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How can you incorporate your expertise into a neural network model?

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Neural networks are powerful machine learning models that can learn from complex and diverse data. But they are not magic. They still need human input and guidance to achieve optimal results. How can you incorporate your expertise into a neural network model? Here are some tips and examples to help you.

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Somesh Chatterjee

Sr. Staff Data Scientist
Elisa Terumi, PhD

Linkedin Top Voice | AI | Machine Learning | Natural Language Processing | LLM | GenAI | Software Development
Cmdr (Dr.?) Reji Kurien Thomas , FRSA, MLE?

I Empower Sectors as a Global Tech & Business Transformation Leader| Stephen Hawking Award| Harvard Leader | UK House…

1 Choose the right architecture

The architecture of a neural network refers to the number and type of layers, neurons, and connections that make up the model. Different architectures are suited for different tasks and domains. For example, convolutional neural networks (CNNs) are good for image processing, while recurrent neural networks (RNNs) are good for natural language processing. You should choose the architecture that best matches your problem and data. You can also customize the architecture by adding or removing layers, changing the activation functions, or applying regularization techniques.

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2 Select the appropriate features

The features are the inputs that you feed to the neural network. They represent the characteristics or attributes of your data that are relevant for the task. For example, if you want to classify images of animals, you might use features such as color, shape, texture, or size. You should select the features that capture the most important and distinctive aspects of your data. You can also use feature engineering or feature extraction methods to create new or better features from your raw data.

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3 Train and tune the model

The training and tuning of a neural network involve finding the optimal values for the parameters or weights that define the model. This is done by using a learning algorithm that minimizes a loss function, which measures the difference between the model's predictions and the actual outcomes. You should train and tune the model using a suitable dataset that reflects your domain and goal. You should also use validation and testing sets to evaluate the model's performance and avoid overfitting or underfitting. You can also use techniques such as cross-validation, grid search, or random search to find the best hyperparameters, which are the settings that control the learning process.

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Cmdr (Dr.?) Reji Kurien Thomas , FRSA, MLE?

I Empower Sectors as a Global Tech & Business Transformation Leader| Stephen Hawking Award| Harvard Leader | UK House of Lord's Awardee | Fellow Royal Society | CyberSec I 200x LinkedIn Top Voice | CCISO CISM
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Incorporate expertise into a neural network model by – Training with Domain-Specific Data - Use datasets enriched with expert insights to teach the model nuanced patterns. Feature Engineering - Craft input features that highlight domain-relevant information, guiding the model's focus. Custom Loss Functions - Design loss functions that penalise inaccuracies in critical areas identified by experts. Hyperparameter Tuning - Adjust the model's hyperparameters based on expert knowledge of the problem space. Transfer Learning - Apply pre-trained models on related tasks to leverage existing knowledge. Expert Validation. Continuous Learning.

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4 Incorporate domain knowledge

Domain knowledge is the specific and relevant information that you have about your field or topic. It can help you improve the neural network model by providing insights, constraints, or feedback. For example, if you want to predict the price of a house, you might use domain knowledge such as the location, the size, the condition, or the market trends. You can incorporate domain knowledge into a neural network model by using prior distributions, regularization terms, custom loss functions, or human-in-the-loop systems.

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Somesh Chatterjee

Sr. Staff Data Scientist
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More domain knowledge you can incorporate into your features, the simpler the model can become. If you have right features, you may not even need a lot of data and a deep neural network to solve the problem. An e.g., if you are dealing with time-series data and frequency features makes sense in your domain then a simpler model like RandomForest using Frequency features (after applying Fourier transform) may work as well as a deep CNN using raw time-series data. RandomForest is significantly faster to train and requires much lesser data as well.

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5 Transfer learning and fine-tuning

Transfer learning and fine-tuning are methods that allow you to leverage existing neural network models that have been trained on similar or related tasks or domains. They can help you save time and resources, as well as overcome data limitations or challenges. For example, if you want to recognize faces, you might use a pre-trained neural network model that has been trained on a large dataset of human faces. You can transfer learning and fine-tune the model by adapting the last layer or layers to your specific task or domain.

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Elisa Terumi, PhD

Linkedin Top Voice | AI | Machine Learning | Natural Language Processing | LLM | GenAI | Software Development
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Usamos muito fine-tuning no nosso grupo de pesquisa, adicionando uma camada linear no topo de um modelo pré-treinado, com dados específicos de uma tarefa ou domínio.

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Dr. Marija K.

SAP Consulting, AI for Business Processes
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Fine-tuning large language models is an effective method for creating models trained with domain-specific data. Fine-tuning also mitigates hallucinations, resulting in increased precision. Large language models (LLMs) utilize transformer architecture, but distinctions exist among LLMs depending on whether they are based on encoder, decoder, or encoder-decoder transformers. The architecture of LLMs have impact, how they perform for different use case. When choosing an LLM, it's essential to consider the specific use case and select the appropriate LLM accordingly.

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6 Experiment and iterate

The final tip is to experiment and iterate with your neural network model. There is no one-size-fits-all solution for every problem or domain. You should try different architectures, features, parameters, and techniques to see what works best for your case. You should also monitor and measure the results, and compare them with your expectations and objectives. You should also learn from your failures and successes, and use them to improve your model and your expertise.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you incorporate your expertise into a neural network model?

1

2

3

4

5

6

7

1 Choose the right architecture

2 Select the appropriate features

3 Train and tune the model

4 Incorporate domain knowledge

5 Transfer learning and fine-tuning

6 Experiment and iterate

7 Here’s what else to consider

Machine Learning

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How can you incorporate your expertise into a neural network model?

1

2

3

4

5

6

7

1 Choose the right architecture

2 Select the appropriate features

3 Train and tune the model

4 Incorporate domain knowledge

5 Transfer learning and fine-tuning

6 Experiment and iterate

7 Here’s what else to consider

Machine Learning

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