A Quick Overview About Gradient Descent Algorithm And Its Types

Introduction Stochastic Gradient Descent

Stochastic gradient descent is the method used for optimal configuration in machine learning. Stochastic gradient descent is for the reduction of error in the machine learning algorithm. It makes minor changes or adjustments in the machine learning network. The stochastic gradient descent is a relevant approach to fit the linear classification. It is a powerful technique in the stochastic gradient algorithm. Stochastic gradient descent can minimize the work on the computer as it is fast. The stochastic gradient descent algorithm takes up small batches for training. These batches are small for training, and the training becomes easier due to this type. Let us know the pros and cons of stochastic gradient descent. 

Pros 

• The calculation of stochastic gradient descent is much better than gradient descent. 
• No algorithm expects stochastic gradient descent to give a better result than the stochastic gradient algorithm. 
• Minibatch help the stochastic gradient descent to give faster results and continuously update the system.
• Popularity of stochastic gradient descent has increased as in computation time. The stochastic gradient descent can give out the maximum result. 
• The stochastic gradient descent algorithm guarantees that there will be minimized error-free data when it is optimized by stochastic gradient descent
• It is efficient and easy to work with. 

Cons

• It requires a hyperparameter and several iterations 
• Sensitive to feature scaling. 

Gradient Descent Algorithm and its types 

The gradient descent algorithm is the most common technique to train the machine learning algorithm for the initial results with minimum errors. It is made to train the neural network. The gradient descent is as old as the 18th century. It was first discovered by 

 “Augustin-Louis Cauchy. The gradient Descent Algorithm is used for the local minimum function. The Gradient descent algorithm does the task of minimizing the objective function. The gradient descent algorithm aims to reduce the cortex function by retracting parameter updates. The model works until the parameter is zero. 

There are mainly three types of Gradient descent algorithms.

Batch gradient descent 

Batch gradient descent updates the model once the error evaluation has been done. The error is aggregated at each point during the training. This procedure is known as the training epoch. The ability of Batch gradient descent is low as it takes minimum computation time. The Batch gradient descent has to store much data in the model.  

Stochastic gradient descent

As we have already discussed earlier, the Stochastic gradient descent keeps updating the system every time. Stochastic gradient descent uses speedy processes to minimize the error. It has a large capacity for storage. It takes less computation time. 

Minibatch gradient descent 

Minibatch gradient descent mixes the types of gradient descent – batch gradient descent and stochastic gradient descent. The minibatch gradient descent divides the data into small mini-batches and then starts training the model for error reduction. It has the efficiency of batch gradient descent and stochastic gradient descent speed. 

How does Gradient Descent Machine Learning work? 

 Gradient Descent Machine Learning works on the model of reducing the algorithm that minimizes the function. To understand the working of Gradient Descent Machine Learning

Firstly we need to understand linear regression. this is the formula of linear regression 

 y = MX + b, where m is the slope and b is the intercept on the y-axis. There is also a scattering plot in statistics which helps in searching for the line in the best fit. The need for this plot is there r find out the error between the actual output and the predicted output. This formula is (y-hat), known as the mean squared error formula. Just like the mean squared error formula, the gradient error descent works similarly, but the convex function exists.

Gradient Descent Machine Learning aims to reduce the cost function or the error found between the actual and the predicted model. For all these functions, we need direction and a learning rate. These terminologies determine the partial derivative calculation of future iterative. Indulging into arriving at local and global minimum. 

Learning Rate :

Learning rate means the size of the steps taken to the model on the minimum level. It is most of the minimum value based on the cost function. The larger step leads to risk shooting minimum error. The lower steps have a high level of precision level. The more the number of iterations, the more it affects, and the more time and computation are required to reach the minimum level. 

The cost function :

The cost function measures the difference error between the actual and predicted level. Gradient Descent Machine Learning improves the ability of the machine learning model by giving feedback to the model. So could adjust the parameter to reduce the model error and find the local and global minimum. The number iteration is always moving in the steepest direction so that it could bring the cost function to zero time when it comes to zero or near zero. The model stops learning. There is a slight difference in the cost function and learning rate in that the cost function takes out the average error, but the learning rate takes out the error of one training.  

 Difference between the Batch Gradient Descent and Stochastic Gradient Descent

• The batch gradient descent figures out the gradient in the full training, and the stochastic gradient descent figures out the gradient in the one training.
• BGD is slow, and the computation cost is expensive, whereas the stochastic gradient descent is fast, and the computation cost is minimum.
• It is not suggested for large training samples, and the stochastic gradient descent could handle large training samples. 
• Nature of the batch gradient descent is deterministic, and the nature of stochastic gradient descent is stochastic. 
• It gives an optimal solution for the ample time for convergence, and the stochastic gradient descent gives the best solution. They are not optimal. 
• In batch gradient descent, there is no need for the random shuffling of points. In stochastic gradient descent, the data sample should be in random order, which is why there is a need for random shuffling after each epoch. 
• The batch gradient descent cannot conveniently escape the shallow local minima, butt the stochastic gradient descent can escape the shallow local minima much more conveniently.
• The convergence rate of batch gradient descent is slow compared to stochastic gradient descent, which has many levels of convergence. 

Conclusion

The article revolves around gradient descent, machine learning, and stochastic gradient descent. As part of gradient descent, stochastic gradient descent works better than typical gradient descent machine learning. However, the working of gradient decent machine learning is not complicated, which depends on a few areas, such as the learning rate and cost function. In conclusion, stochastic gradient descent works efficiently and is much better than batch gradient descent.