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Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.

Data mining. --- Natural language processing (Computer science) --- Machine learning. --- Python (Computer program language) --- Artificial intelligence. --- Neural networks (Computer science) --- Data mining --- Machine learning --- Artificial intelligence --- Scripting languages (Computer science) --- NLP (Computer science) --- Electronic data processing --- Human-computer interaction --- Semantic computing --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Learning, Machine --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Exploration de données --- Traitement du langage naturel --- Apprentissage automatique --- deep learning --- data mining --- artificiële intelligentie (AI) --- fastai --- PyTorch --- Exploration de données --- Apprentissage automatique. --- Artificial Intelligence. --- Data Mining. --- Exploration de données (Informatique). --- Intelligence artificielle. --- Natural Language Processing. --- Natural language processing (Computer science). --- Neural networks (Computer science). --- Python (Computer program language). --- Python (Langage de programmation). --- Traitement automatique des langues naturelles. --- artificial intelligence.

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Deep Learning with PyTorch teaches you to create neural networks and deep learning systems with PyTorch. This practical book quickly gets you to work building a real-world example from scratch: a tumor image classifier. Along the way, it covers best practices for the entire DL pipeline, including the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. After covering the basics, the book will take you on a journey through larger projects. The centerpiece of the book is a neural network designed for cancer detection. You'll discover ways for training networks with limited inputs and start processing data to get some results. You'll sift through the unreliable initial results and focus on how to diagnose and fix the problems in your neural network. Finally, you'll look at ways to improve your results by training with augmented data, make improvements to the model architecture, and perform other fine tuning.

Neural networks (Computer science) --- Machine learning. --- Artificial intelligence. --- Python (Computer program language)