What was never thought to be possible or perhaps deemed unthinkable is now manifesting itself in the form of Artificial Intelligence (AI). Artificial intelligence is now transforming the world in many ways, especially in the highly industrialized world. The most interesting part of artificial intelligence is the technology behind it. It's even more interesting on how this intelligence is transforming and improving our lives. If you watched the big-time movie, Terminator a few years back, you'd get the following idea.

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Off late, artificial intelligence (AI) has become the buzzword for almost all industry sectors; this is no different for the healthcare arena. However, AI is a massive infrastructure undertaking, and most organizations get lost in the process of injecting this technology into their IT environment. As many organizations continue to look at big data analytics to improve healthcare, the scope of machine learning and deep learning solutions in healthcare also increases simultaneously. Machine learning and deep learning are no newbies for the healthcare industry; however, their progress is. This makes AI hard to be deployed and efficiently managed.

Do neural networks represent a shift in coding? Tesla's director of AI Andrej Karpathy in his note on Software 2.0 has coined this new term which emphasises a shift in developing and writing software. His take on the question is that training neural nets and predicting using them involves a new way of thinking of software. The earlier methodology, according to Karpathy, involved writing code, say, in Python and putting it in production. However, when it comes to training neural nets -- the developer has to set up a framework for the neural net to properly "learn" and repeatedly feed examples through it of what the correct answer is to "teach" it.

The course is an introduction to the basics of deep learning methods. We will start with object detection and tracking, in which we will track faces, objects and eyes. We will then build a neural network and an OCR. We will then learn how to build learning agents that can learn from interacting with the environment. We will use Deep Learning with Convolutional Neural Networks, and use TensorFlow to build neural networks.

MIT 6.S191 is more than just another lecture series on deep learning. In designing the course, we wanted to do something more. We wanted to equip our audience with the practical skills necessary to go out and implement their own deep learning models, to apply what they got out of this course to the questions that excite and inspire them. And so, we turned to TensorFlow. We designed two TensorFlow based software labs, focusing on music generation with recurrent neural networks and pneumothorax detection in medical images, to complement the course lectures.

In this post, we're going to lay some groundwork for the custom model which will be covered in the next post by familiarizing ourselves with using RNN models in Tensorflow to deal with the sequential MNIST problem. I've put the source code for this post in a notebook hosted on Google Colaboratory, which kindly provides a free GPU runtime for the public to use （I kept getting disconnected to the runtime when running the notebook. So some of the model training was not completed. You can copy the notebook and run it yourself.）: The notebook should have done most of the talking.

Get your team access to Udemy's top 2,500 courses anytime, anywhere. Deep Learning is the most exciting, highly sought and one of the fastest-growing field nowadays. If you want to pursue a career in Artificial Intelligence, Deep Learning will help you do so. Actually Deep Learning is a subfield of Machine learning concerned with algorithms inspired by artificial neural networks i.e the structure and function of the brain. DL is a key enabler of AI powered technologies being developed across the globe.

We fell for Recurrent neural networks (RNN), Long-short term memory (LSTM), and all their variants. Now it is time to drop them! It is the year 2014 and LSTM and RNN make a great come-back from the dead. But we were all young and unexperienced. For a few years this was the way to solve sequence learning, sequence translation (seq2seq), which also resulted in amazing results in speech to text comprehension and the raise of Siri, Cortana, Google voice assistant, Alexa.

My course does exactly what the title describes in a simple, relatable way. I help you to grasp the complete start to end concepts of fundamental deep learning. On your own it can be quite confusing, difficult and frustrating. I've been through the process myself, and with the help of lifelong ... I want to share this with my fellow beginners, developers, AI aspirers, with you. I will give you straightforward examples, instructions, advice, insights and resources for you to take simple steps to create your own neural networks from scratch.