Challenges of deep learning for big data
The potential of big data is certainly noteworthy. However, to fully extract valuable information at this scale, we would require new innovations and promising algorithms to address many of these related technical problems. For example, to train the models, most of the traditional machine learning algorithms load the data in memory. But with a massive amount of data, this approach will surely not be feasible, as the system might run out of memory. To overcome all these gritty problems, and get the most out of the big data with the deep learning techniques, we will require brain storming.
Although, as discussed in the earlier section, large-scale deep learning has achieved many accomplishments in the past decade, this field is still in a growing phase. Big data is constantly raising limitations with its 4Vs. Therefore, to tackle all of those, many more advancements in the models need to take place.
