Basic Concepts of Deep Learning – Part3

By: Himanshu Singh

This is the third and last installment of a 3-part series, featuring 31 Question-and-Answer pairs designed to help you easily understand the basic terms and their purpose in Deep Learning.

For the first 10 questions, check out Part 1 here: Basic Concepts of Deep Learning - Part 1

For the second 10 questions, check out Part 2 here: Basic Concepts of Deep Learning - Part 2

This article is structured in a Question-and-Answer format, with key takeaways highlighted under a Learnings section after each Q&A. Although it is a detailed and comprehensive read, the format ensures clarity, smooth flow, and an engaging learning experience.

❓Question 21: What is backpropagation in model training?

🔍Answer: Backpropagation is a key process in training a neural network, allowing the model to adjust its weights and biases to reduce prediction error. The process works as follows:

• Error Estimation: After forward propagation, the prediction error is estimated based on the difference between the predicted values (y′) and the actual values (y).
• Overall Error Contribution: Each node in the neural network contributes to the overall error. This contribution depends on the weights and biases of the node and how well they model the relationship between the feature and target variables.
• Weight and Bias Adjustment: Backpropagation works to adjust the weights and biases of each node. The goal is to minimize the error contribution of each node. By systematically tuning the weights and biases, the network learns to better model the data, reducing the overall error.

💡Learnings:

• Backpropagation is used to adjust weights and biases in a neural network.
• It minimizes prediction error by systematically refining the network's parameters.
• Each node's contribution to error is determined by its weights and biases.
• Adjusting weights and biases lowers the error contribution of each node, improving the model's performance.

❓Question 22: How does backpropagation work?

🔍Answer: Backpropagation works by iteratively adjusting weights and biases in a neural network in the reverse direction of forward propagation. The steps involved are:

• Start from the Output Layer: Compute a delta value for the output layer based on the overall error. The delta value represents the adjustment needed for the weights and biases in this layer.
• Update Weights and Biases: Apply the delta value to update the weights and biases in the output layer. This results in new values for these parameters.
• Move to the Previous Layer: Calculate a new delta value for the previous layer using the updated weights and biases from the current layer. Apply this delta to adjust the weights and biases in the previous layer.
• Repeat the Process: Continue computing deltas and updating weights and biases layer by layer, moving backward through the network until reaching the input layer.
• Automated Computation: Deep learning libraries handle the complex calculations involved in backpropagation.
• Final Outcome: At the end of backpropagation, the network has an updated set of weights and biases that reduce the overall prediction error.

💡Learnings:

• Backpropagation adjusts weights and biases in the reverse direction of forward propagation.
• The process begins at the output layer and moves backward through the network.
• Delta values are computed and applied layer by layer to refine weights and biases.
• The process continues until all layers are updated.
• Deep learning libraries automate backpropagation computations.
• The final result is an optimized set of weights and biases that minimize prediction error.

❓Question 23: What is Gradient Descent?

🔍Answer: Gradient descent is an iterative optimization process used in training machine learning models to minimize error and improve the model's performance. The steps involved are:

• Forward Propagation: Predict outcomes based on the current weights and biases. Compute the error using a cost function.
• Backward Propagation: Propagate the error backward through the network. Adjust weights and biases to reduce the error.
• Iteration: Repeating this process of forward propagation, error estimation, backward propagation, and weight adjustment constitutes one pass of learning. This iterative process is called gradient descent.
• Error Reduction: With each iteration, the weights and biases are refined, and the error estimated by the cost function reduces. The process continues until the error oscillates around or approaches zero.
• Hyperparameters: Additional hyperparameters can control the learning process, such as: Speed up or slow down learning. Handle situations where error stops reducing (e.g., learning rate, momentum).

💡Learnings:

• Gradient descent is the repeated process of forward propagation, error estimation, backward propagation, and weight adjustment.
• It minimizes the error estimated by the cost function.
• Iteratively refining weights and biases reduces the overall prediction error.
• The process continues until the error approaches zero or a stopping criterion is met.
• Hyperparameters like learning rate and momentum control the learning speed and behavior. 

❓Question 24: What is a batch?

🔍Answer: A batch is a subset of training samples processed by a neural network in a single pass. The key aspects of a batch are:

• Training Subset: The training dataset is divided into one or more batches. Each batch is processed individually by the neural network.
• Processing Steps: Forward propagation is performed for the batch. Cost functions are calculated to estimate the error. Weights and biases are updated after processing each batch.
• Batch-to-Batch Updates: When a new batch is processed, the neural network uses the updated weights and biases from the previous batch.
• Types of Gradient Descent:

Batch Gradient Descent: When the batch size equals the entire training dataset size.

Mini-Batch Gradient Descent: When the batch size is smaller than the training dataset size.

💡Learnings:

• A batch is a subset of training samples used in a single forward propagation pass.
• The training dataset is divided into batches to optimize training efficiency.
• Weights and biases are updated after processing each batch.
• Batch gradient descent processes the entire dataset at once, while mini-batch gradient descent processes smaller subsets. 

❓Question 25: What is an epoch?

🔍Answer: An epoch refers to one complete pass of the entire training dataset through the neural network during the learning process. Key points about epochs include:

• Multiple Passes: The entire training dataset is sent through the neural network multiple times during training.
• Relation to Batches: An epoch consists of one or more batches. Each batch is processed individually in a single epoch.
• Weights and Biases Update: During each epoch, batches are repeatedly sent through the neural network, with weights and biases updated after processing each batch. Each epoch works with progressively refined weights and biases.
• Impact of Epoch Count: More epochs can lead to better model accuracy but may increase training time.
• Example Calculation:

Training dataset size: 1000 samples.

Batch size: 128 samples.

Epoch count: 50.

Number of batches in each epoch: 1000/128 = 8 (last batch may have fewer samples).

Total passes (iterations): 8×50 = 400

Weights and biases are updated 400 times during the training.

• Hyperparameter Tuning: Epoch count and batch size is hyperparameters tuned to balance model accuracy and training efficiency.

💡Learnings:

• An epoch is a complete pass of the training dataset through the neural network.
• Each epoch consists of one or more batches.
• Weights and biases are updated after processing each batch.
• Higher epoch counts may improve accuracy but increase training time.
• Epochs and batch sizes are hyperparameters tuned to optimize training performance. 

❓Question 26: What is validation and testing in model training?

🔍Answer: Validation and testing are processes used during model training to assess the model's performance and generalizability on independent data. Here's an overview:

• Purpose:
• Validation: Measures how well the model performs on unseen data during training, helping to tune hyperparameters and avoid overfitting.
• Testing: Evaluates the final model's performance on a completely independent dataset to estimate out-of-sample error.

• Data Isolation: During input preparation, the dataset is divided into:
• Training set: Used for model learning.
• Validation set: Used for tuning and validation during training.
• Test set: Held back until the final evaluation to test the model's generalizability.

• Usage: The validation set helps monitor the model's performance during training and refine parameters. The test set provides a final, unbiased evaluation of the model after training is complete.

💡Learnings:

• Validation and testing measure a model's performance on independent datasets.
• The validation set is used during training to optimize the model and avoid overfitting.
• The test set evaluates the final model's generalizability to unseen data.
• Proper data isolation during input preparation ensures reliable validation and testing. 

❓Question 27: What is the validation step?

🔍Answer: Validation is the process of evaluating a model's performance on a separate dataset, the validation set, during training to ensure its generalizability and avoid overfitting. Key points about validation include:

• Purpose: To measure how well the model performs on data it has not seen during training. To monitor and compare in-sample errors (training data) with out-of-sample errors (validation data).
• Process: After each epoch, the model is used to make predictions on the validation dataset. The accuracy and loss are measured for the validation set. These metrics are compared with the training metrics to ensure no significant deviation.
• Model Tuning: If validation metrics indicate poor performance or significant deviation from training metrics, the model is fine-tuned. Hyperparameters, architecture, or training configurations can be adjusted based on validation results.
• Outcome: The validation process ensures that the model generalizes well to unseen data and helps avoid overfitting.

💡Learnings:

• Validation measures a model's performance on unseen data during training.
• It helps compare in-sample (training) errors with out-of-sample (validation) errors.
• Significant deviations in validation metrics indicate potential overfitting or underfitting.
• Validation results guide model fine-tuning and optimization. 

❓Question 28: What is the testing step?

🔍Answer: The testing step is the final phase of model evaluation, where the model's performance is assessed using the test dataset. This step provides an unbiased measure of the model's generalizability. Key points about the testing step include:

• Purpose: To evaluate the final model's accuracy and error rates on unseen data. To measure the model's performance in terms of its ability to generalize beyond the training and validation datasets.
• Timing: Performed only once, after all training, validation, and fine-tuning processes are completed.
• Process: The test dataset, which has been kept separate during training and validation, is used to make predictions. The model's predictions are compared with the actual values in the test dataset to compute final performance metrics, such as accuracy and error rates.
• Outcome: The results from the testing step provide the final assessment of the model's effectiveness and readiness for deployment.

💡Learnings:

• The testing step evaluates the model's performance on a completely unseen dataset.
• It is performed only after training and validation are complete.
• Test results provide an unbiased measure of the model's generalizability.
• The testing step is crucial for determining whether the model meets performance expectations before deployment. 

❓Question 29: What is an ANN model and what does it contain at the end of the training?

🔍Answer: An Artificial Neural Network (ANN) model is a computational representation of a neural network. At the end of training, it contains:

• Parameters:
• Weights and Biases: These are the core parameters learned during training. The total number of parameters refers to the combined count of all weights and biases in the model.

• Hyperparameters:
• Structure-related: Number of layers, nodes in each layer, and activation functions.
• Training-related: Cost functions, optimizers, learning rates, batch sizes, and epoch values. These are predefined and adjusted during the training process but not learned directly.

• Model File: The trained model is typically stored as a file containing all the learned parameters (weights and biases) and hyperparameter configurations. These files can be saved, shared, and loaded into other environments for inference or further training.

💡Learnings:

• An ANN model consists of learned parameters (weights and biases) and predefined hyperparameters.
• The total parameter count is the sum of all weights and biases in the network.
• Hyperparameters define the structure and training configurations of the model.
• The trained model can be saved, shared, and reused for various purposes. 

❓Question 30: Once you have a model, what does the prediction process look like?

🔍Answer: The prediction process in a trained model closely resembles the forward propagation step, with the following stages:

• Input Preparation: Pre-process and prepare the input data, which includes only the feature attributes for prediction. The target value is unknown during this step.
• Forward Propagation: Pass the input data through the layers of the neural network. Compute the outputs at each node using their final learned weights and biases. Derive the outcomes at the final layer.
• Post-Processing: Convert the raw predictions into meaningful business representations, such as class labels or probabilities, depending on the problem.

💡Learnings:

• The prediction process mirrors the forward propagation step but uses unseen data without known target values.
• Inputs are pre-processed before being passed through the neural network.
• Final predictions may require post-processing to make them interpretable for business or practical use cases.

❓Question 31: How do we build neural networks for a use case?

🔍Answer: Building neural networks for a specific use case typically involves leveraging existing architectures and fine-tuning them rather than designing them from scratch. The process includes:

• Utilizing Existing Research: The neural network community shares knowledge and published papers on successful architectures. Start with a related architecture that has been proven effective for similar tasks.
• Implementing Proven Architectures: Use open-source implementation code available in repositories to set up the initial neural network. Fine-tune the chosen architecture according to the specific requirements of your use case.
• Leveraging Open Source Models: Utilize pre-trained models available in standardized formats compatible with popular deep learning frameworks. These models come with trained parameters and hyperparameters that can save time and improve performance.
• Popular Neural Network Architectures:
• LeNet5: Early model for document and handwriting recognition.
• AlexNet: A conventional CNN for image recognition.
• ResNet: A CNN that addresses limitations of traditional architectures.
• VGG: Another widely used CNN architecture.
• LSTM: A recurrent neural network for sequence prediction in text.
• Transformers: Cutting-edge architecture transforming generative AI applications.

💡Learnings:

• Building neural networks often involves using established architectures rather than starting from scratch.
• The neural network community provides valuable resources and research for building upon existing knowledge.
• Open-source implementation code and pre-trained models can significantly streamline the development process.
• Familiarity with popular architectures helps in selecting the right starting point for specific use cases. 

I hope this helps to understand fundamental concepts and processes in Deep Learning and Neural Network Models.

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