Menu

FastAI

aziz-acharki-gv3VWXwKrrA-unsplash

Unleashing the Power of CNNs on Non-Image Data: A Creative Twist

, , , , , , , 0

Deep learning models have proven their prowess in tasks ranging from identifying objects in images to recognizing handwriting. But what if your data doesn’t come in the form of images? Can you still harness the incredible power of Convolutional Neural Networks (CNNs)? The answer is a resounding “yes,” and today, we’ll explore just how to do that with a captivating example involving heart sounds.

Heart Sounds: Unveiling the Dual-Domain Magic

Heart sounds are typically recorded and can be examined in two fundamental ways: the time domain or the spectral domain. In the time domain, we track how the sound evolves over time, while the spectral domain delves into the sound’s frequency components. Each of these domains reveals a piece of the puzzle, but it’s when we dive into the realm of Wavelet Analysis that the real magic happens.

Wavelet Analysis: Where Time and Frequency Converge

Wavelet Analysis allows us to explore both time and frequency domains simultaneously. Instead of being limited to just one dimension, it combines information from both dimensions, enriching our data with a wealth of details beyond what we can obtain from either time or frequency alone. It’s like putting on 3D glasses for data analysis.

From Dual-Domain Data to Heat-Map Images

Now, here’s where it gets truly fascinating. This dual-domain representation lends itself beautifully to the creation of heat-map images. These images showcase how different frequencies play out over time, resembling a dynamic canvas of information. And guess what? These heat-map images are precisely what we need to tap into the world of CNNs.

CNNs: Ready to Work Their Magic

While CNNs are renowned for their image-processing abilities, they can effortlessly handle these heat-map images derived from heart sounds. There’s no need to reinvent the wheel or build a new model from scratch. With their established architectures like LeNet, AlexNet, GoogLeNet, and ResNet, CNNs become our partners in diagnosing heart defects from murmurs, all thanks to a little creative thinking.

Signal-to-Noise Ratio: A Cautionary Note

Of course, we should exercise caution. Not every image representation is equally informative. Maintaining a high signal-to-noise ratio is critical. We don’t want to obscure our diagnostic insights with unnecessary noise.

In Conclusion: Creativity Meets Cutting-Edge Technology

In the world of deep learning, innovation knows no bounds. Even when dealing with non-image data like heart sounds, we can creatively adapt CNNs to our advantage. No need to start from scratch; we can convert our data into an image format and let CNNs work their magic. This approach opens up exciting avenues for enhancing medical diagnosis and treatment. So, remember, with a little ingenuity, non-image data can also find its place in the world of CNNs.

Our FastAI coaching program at Craft With Code is your passport to mastery of deep learning. Dive into model building, explore real-world data, and transform imaginative concepts into practical solutions. Our hands-on approach guarantees the confidence to tackle a variety of data challenges, with expert guidance from seasoned instructors. Don’t miss out on the opportunity to unlock a world of possibilities—enroll today!”

mimi-thian-8kdA2IJsjcU-unsplash

Groove with Gradio or Jive with FastAPI

, , , , , , 0

Gradio’s speed, a digital cruise,

Swift and simple, sparks a muse!

In no time flat, your groove you’ll prove,

With Gradio in your corner, that’s the move.

FastAPI’s power, it rocks the stage,

Shape your endpoints like a sage.

Flexibility is the game it plays,

In countless ways, it’ll amaze for always.

Streamlit’s charm, a sweet compromise,

Stands up your app through day and night’s rise.

It don’t take much coding might, no lies,

Streamlit’s got you covered, that’s so right.

Now, which path will you choose, tech bro?

Take a moment, let your thoughts flow.

Don’t be shy, eat your dogfood, let your app grow,

With these allies, you’ll architect with the know-how.

In our FastAI coaching program, we go beyond the realm of creation. We equip you with the skills not only to build astounding AI applications but also to deploy apps quickly. Imagine the satisfaction of putting your creations directly into the hands of eager users or showcasing them to impress friends and family, or persuade potential employers or investors. You’ll learn to deploy AI in swift and efficient ways, ensuring your innovations make an impact on the world. So, if you’re ready to not just craft AI marvels but get them out there for the world to see, reach out to us on WhatsApp, and let’s embark on this transformative journey together. Your AI-powered future awaits!

logan-weaver-lgnwvr-vVuPvOzL3RA-unsplash

“Now that I have built my amazing AI app, how do I put it in the hands of my users?”

, , , , 0

Two words. Rest API. 

Rest is an acronym that stands for: Representational State Transfer. To put it simply, it’s a protocol that provides a blueprint for organizing data exchanges between machines across the vast landscape of the World Wide Web. Why “representation“? Because we adhere to guidelines in representing data. “State” because data mirrors the ever-evolving state of a system, encapsulating variables that change over time, such as stock market information. And “transfer” because this protocol facilitates the exchange of information. 

API is an acronym for Application Programming Interface, with “interface” being the operative term. It’s the digital gateway that enables internet users to interact, whether they’re human or machine. But why the need for such protocols? Picture a world without them, a digital Tower of Babel. Protocols are the glue holding the internet together, the guardians of order in the often chaotic realm of the World Wide Web. 

Let’s take a concrete example: Imagine you want to uncover the postal code of Cherrapunji, the renowned record-holder for the world’s highest rainfall. You can achieve this by simply entering its name into a website like worldpostalcode.com. The resulting webpage will pinpoint the postal code on a map, accompanied by relevant details like lat-long coordinates and neighboring postal codes. This seamless exchange of information is orchestrated by a protocol known as HTTP, or Hypertext Transfer Protocol. Any computer equipped with a web browser that ‘speaks’ HTTP can access the website as demonstrated, without any unforeseen hiccups. 

But what if your needs extend beyond mere manual inquiries? What if you’re dealing with a deluge of geo-tagged messages, seeking to decode their origins for continuous monitoring? In such cases, the manual approach becomes impractical. This is precisely where the Rest API emerges as a beacon of efficiency. A Rest API, accessed much like a webpage through a URL, responds to your questions with answers. It’s worth noting that sometimes, the answer might be a straightforward “I don’t know!” Each unique query corresponds to a specific URL template known as a Rest API endpoint, as illustrated here: https://app.zipcodebase.com/api/v1/code/city?apikey=YOUR-APIKEY&city=cherrapunji&state_name=meghalaya&country=in. 

Let’s unpack this URL, shall we? One notable distinction is “/code/city,” signaling its purpose as the endpoint for city-to-postal code queries. To formulate the URL with this endpoint, we provide parameters such as city, state_name, and country. Can you decipher the role of the “apikey” parameter? Hint: It’s a guardian of security. 

The response from a Rest API typically arrives in a format known as JSON (JavaScript Object Notation), appearing something like this: 

	{
   "query":{
      "code":"10005",
      "unit":"km",
      "radius":"10",
      "country":"us"
   },
   "results":[
      {
         "code":"10005",
         "city":"New York",
         "state":"NY",
         "distance":0
      },
      {
         "code":"10270",
         "city":"New York",
         "state":"NY",
         "distance":0.14
      },
      {
         "code":"10041",
         "city":"New York",
         "state":"NY",
         "distance":0.24
      },
	   ...
	  {
         "code":"11102",
         "city":"Astoria",
         "state":"NY",
         "distance":9.99
      }
   ]
}

While it might not appear overly impressive, the true power lies in its versatility. This plain, universal format, devoid of embellishments, makes automation a breeze—precisely why Rest API packs a punch. Furthermore, beyond retrieving information, Rest API encompasses all four of the CRUD verbs: Create, Retrieve, Update, or Delete information remotely. Most programming languages are fluent in JSON, capable of converting it into objects within the programming environment and vice versa, facilitating the exchange referred to as serialization. 

Having demystified the Rest API, you can see that exposing Rest API endpoints can place your amazing AI app directly into the hands of users. This opens up a world of possibilities for crafting innovative User Experiences around these endpoints. How easy is it to stand up a Rest API endpoint for your fantastic AI app? Surprisingly straightforward, depending on your choice and flexibility preferences. In future posts, we’ll delve into three options: Gradio, Streamlit, and Fastapi, each with its own trade-offs. Imagine not only learning to build incredible AI apps but also mastering the art of deploying them. 

Our FastAI coaching program is your gateway to the cutting-edge realm of artificial intelligence. Here, you’ll wield the power of Python and the enchantment of deep learning to craft AI applications that will astound the world. If you boast at least a year’s worth of Python experience, hesitate no longer. Reach out to us on WhatsApp, and let’s embark on this electrifying journey together.

kobu-code

How many lines of code does it take to train a deep learning model?

, , , , , , , 0

Six, if you know which ones.

Check out the secret sauce for training a savvy deep learning model to spot pets in pictures:

	from fastai.vision.all import *
path = untar_data(URLs.PETS)
dls = ImageDataLoaders.from_name_re(path, get_image_files(path/'images'), pat='(.+)_\d+.jpg', item_tfms=Resize(460), batch_tfms=aug_transforms(size=224, min_scale=0.75))
learn = vision_learner(dls, models.resnet50, metrics=accuracy)
learn.fine_tune(1)
learn.path = Path('.')
learn.export()

When I mentor AI novices, they often do a double-take at how little code it actually takes to kickstart a formidable deep learning machine. People have these wild notions about deep learning, thinking it’s all about:

  • A boatload of complex math
  • An ocean of data
  • A wallet-draining supercomputer setup

But here’s the reality check:

  • Basic high school math will do the trick
  • We can make do with just a pinch of data
  • And guess what? Most of the heavy lifting can be done on freebie computing resources

Get ready to craft some mind-blowing AI apps in our FastAI coaching program. We’ll arm you with the skills to build top-notch deep learning models. All you need is a minimum of one year’s experience in Python programming. Shoot us a message on WhatsApp now to snag your spot!