From the course: Deep Learning with Python: Optimizing Deep Learning Models

Batch gradient descent - Python Tutorial

From the course: Deep Learning with Python: Optimizing Deep Learning Models

Batch gradient descent

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- [Instructor] In deep learning, optimization algorithms play a fundamental role in how neural networks are trained. They govern how the weights and biases of a model are updated during each iteration of training to minimize the loss function. By iteratively adjusting parameters based on the gradient of the loss function, these algorithms aim to find the optimal values that yield the best predictions. Kairos provides a variety of optimization algorithms, ranging from simple gradient-based methods to more advanced adaptive approaches. Each method has its unique strengths and limitations, and understanding these differences is essential for choosing the right approach. One of the most fundamental optimization algorithms is batch gradient descent. It calculates the gradients of the loss function Using the entire training dataset in a single pass, then uses this gradient to update the model's parameters. Imagine trail running downhill. Batch gradient descent calculates the best path down the hill by considering all possible routes at once. This comprehensive approach ensures that each update moves the model closer to the optimal solution in a stable and predictable manner. One of the significant benefits of batch gradient descent is its stability. Since it uses all the data available, the updates to the model's parameters are consistent, and moves steadily towards minimizing the loss function. This means that convergence towards the minimum loss is smooth and can be easy to track and predict. Another advantage is its deterministic nature. Given the same dataset and initial conditions, batch gradient descent will produce the same updates every time you run it. This predictability can be beneficial when you need reproducible results, such as in academic research or when verifying the consistency of a model. Moreover, batch gradient descent is straightforward to implement and understand. Its algorithmic simplicity makes it an excellent starting point for those new to deep learning and optimization techniques. However, batch gradient descent is not without its limitations. One of the primary drawbacks is that it can be computationally intensive. Processing the entire dataset in each iteration requires significant computational resources, especially when dealing with large datasets common in real-world applications. This can lead to longer training times which may not be practical in time-sensitive projects. Additionally, the requirement to load the entire dataset into memory for each update can be problematic. For massive data sets, this might exceed the available memory capacity, leading to system slowdowns or crashes. This necessitates the use of high-performance computing resources, which may not be accessible to everyone. Another limitation is the possibility of getting stuck in local minima. In complex non-convex loss services, typical of deep learning models, batch gradient descent may converge to a suboptimal solution. Since it takes the average gradient over the entire dataset, it may not have the flexibility to escape these local minima and find a better global minima.

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