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Priyanka Vergadia

@pvergadia

28 Feb

Experimented with Teachable Machine today and created a #nocode classification model in less than 5 mins!

It's a web-based tool making #machinelearning models fast, easy, and accessible to everyone.

See how I did it 🧵👇

teachablemachine.withgoogle.com

#nocode #31DaysofML

How do I use it?

📌Gather data (upload it)
📌Train model (in the web interface)
📌Export the model (use it in your app)

What can I use to teach it?

📌Images
📌Sounds
📌Poses

We can use files or capture examples live through webcam/microphone.

Read 5 tweets

Priyanka Vergadia

@pvergadia

15 Feb

Day 14 #31DaysofML

🤔 How to pick the right #GoogleCloud #MachineLearning tool for your application?

Answer these questions
❓ What's your teams ML expertise?
❓ How much control/abstraction do you need?
❓ Would you like to handle the infrastructure components?

🧵 👇

@SRobTweets

@SRobTweets created this pyramid to explain the idea.
As you move up the pyramid, less ML expertise is required, and you also don’t need to worry as much about the infrastructure behind your model.

To lear more watch this video 👉

#31DaysofML 2/10

@SRobTweets

@SRobTweets If you’re using Open source ML frameworks (#TensorFlow) to build the models, you get the flexibility of moving your workloads across different development & deployment environments. But, you need to manage all the infrastructure yourself for training & serving

#31DaysofML 3/10

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Priyanka Vergadia

@pvergadia

14 Feb

Day 13 #31DaysofML

⚖️ How to deal with imbalanced datasets?⚖️
Most real-world datasets are not perfectly balanced. If 90% of your dataset belongs to one class, & only 10% to the other, how can you prevent your model from predicting the majority class 90% of the time?

🧵 👇

🐱🐱🐱🐱🐱🐱🐱🐱🐱🐶 (90:10)
💳 💳 💳 💳 💳 💳 💳 💳 💳 ⚠️ (90:10)
There can be many reasons for imbalanced data. First step is to see if it's possible to collect more data. If you're working with all the data that's available, these 👇 techniques can help

#31DaysofML 2/7

Here are 3 techniques for addressing data imbalance. You can use just one of these or all of them together:
⚖️ Downsampling
⚖️ Upsampling
⚖️ Weighted classes

#31DaysofML 3/7

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Priyanka Vergadia

@pvergadia

11 Feb

@kweinmeister

Since it is Day 10 of #31DaysofML it's perfect to discuss 1️⃣0️⃣ things that can go wrong with #MachineLearning Projects and what you can do about it!

I watched this amazing presentation by @kweinmeister that sums it all up

A 🧵

@kweinmeister

@kweinmeister 1️⃣ You aren't solving the right problem
❓What's the goal of your ML model?
❓How do you assess if your model is "good" or "bad"?
❓What's your baseline?
👉 Focus on a long-term mission with maximum impact
👉 Ensure that your problem is a good fit for ML

#31DaysofML

@kweinmeister

@kweinmeister 2️⃣ Jumping into development without a prototype
👉 ML project is an iterative process
👉 Start with simple model & continue to refine it until you've reached your goal
👉 Quick prototype can tell a lot about hidden requirements, implementation challenges, scope, etc

#31DaysofML

Read 11 tweets

Priyanka Vergadia

@pvergadia

10 Feb

Day 9 of #31DaysofML

💁‍♀️ I thought today I would share a tip that has helped me in my #MachineLearning journey
💡The best way to learn ML is to pick a problem that you feel excited about & let it guide your learning path. Don't worry about the terms or tools, it's all secondary

@GCPPodcast

Here's an example. Few weeks ago I wanted to live translate an episode of @GCPPodcast. The first question I asked myself was:
🤔 Does any video/audio translation API already exist?
🔹 If so - I would give that a try
🔹 If not, I would create it from scratch

#31DaysofML (2/5)

@GCPPodcast

@GCPPodcast Next, I started digging into the Media Translation API which would translate audio & video data.
My point is:
📌 You don't always need to create a model
📌 Save yourself time & resources by using the models that already exist (if they server your purpose)

#31DaysofML (3/5)

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Priyanka Vergadia

@pvergadia

8 Feb

Day 7 of #31DaysofML

⬇️ Reducing Loss ⬇️
An iterative process of choosing model parameters that minimize loss
👉 Loss function is how we compute loss
👉 Loss function curve is convex for linear regression

A 🧵 👇

Calculating loss for every value of W isn't efficient: most common way is called gradient descent
👉 Start with any value of w, b (weights & biases)
👉 Keep going until overall loss stops changing or changes slowly
👉 That point is called convergence

#31DaysofML 2/4

As you probably already guessed, gradient is a vector with:
👉 Direction
👉 Magnitude
Gradient descent algorithms multiply the gradient by a scalar known as the learning rate (or step size) to determine the next point.

#31DaysofML 3/4

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