Welcome (Deep Learning Specialization C1W1L01)

Hello, and welcome. As you probably know, deep learning has already transformed traditional internet businesses like web search and advertising. But deep learning is also enabling brand new products and businesses and ways of helping people to recreate it. Everything ranging from better healthcare where deep learning is getting really good at reading X-Ray images to delivering personalized education, to precision agriculture, to even self-driving cars and many others. If you want to learn the tools of deep learning and be able to apply them to build these amazing things, I want to help you get there. When you finish the sequence of courses on Coursera called a Specialization, you will be able to put deep learning onto your resume with confidence. Over the next decade, I think all of us have an opportunity To build an amazing world, amazing society that is AI powered And I hope that you will play a big role in the creation of this AI-powered society. So that it. Let's get started. I think that AI is the new electricity. Starting from about a hundred years ago, the electrification of our society transformed every major industry ranging from transportation, manufacturing to healthcare communications and many more. And I think that today, we see a surprisingly clear power for AI to bring about an equally big transformation and of course the part of AI that is rising rapidly and driving a lot of these developments is deep learning. So, today, deep learning is one of the most highly sought-after skills in the technology world and Through this course and a few courses after this one. I want to help you to gain and master those skills so here's what you'll learn in this sequence of courses also called a Specialization on Coursera. In the first course you'll learn about the foundations of Neural networks to learn about neural networks and deep learning This video that you're watching is part of this first course, which lasts four weeks in total. And each of the five courses in this specialization will be about two to four weeks, where most of them are actually shorter than four weeks. But in this first course you learn how to build a neural network, including a deep neural network and how to train it on data and at the end of this course, you'll be able to build a deep neural network to recognize, guess what? Cats. For some reason there is a cat meme running around in deep learning and so following tradition in this first course, we'll build a cat recognizer Then in the second course you'll learn about the practical aspects of deep learning. So you'll learn now that you've built in your network how to actually get it to perform well to learn about hyper constituting, regularization, how to diagnose lies and variants and advanced optimization algorithms like Momentum RMSprop and the Atom Optimization algorithm Sometimes it seems like there's a lot of tuning even some black Magic and how you build in your network? So the second course which is just three weeks will demystify some of that black magic In a third course which is just two weeks you learn how to structure your machine learning project It turns out that the strategy for building a machine learning system has changed in the area of deep learning So for example the way you fit your data into train Development or depth also called holdout cross validation set and test sets has changed in the era of deep learning So what are the new best practices for doing graph and whatever your training set and your test set come from different distributions That's happening a lot more in the area of deep learning so how do you deal with that and if you've heard of? End-To-end deep learning You also learn more about that in this third course and see when you should use it and maybe when you shouldn't The material in this third course is relatively unique I'm going to share with you a lot of the hot one lessons that I've learned building and shipping Quite a lot of deep learning products this as I know this is actually material that is not taught in most universities that active learning causes But I think I'll really help you to get your deep learning systems to work well in the next course will then talk about convolutional Neural Networks often abbreviated see convolutional net works or Convolutional Neural Networks are often applied to images so you learn how to build these models in Course 4 Finally in Course 5 you learn sequence models and how to apply them to natural language processing and other problems so sequence models includes models like recurrent Neural Networks, abbreviated to RNNs, and Ost-N Models, sensor long Short-term memory models. You learn what these terms mean in Course 5 and be able to apply them to natural language processing problems So you learn these models in Course 5 and be able to apply them to Sequence Data. So for example natural language is just a sequence of words and you also understand how these models can be applied to speech recognition or to music generation and other problems. So through these courses you learn the tools of deep learning. You'll be able to apply them to build amazing things, and I hope many of you through this will also be able to advance your career So that. Let's get started, please go into the next video. We will talk about deep learning applied to supervised learning.