Machine learning development process
Beginner guide for machine learning development process
Introduction
Starting a project is a bit overwhelming for beginners. This guide simplifies the process into clear and manageable steps.
Let’s dive in!
Iterative loop of ML development
Choose architecture: First, decide the overall architecture of your system like what model to select, what data to use, etc.
Train Model: You will train the model, In the first shot it will never work as how you want it to
Diagnostics: you will implement diagnostics such as looking at the bias, variance, and error analysis. and on the basis of diagnostics, you make decisions about whether you want your neural network bigger, change the regularization parameter, or add features or subtract features. and then you go around this loop
Error Analysis
Error Analysis is a process used to evaluate the mistakes made by a learning algorithm in order to improve its performance.
Purpose: To identify and understand the types of errors the algorithm is making
Process:
Review misclassified examples from the validation set.
Group these examples based on common traits or properties
Analyzing the frequency of the specific type of errors (e.g, misclassified spam emails)
Outcome:
Gain insights into where the algorithm is failing
prioritize which issues to address based on their impact on overall performance
This method helps in making informed decisions about data, collection, feature engineering and algorithm adjustments, ultimately leading to better model performance.
Add Data
Collecting More data
Targeted Collection: Instead of gathering random data , focus on collecting more example of specific types that your model struggles with.
For Instance, if your model has trouble identifying spam emails related to pharmaceuticals, you might want to collect more examples of those specific emails.
Error Analysis: Analyze where your model is making the mistakes
Data Augmentation:
modifying an existing training example to create a new training example,
As you can see in the image, these would be ways of taking a training example and applying a distortion of transformation to the input, In order to come up with another example that has the same label. And creating additional examples like this holds the learning algorithm, do a better job in learning how to recognize the Butterfly
The distortion introduced should be a representation of the type of distortions in the test set
Data Synthesis:
Generating New Data: Instead of modifying existing data, you can create entirely new examples from scratch. For example, in optical character recognition (OCR), you can use different fonts and colors to generate synthetic images of text
Transfer Learning
Using Related Data: If you don’t have enough data for your specific task, you can use data from a different but related task.
For example, if you’re training a model to recognize specific types of animals but lack sufficient images, you might use a model trained on a broader dataset of animals to help improve your model's performance
Quality Over Quantity
Focus on Relevant Data: Sometimes, it’s not just about having more data but having the right kind of data.
Iterative Process
Continuous Improvement: Adding data is not a one-time task. As you gather more data and improve your model, you should continuously analyze its performance and look for new data to add.
Transfer learning: using data from a different task
Transfer Learning is a powerful technique in ML that allows you to leverage knowledge gained from one task to improve performance on a different but related task
It involves taking a pre-trained model (trained on a large dataset) and fine-tuning it for a specific task with a smaller dataset.
Purpose: It helps in scenarios where you have limited data for your specific task but can utilize a model trained on a larger dataset.
How does transfer learning work?
Pre-training:
A neural network is trained on a large dataset (e.g., ImageNet with millions of images).
The model learns to recognize various features (shapes, etc.) that are useful across different tasks.
Fine-tuning:
You take the pre-trained model and modify it for your specific task.
Typically, you replace the final output layer to match the number of classes in your new task.
You can choose to:
Freeze the earlier layers: Keep the weights of the earlier layers unchanged and only train the new output layer.
Fine-tune all layers: Initialize the weights of the earlier layers with the pre-trained weights and train the entire model on your new dataset.
Full Cycle of Project Learning Process
Steps for machine learning project
Scope Project: Know what is the project about and what you want to work on.
Collect Data: Decide what data to train your ML system
Train Model: for instance, train a speech recognition system, carry out error analysis, and iteratively improve the model
Deploy in production: Deploy, monitor, and maintain the system in case the performance gets worse to bring us performance back up and repeat this steps until you will get your desired input.
Thank you for reading this post, Hope it added some value!