Advantages and Disadvantages of Deep Learning

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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.
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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 whats 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.

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• 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!

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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.

• Its 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 doesnt give us a ton of accurate data. What
  youre 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.

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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
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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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