1.3 WHAT IS THE NEED OF A TRANSTITION FROM MACHINE LEARNING TO DEEP LEARNING?

 Machine Learning has been around for a very long time. Machine Learning helped and motivated scientists and researchers to come up with newer algorithms to meet the expectations of technology enthusiasts. The major limitation of Machine Learning lies in the explicit human intervention for the extraction of features in the data that we work (Figure 1.1). Deep Learning allows for automated feature extraction and learning of the model adapting all by itself to the dynamism of data.

Apple => Menual feature extraction => Learning => Machine learning => Apple

Limitation fo Machine Learning.

Apple => Automatic feature extraction and learning => Deep learning => Apple

Advantages of Deep Learning.

Deep Learning very closely tries to imitate the structure and pattern of biological neurons. This single concept, which makes it more complex, still helps to come out with effective predictions. Human intelligence is supposed to be the best of all types of intelligence in the universe. Researchers are still striving to understand the  complexity of how the Human intelligence is supposed to be the best of all types of intelligence in the universe. Researchers are still striving to understand the complexity of how the human brain works. The Deep Learning module acts like a black box, which takes inputs, does the processing in the black box, and gives the desired output. It helps us, with the help of GPUs and TPUs, to work with complex algorithms at a faster pace. The model developed could be reused for similar futuristic applications.