Deep learning refers to a set of techniques by which we can achieve varying degrees of artificial intelligence by mimicking the working of a human brain. Deep learning is a subset of Machine Learning techniques that aim to achieve Artificial Intelligence.
The distinguishing feature of Deep Learning is its use of various Artificial Neural Networks, that imitate the human brain. Just as in the brain, Artificial Neural Networks or ANNs also consist of neurons and synapses between them.
Deep Learning vs Machine Learning:
In traditional Machine Learning, the data must be broken down into individual features. These hand-crafted features are fed into the model and we get a prediction as an output. However, hand-crafting features is a time-consuming process that involves a lot of statistical knowledge and expertise in data science.
With the advent of Deep Learning and multi-layered Artificial Neural Networks, feature selection can now be handled by the model itself. By feeding it numerical representation of raw data (Images, Video, Audio etc), the multi-layered architecture allows for the model to determine the highest-contributing features and uses them to make successful predictions, without any human crafted features. This drastically shortened project timelines and human intervention in the preliminary stages.
A caveat is that DL models required larger volumes of data to train than traditional ML models.
Advantages of studying Deep Learning:
Deep learning is a buzz-word, synonymous with cutting edge Artificial Intelligence. Whether it’s Waymo’s self driving car, OpenAI’s DoTA playing AI or digital smart assistants like Siri or Alexa, the impact that deep learning has had on modern day technology is significant.
Let us discuss some of the advantages studying deep learning.
Data-Driven Everything - Deep Learning can be applied to ANY domain at some capacity, so long as there are volumes of data generated to train the models.
Highly Accessible - Advancements in software, hardware and the open-source community of Deep Learning Practitioners have made DL the most accessible it’s ever been since its inception.
Math and Python - As this point is titled, high-school math and basic knowledge of Python syntax is all you need to begin your journey as a deep learning practitioner.
Deep Learning Career Opportunities:
Projects @ IT/ITeS Companies: For those looking to transition into DL/ML projects at IT companies, familiarity of programming, application and mathematics behind deep learning will be suitable.
DL products @ startups: For those looking to join startups focused on DL products, a working level of proficiency in programming, mathematics and application of DL techniques would be required
Research @ universities, research labs: For those looking to enter the research field, expert understanding of Deep-Learning, its underlying concepts and its practical application are required.
Also read from the website of IBM https://www.ibm.com/blogs/systems/ai-machine-learning-and-deep-learning-whats-the-difference/
The distinguishing feature of Deep Learning is its use of various Artificial Neural Networks, that imitate the human brain. Just as in the brain, Artificial Neural Networks or ANNs also consist of neurons and synapses between them.
Deep Learning vs Machine Learning:
In traditional Machine Learning, the data must be broken down into individual features. These hand-crafted features are fed into the model and we get a prediction as an output. However, hand-crafting features is a time-consuming process that involves a lot of statistical knowledge and expertise in data science.
With the advent of Deep Learning and multi-layered Artificial Neural Networks, feature selection can now be handled by the model itself. By feeding it numerical representation of raw data (Images, Video, Audio etc), the multi-layered architecture allows for the model to determine the highest-contributing features and uses them to make successful predictions, without any human crafted features. This drastically shortened project timelines and human intervention in the preliminary stages.
A caveat is that DL models required larger volumes of data to train than traditional ML models.
Advantages of studying Deep Learning:
Deep learning is a buzz-word, synonymous with cutting edge Artificial Intelligence. Whether it’s Waymo’s self driving car, OpenAI’s DoTA playing AI or digital smart assistants like Siri or Alexa, the impact that deep learning has had on modern day technology is significant.
Let us discuss some of the advantages studying deep learning.
Data-Driven Everything - Deep Learning can be applied to ANY domain at some capacity, so long as there are volumes of data generated to train the models.
Highly Accessible - Advancements in software, hardware and the open-source community of Deep Learning Practitioners have made DL the most accessible it’s ever been since its inception.
Math and Python - As this point is titled, high-school math and basic knowledge of Python syntax is all you need to begin your journey as a deep learning practitioner.
Deep Learning Career Opportunities:
Projects @ IT/ITeS Companies: For those looking to transition into DL/ML projects at IT companies, familiarity of programming, application and mathematics behind deep learning will be suitable.
DL products @ startups: For those looking to join startups focused on DL products, a working level of proficiency in programming, mathematics and application of DL techniques would be required
Research @ universities, research labs: For those looking to enter the research field, expert understanding of Deep-Learning, its underlying concepts and its practical application are required.
Also read from the website of IBM https://www.ibm.com/blogs/systems/ai-machine-learning-and-deep-learning-whats-the-difference/
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