Advantages and Disadvantages of Deep Learning

1. Advantages and Disadvantages of Deep Learning Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Deep learning eliminates some of data pre-processing that is typically involved with machine learning. These algorithms can ingest and process unstructured data, like text and images, and it automates feature extraction, removing some of the dependency on human experts.

2. Advantages of Deep Learning • It robust enough to understand and use novel data, but most data scientists have learned to control the learning to focus on what’s important to them. Deep learning takes advantage of this by allowing you to control the learning, but not the statistical modeling. • It allows us to teach a specific task rather than teaching the system how to learn. We can use different examples to train a particular model or we can use a very simple training set and simply ask it to learn. • It can go and get a new image from its own memory. • It can adapt automatically to all data, but it makes for a nice alternative to traditional machine learning that relies on human expertise • It handles everything at a much higher level of abstraction than your standard neural network, so the deep learning training process is, at its core, much less complex. • It allows us to retain a lot of information, even on the basis of a very tiny or badly known object. And we are in the process of learning these ways of achieving efficiency for the vision.

3. Continue • It is not affected by computation power. Hence, it can gain insights much more quickly and thus, it can tackle problems that are traditionally tricky to solve. • It has a high dimensionality. This means that we can create more learning models by adding more layers to our neural network. • It allows us to study the world as a non-supervised structure. If you look at neurons, they have such varied functions and shapes. • It can see more than one and can learn with more information. • It gets its results more quickly. It learns over time rather than just in a flash. • It can learn over time, over billions of examples of images, and, crucially, recognize patterns. • It can handle large amounts of data for small networks with a much lower learning cost. • Also Read: Difference Between Machine Learning And Deep Learning That You Must Know!

4. Disadvantages of Deep Learning • It is much harder to compare what it achieves to that of hand-crafted methods. There is an alternative approach, called “deep learning by gradient descent”, which can be considered as an extension of deep learning to higher-dimensional regions. • It is very difficult to assess its performance in real world applications; applications can vary greatly from application to application, and testing techniques for analysis, validation and scaling vary widely. • It’s not 100% efficient and it will have some difficult problems. • It can be trained on very large amounts of data (think thousands of images or videos). • It doesn’t give us a ton of accurate data. What you’re getting are approximate statistics. • It requires huge data sets in order to train. They can be huge, especially when you consider that we only know the image and not the context.

5. Continue It is computationally very expensive, requiring a large amount of memory and computational resources, and it is not easy to transfer it to other problems. It requires to train the model to learn about deep structures, a process which requires billions of hours of computation in a highly parallel computer architecture. It is hard to describe, and is not completely understood. It is a little bit complicated. I do believe the second generation methods are simpler and give a better result. It tends to be more costly. It requires much larger datasets with many more features. As a result, it takes longer to train the algorithm and it takes more memory for it to work with the data. It requires very advanced optimization techniques, and these should have been incorporated to obtain good results. Also Read: Kick Start Your Career With Machine Learning

6. Conclusion It is computationally very expensive, requiring a large amount of memory and computational resources, and it is not easy to transfer it to other problems. It requires to train the model to learn about deep structures, a process which requires billions of hours of computation in a highly parallel computer architecture. It is hard to describe, and is not completely understood. It is a little bit complicated. I do believe the second generation methods are simpler and give a better result. It tends to be more costly. It requires much larger datasets with many more features. As a result, it takes longer to train the algorithm and it takes more memory for it to work with the data. It requires very advanced optimization techniques, and these should have been incorporated to obtain good results.

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