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The training process for Generative AI models enables them to produce coherent, relevant outputs. The steps involved in the training and deployment processes are:
Data Collection
Gathering a large and diverse dataset relevant to the model’s intended application. To ensure high-quality input, remove duplicates, errors, and irrelevant information.
Preprocessing
Breaking down the text into smaller units such as words or subwords. Converting text to a consistent format.
Model Architecture Design:
Selecting an appropriate model architecture and setting the hyperparameters, such as:
- Number of layers
- Hidden units
- Learning rate
- Batch size
Training:
Initializing model weights and passing input data through the model to generate predictions. Comparing predictions against actual data using a loss function. Computing gradients of the loss with respect to model parameters. Updating model parameters using an optimization algorithm. Repeating forward and backward passes over many iterations. Periodically evaluating the model to monitor performance.
Fine-Tuning
After initial training on a broad dataset, optionally fine-tune the model on a more specific dataset.
Testing:
Testing the model on a separate test dataset to assess its generalization capability. Measuring performance using relevant metrics and comparing it against benchmarks. Deploying the trained model for real-world use.
Monitoring:
Continuously monitoring the model’s performance and updating it based on new data or changes in requirements.
Iteration:
Based on feedback and new data, the model can be fine-tuned periodically. This helps to maintain and improve performance.
Generative Adversarial Networks (GANs) are a class of machine learning frameworks. It is designed for generating new data samples that resemble a given training dataset. GANs consist of two main components,
- Generator,
- Discriminator.
Training Process of GANs
The training of GANs involves the following iterative process:
Initialize the weights of both the generator and discriminator networks.
Train the Discriminator:
- Sample real data.
- Generate a batch of fake data samples using the generator.
- Compute the discriminator’s loss using both real and fake samples.
- Update the discriminator’s weights to minimize the loss.
Train the Generator:
- Generate fake data.
- Compute the generator’s loss based on how well it fools the discriminator.
- Update the generator’s weights to minimize the loss.
Iterate to optimize both networks in a way that improves the generator’s ability to produce realistic data and the discriminator’s ability to distinguish real from fake data.
To enroll in our Generative AI training, No prerequisites are needed. Our Gen AI Course is suitable for all IT aspirants as it starts from scratch. This course can be customized based on the student’s needs. The basic understanding and knowledge of the skills mentioned below will be helpful.
- Foundational knowledge of machine learning
- Programming language skills
- Basics of probability and linear algebra
