fastbook/01-intro.ipynb

{"metadata":{"jupytext":{"split_at_heading":true},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"#hide\n! [ -e /kaggle ] && pip install -Uqq fastbook\nimport fastbook\nfastbook.setup_book()","metadata":{"execution":{"iopub.status.busy":"2023-10-26T04:50:24.557391Z","iopub.execute_input":"2023-10-26T04:50:24.557812Z","iopub.status.idle":"2023-10-26T04:50:39.548543Z","shell.execute_reply.started":"2023-10-26T04:50:24.557781Z","shell.execute_reply":"2023-10-26T04:50:39.546832Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"#hide\nfrom fastbook import *","metadata":{"execution":{"iopub.status.busy":"2023-10-26T04:50:46.920562Z","iopub.execute_input":"2023-10-26T04:50:46.920970Z","iopub.status.idle":"2023-10-26T04:50:46.926542Z","shell.execute_reply.started":"2023-10-26T04:50:46.920942Z","shell.execute_reply":"2023-10-26T04:50:46.924817Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"raw","source":"[[chapter_intro]]","metadata":{}},{"cell_type":"markdown","source":"# Your Deep Learning Journey","metadata":{}},{"cell_type":"markdown","source":"Hello, and thank you for letting us join you on your deep learning journey, however far along that you may be! In this chapter, we will tell you a little bit more about what to expect in this book, introduce the key concepts behind deep learning, and train our first models on different tasks. It doesn't matter if you don't come from a technical or a mathematical background (though it's okay if you do too!); we wrote this book to make deep learning accessible to as many people as possible.","metadata":{}},{"cell_type":"markdown","source":"## Deep Learning Is for Everyone","metadata":{}},{"cell_type":"markdown","source":"A lot of people assume that you need all kinds of hard-to-find stuff to get great results with deep learning, but as you'll see in this book, those people are wrong. <<myths>> is a list of a few thing you *absolutely don't need* to do world-class deep learning.\n\n```asciidoc\n[[myths]]\n.What you don't need to do deep learning\n[options=\"header\"]\n|======\n| Myth (don't need) | Truth\n| Lots of math | Just high school math is sufficient\n| Lots of data | We've seen record-breaking results with <50 items of data\n| Lots of expensive computers | You can get what you need for state of the art work for free\n|======\n```\n\nDeep learning is a computer technique to extract and transform data–-with use cases ranging from human speech recognition to animal imagery classification–-by using multiple layers of neural networks. Each of these layers takes its inputs from previous layers and progressively refines them. The layers are trained by algorithms that minimize their errors and improve their accuracy. In this way, the network learns to perform a specified task. We will discuss training algorithms in detail in the next section.","metadata":{}},{"cell_type":"markdown","source":"Deep learning has power, flexibility, and simplicity. That's why we believe it should be applied across many disciplines. These include the social and physical sciences, the arts, medicine, finance, scientific research, and many more. To give a personal example, despite having no background in medicine, Jeremy started Enlitic, a company that uses deep learning algorithms to diagnose illness and disease. Within months of starting the company, it was announced that its algorithm could identify malignant tumors [more accurately than radiologists](https://www.nytimes.com/2016/02/29/technology/the-promise-of-artificial-intelligence-unfolds-in-small-steps.html).\n\nHere's a list of some of the thousands of tasks in different areas at which deep learning, or methods heavily using deep learning, is now the best in the world:\n\n- Natural langua