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What are the advantages and disadvantages of Deep Learning?

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Advantages and disadvantages of Deep Learning

1Highly Effective in Complex Tasks involving large datasets and complex patterns, such as image and speech recognition, natural language processing, and game playing May require large amounts of data for training, which can be both costly and time-consuming to obtain
Deep Learning models can process both structured and unstructured data effectively
structured data - tabular data
unstructured data - images, text, audio
Computationally intensive and often requires specialized hardware, like GPUs or TPUs
Automatically learn and extract relevant features from raw data, reducing the need for manual feature engineering or domain expertiseProne to overfitting, where they perform well on training data but generalize poorly on new, unseen data
4 State-of-the-Art Performance in several domains surpassing human-level performance, and setting new benchmarks Hyper-parameter tuning process is tedious: Finding the right hyperparameters for deep networks can be challenging and may require extensive experimentation
5 Wide range of applications across industries , including marketing, finance, customer service, virtual assistants, chatbots etc.
Black box in nature as it is almost impossible to understand what is happening in hidden layers within deep networks

Video Explanation

  • This video titled “Artificial Intelligence and algorithms: pros and cons” by DW Documentary is an excellent representation of the use of Artificial Intelligence powered by Deep Learning in our daily lives and explores the various pros and cons associated with such systems
Artificial Intelligence and algorithms: pros and cons by DW Documentary

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