon networks for instance when classifying images of cats and dogs images are unstructured data we don't care as much about why the network knows it is a cat or a dog and we can benefit from a convolutional neural network so it is wise to use neural networks you will learn about the usefulness of convolutional neural networks later on in the course it is now time to review\n"