000			04374nam a2200241Ia 4500
003			NULRC
005			20250520103029.0
008			250520s9999 xx 000 0 und d
020			_a9781492045526
040			_cNULRC
050			_aQ 325.5 .H69 2020
100			_aHoward, Jeremy _eauthor
245		0	_aDeep learning for coders with fastai and pytorch : _bAI applications without a PhD / _cJeremy Howard and Sylvain Gugger
260			_aSebastopol, California : _bO'Reilly Media, Incorporated, _cc2020
300			_a594 pages : _billustrations ; _c24 cm.
365			_bUSD39
504			_aIncludes bibliographical references and index.
505			_aIntro -- Preface -- Who This Book Is For -- What You Need to Know -- What You Will Learn -- O'Reilly Online Learning -- How to Contact Us -- Foreword -- I. Deep Learning in Practice -- 1. Your Deep Learning Journey -- Deep Learning Is for Everyone -- Neural Networks: A Brief History -- Who We Are -- How to Learn Deep Learning -- Your Projects and Your Mindset -- The Software: PyTorch, fastai, and Jupyter (And Why It Doesn't Matter) -- Your First Model -- Getting a GPU Deep Learning Server -- Running Your First Notebook -- What Is Machine Learning? -- What Is a Neural Network? A Bit of Deep Learning Jargon -- Limitations Inherent to Machine Learning -- How Our Image Recognizer Works -- What Our Image Recognizer Learned -- Image Recognizers Can Tackle Non-Image Tasks -- Jargon Recap -- Deep Learning Is Not Just for Image Classification -- Validation Sets and Test Sets -- Use Judgment in Defining Test Sets -- A Choose Your Own Adventure Moment -- Questionnaire -- Further Research -- 2. From Model to Production -- The Practice of Deep Learning -- Starting Your Project -- The State of Deep Learning -- Computer vision -- Text (natural language processing) Combining text and images -- Tabular data -- Recommendation systems -- Other data types -- The Drivetrain Approach -- Gathering Data -- From Data to DataLoaders -- Data Augmentation -- Training Your Model, and Using It to Clean Your Data -- Turning Your Model into an Online Application -- Using the Model for Inference -- Creating a Notebook App from the Model -- Turning Your Notebook into a Real App -- Deploying Your App -- How to Avoid Disaster -- Unforeseen Consequences and Feedback Loops -- Get Writing! -- Questionnaire -- Further Research -- 3. Data Ethics -- Key Examples for Data Ethics Bugs and Recourse: Buggy Algorithm Used for Healthcare Benefits -- Feedback Loops: YouTube's Recommendation System -- Bias: Professor Latanya Sweeney "Arrested" -- Why Does This Matter? -- Integrating Machine Learning with Product Design -- Topics in Data Ethics -- Recourse and Accountability -- Feedback Loops -- Bias -- Historical bias -- Measurement bias -- Aggregation bias -- Representation bias -- Addressing different types of bias -- Disinformation -- Identifying and Addressing Ethical Issues -- Analyze a Project You Are Working On -- Processes to Implement -- Ethical lenses The Power of Diversity -- Fairness, Accountability, and Transparency -- Role of Policy -- The Effectiveness of Regulation -- Rights and Policy -- Cars: A Historical Precedent -- Conclusion -- Questionnaire -- Further Research -- Deep Learning in Practice: That's a Wrap! -- II. Understanding fastai's Applications -- 4. Under the Hood: Training a Digit Classifier -- Pixels: The Foundations of Computer Vision -- First Try: Pixel Similarity -- NumPy Arrays and PyTorch Tensors -- Computing Metrics Using Broadcasting -- Stochastic Gradient Descent -- Calculating Gradients.
520			_aDeep learning has the reputation as an exclusive domain for math PhDs. Not so. With this book, programmers comfortable with Python will learn how to get started with deep learning right away. Using PyTorch and the fastai deep learning library, you'll learn how to train a model to accomplish a wide range of tasks-including computer vision, natural language processing, tabular data, and generative networks. At the same time, you'll dig progressively into deep learning theory so that by the end of the book you'll have a complete understanding of the math behind the library's functions
650			_aMACHINE LEARNING
700			_aGugger, Sylvain _eco-author
942			_2lcc _cBK
999			_c21785 _d21785