DEV Community: Tahara Kazuki

5 AI Training Steps & Best Practices

Tahara Kazuki — Tue, 20 Feb 2024 17:24:59 +0000

One of the biggest challenges in developing AI systems is training the models.

To help developers improve the process of building AI, this article explores 5 steps and best practices to train your AI models effectively. You can also explore how to train large language models.

Dataset preparation

Data collection and preparation is a prerequisite for training AI and machine learning algorithms. Without quality data, machine & deep learning models cannot perform the required tasks and mimic human behavior.

Hence, this stage of the training process is of utmost importance.

1.1. Collect the right data

Custom crowdsourcing

Private collection or in-house data collection

Precleaned and prepackaged data sets

Automated data collection

1.2. Data preprocessing

Data gathered to train machine learning models can be messy and needs preprocessing and data modeling to be prepared for training.

Data processing involves enhancing and cleaning the data to improve the overall quality and relevancy of the whole dataset.

Data modeling can help prepare datasets for training machine learning models by identifying the relevant variables, relationships, and constraints that need to be represented in the data. This can help ensure that the dataset is comprehensive, accurate, and appropriate for the specific AI/ML problem being addressed.

1.3. Accurate data annotation

After the data has been gathered, the next step is to annotate it. This involves labeling the data to make it machine-readable. Ensuring the annotation quality is paramount to ensuring the overall quality of the training data.

Model selection

The complexity of the problem

The size and structure of the data

The computational resources available

The desired level of accuracy

Initial training

After data collection and annotation, the training process can start by inputting the prepared data into the model to identify any errors that might surface.

Expanding the training dataset

Leveraging data augmentation

Simplifying the model can also help avoid overfitting. Sometimes the complexity of the model makes it overfitting even when the dataset is large.

Training validation

Once the initial training phase is complete, the model can move to the next stage: validation. In the validation phase, you will corroborate your assumptions about the performance of the machine learning model with a new dataset called the validation dataset.

Testing the model

Test the mode: Use the trained model on the test data.

Compare results: Evaluate the model’s predictions against actual values.

Compute metrics: Calculate relevant performance metrics (e.g., accuracy for classification, MAE for regression).

Error analysis: Investigate instances where the model made errors.

Embedding concept

Tahara Kazuki — Tue, 20 Feb 2024 00:23:05 +0000

I'm going to post some basics related to AI.

An embedding is a relatively low-dimensional space into which you can translate high-dimensional vectors. Embeddings make it easier to do machine learning on large inputs like sparse vectors representing words. Ideally, an embedding captures some of the semantics of the input by placing semantically similar inputs close together in the embedding space. An embedding can be learned and reused across models.

First of all, to understand embedding, you need to know what a vector is in computer data.

Vectors are 1-dimentional Arrays

A vector can be represented as a matrix

This is the vector concept in computer data processing.

To put it simply, embedding is expressing data as a vector.

In other words, it is expressed as a determinant.

Embedding is the foundation of AI and is something that anyone pursuing AI should know. I hope this article will be of some help to beginners learning AI.