Just like the human brain, the Neural Net needs to be trained. Although they are fundamentally different, both need large amounts of data to form an understanding of the world. Thus, high-quality training data is paramount for developing AI models.
The quality of the input data profoundly impacts the efficacy of the output or predictions of the AI model.
OpenAI, for example, has three main sources for its data:
Publicly available information: This is data accessible on the internet.
Licensed data: OpenAI licenses datasets from third-party sources. These may include proprietary datasets and are probably company secrets.
User-provided data: This may come in the form of labeled examples or other forms of human input.
This is where the expertise of data scientists, well-versed in computer science and various programming languages, becomes the key.
3.1 Understanding the challenges of training
Overfitting
This occurs when the model learns to capture noise or random fluctuations in the training data rather than underlying patterns. As a result, the model performs well on the training data but fails to generalize and, therefore, performs poorly with real-world data.
To address overfitting, regularization methods penalize large model weights, dropout randomly removes units during training to enhance robustness, and data augmentation introduces variations in the training data, promoting exposure to diverse scenarios.
Underfitting
In contrast, underfitting occurs when the model is too simplistic to capture the underlying patterns in the data. This results in poor performance both on the training data and unseen data. Underfitting may arise from using overly simple models or insufficient training data.
Balancing between these two extremes—overfitting and underfitting—requires careful experimentation and fine-tuning of various model parameters.
Insufficient data
When the amount of data available for training an AI model is limited, the model may not capture the full complexity of the underlying data distribution. As a result, the model may generalize poorly to unseen data.
Insufficient training data is a common challenge across various fields, particularly in emerging or specialized domains where data collection is limited or expensive. In industries like healthcare, where niche datasets are crucial for specialized applications such as medical imaging or personalized treatments, obtaining sufficient and diverse data can be especially challenging due to legal and moral concerns.
Developers found creative solutions to these issues, such as mirroring, rotation, or adding noise to existing data to increase the size of the training dataset artificially.
Biased data
Bias in the training data occurs when certain subsets or categories of data are overrepresented or underrepresented compared to others. This can lead to skewed predictions and unfair treatment of different groups or individuals.
For example, if a facial recognition system is trained primarily on images of individuals from one demographic group, it may perform poorly on individuals from underrepresented groups. To address bias in training data, it's essential to carefully curate and balance the dataset to cover diverse representation across different groups. Additionally, techniques such as data preprocessing, stratified sampling, and bias correction algorithms can help mitigate bias in AI models.
Hyperparameter tuning
Hyperparameters are parameters that are set before the training process begins and control aspects of the model's architecture, learning process, and optimization strategy.
Tuning typically requires training and evaluating multiple versions of the model with different hyperparameter settings. This process can be computationally intensive, while high dimensionality makes it difficult to explore the entire hyperparameter space efficiently.
This post is just one part of the blog on How to Make an AI Model. You can check out the full tutorial here
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