“March” ing into Deep Learning from Machine Learning

... into Mathematics from Statistics , Tensor Flow and Keras from Jupyter Notebook, GPU from CPU & spread of technology to non-techies from techies ...of-course, it does not stop here :))

Excitement continues while making way through complex concepts in the journey of deep learning. As soon as there is entry in this space, it’s time to venture into math heavy terrain. Like it takes time to get familiar with the Juptyer notebook, it is way deeper when it comes to using Tensor Flow and Keras. It’s a steep curve at the end of machine learning and very beginning of deep learning. Like it sounds, Deep Learning is quite deep since the dynamics of the data change & other associated attributes transform in totality. With humongous data to be computed, it’s good to get familiar with GPU for faster computing, to speed up and hence achieving the objective of lesser computing time. The space of AI-ML is ever changing and changing too fast. While the concept of GPU gets introduced, there is TPU (Tensor Flow Processing Unit), already ready to be explored, understood and worked with. What is catching attention is that study of artificial intelligence is widely accepted by non-techies equally along with tech professionals, now! The key to sustenance is to catch up with changing times. Studies across show a spurt in number of non-tech professionals taking up this subject, to make a mark and to create their place in times technological evolution. Before facing redundancy in job profiles, it’s smart to be equipped to reserve a lasting place for self, in industries across

It's fast paced , ever changing, innovation-based science, very dynamic , truly insightful and there is much more to experience as we take a sneak peek into machine learning and from there on, a leap into deep learning. Ready to Get Set Go…