Self-supervised Learning is the solution for the limitations of Deep learning like requirement of large amount of data for huge computation process.

Neural Networks and Deep Learning are currently the two hot buzzwords that are being used nowadays with Artificial Intelligence. The recent developments in the World of Artificial intelligence can be attributed to these two as they have played a significant role in improving the intelligence of AI. Look around, and you will find more and more intelligent machines around. Thanks to Neural Networks and Deep Learning, jobs and capabilities that were once considered the forte of humans are now being performed by machines. Today, Machines are no longer made to eat more complex algorithms, but instead, they are fed to develop into an autonomous, self-teaching systems capable of revolutionizing many industries all around.

Deep learning has gained massive popularity in scientific computing, and deep learning models are widely used by industries that solve complex problems. Deep learning uses artificial neural networks to perform sophisticated computations on large amounts of data. It is a type of machine learning that works based on the structure and function of the human brain. Deep learning has offered noteworthy capabilities and advances in voice recognition, image comprehension, self-driving car, natural language procession, search engine optimization, and more. Understanding AI has become one of the most demanded skills across the industry.

If you've been following developments over the last few years, you may have noticed that deep learning and neural networks have grown wildly. Neural network architecture is able to make predictive judgments in in sports, medicine and the financial sector.

In the past year I have also worked with Deep Learning techniques, and I would like to share with you how to make and train a Convolutional Neural Network from scratch, using tensorflow. Later on we can use this knowledge as a building block to make interesting Deep Learning applications. The pictures here are from the full article. Source code is also provided. Before you continue, make sure you understand how a convolutional neural network works.