Advantages of deep learning:
Properties are automatically subtracted and adjusted optimally for the desired result. Features do not need to be removed in advance. This avoids time-consuming machine learning techniques.
Dayanıklılık resistance to natural variations in data is automatically learned.
Aynı the same neural network-based approach can be applied to many different applications and data types.
Çok very large parallel calculations can be performed using GPUs and can be scaled for large volumes of data. It also provides better performance results when the amount of data is too large.
Derin deep learning architecture is flexible to adapt to new problems in the future.
Disadvantages of deep learning:
Gerektirir requires a huge amount of data to perform better than other techniques.
Karmaşık it is extremely expensive to train due to complex data models. It also requires deep learning, expensive GPUs and hundreds of machines. This, in turn, increases the cost of users.
There is no standard theory to guide you in choosing the right deep learning tools, as topology requires knowledge of the training method and other parameters. As a result, it is difficult to adopt by less talented people.
Understanding outcomes based solely on learning is not easy and requires classifiers to do so. Convoluted neural network-based algorithms perform such tasks.