书籍Deep Learning for Computer Vision with Python源码

qinzhenpku 2018-03-15 09:49:18

请问谁有Deep Learning for Computer Vision with Python这本书的源码？作者是Adrian Rosebrock。谢谢

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月光吉他 2020-04-29

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https://github.com/dloperab/PyImageSearch-CV-DL-CrashCourse 给你！我最近也开始学习好想找pdf的但是原版的好贵啊，我看reddit上别人写的review好像说不值得那么多钱买

刘大望 2019-08-11

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Deep Learning For Computer Vision With Python概述 https://mp.csdn.net/postedit/98944654

parsleysage 2019-01-30

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Github上有

欢乐的小猪 2018-05-25

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没看过。我想说一般书里会写在哪里下载源码的

20083959 2018-05-24

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请联系QQ 2862464839

Dr. Adrian Rosebrock Deep Learning for Computer Vision with Python

Welcome to the Practitioner Bundle of Deep Learning for Computer Vision with Python! This volume is meant to be the next logical step in your deep learning for computer vision education after completing the Starter Bundle. At this point, you should have a strong understanding of the fundamentals of parameterized learning, neural net works, and Convolutional Neural Networks (CNNs). You should also feel relatively comfortable using the Keras library and the Python programming language to train your own custom deep learning networks. The purpose of the Practitioner Bundle is to build on your knowledge gained from the Starter Bundle and introduce more advanced algorithms, concepts, and tricks of the trade — these tech- niques will be covered in three distinct parts of the book. The first part will focus on methods that are used to boost your classification accuracy in one way or another. One way to increase your classification accuracy is to apply transfer learning methods such as fine-tuning or treating your network as a feature extractor. We’ll also explore ensemble methods (i.e., training multiple networks and combining the results) and how these methods can give you a nice classification boost with little extra effort. Regularization methods such as data augmentation are used to generate additional training data – in nearly all situations, data augmentation improves your model’s ability to generalize. More advanced optimization algorithms such as Adam [1], RMSprop [2], and others can also be used on some datasets to help you obtain lower loss. After we review these techniques, we’ll look at the optimal pathway to apply these methods to ensure you obtain the maximum amount of benefit with the least amount of effort.

Lecture theme: AI-for-Science: The next wave of artificial intelligenceSession 1:Overview：AI-for-Science: The next wave of artificialAI BoomingAI: From Mimicking Human to Discovering the WorldScience: Discover and Change the WorldFour Paradigms of ScienceClosing the Loop of Scientific DiscoverySession 2:Solving Scientific EquationsGraphormer for Molecular ModelingMolecular SimulationAI Models for Molecular SimulationGraphormerGraphormer: ResultsMD Simulation for SARS-COV-2The “Wedge” Effect of NTDDeepVortexNet for Fluid ModelingMeteorological SimulationPhysics-informed Neural NetworkFrom Derivative-based to Monte-CarloDeep Vortex NetworkSession 3:Mining Experimental ObservationsLorentzNet for Particle DetectionParticle DetectionJet Analysis and Group EquivarianceProblems with Existing AI ModelsLorentzNetExperimental ResultsSPT-Scientific Language ModelSPT: Foundation Model for ScienceEffective Training: A System WorkEffective Inference: Double PromptsExperimental ResultsSession 4:Discovering New ScienceAI for New Physics DetectionForce Field DecomposingLearning Non-conservative DynamicsExperimental ResultsTarget ApplicationsMore of Our Recent ResearchMicrosoft Research AI4Science

经典书籍：Deep_Learning_for_Computer_Vision_with_Python的section1-5的源码。

Chapter 1, Theano Basics, helps the reader to reader learn main concepts of Theano to write code that can compile on different hardware architectures and optimize automatically complex mathematical objective functions. Chapter 2, Classifying Handwritten Digits with a Feedforward Network, will introduce a simple, well-known and historical example which has been the starting proof of superiority of deep learning algorithms. The initial problem was to recognize handwritten digits. Chapter 3, Encoding word into Vector, one of the main challenge with neural nets is to connect the real world data to the input of a neural net, in particular for categorical and discrete data. This chapter presents an example on how to build an embedding space through training with Theano. Such embeddings are very useful in machine translation, robotics, image captioning, and so on because they translate the real world data into arrays of vectors that can be processed by neural nets. Chapter 4, Generating Text with a Recurrent Neural Net, introduces recurrency in neural nets with a simple example in practice, to generate text. Recurrent neural nets (RNN) are a popular topic in deep learning, enabling more possibilities for sequence prediction, sequence generation, machine translation, connected objects. Natural Language Processing (NLP) is a second field of interest that has driven the research for new machine learning techniques. Chapter 5, Analyzing Sentiments with a Bidirectional LSTM, applies embeddings and recurrent layers to a new task of natural language processing, sentiment analysis. It acts as a kind of validation of prior chapters. In the meantime, it demonstrates an alternative way to build neural nets on Theano, with a higher level library, Keras. Chapter 6, Locating with Spatial Transformer Networks, applies recurrency to image, to read multiple digits on a page at once. This time, we take the opportunity to rewrite the classification network for handwritten digits images, and our recurrent models, with the help of Lasagne, a library of built-in modules for deep learning with Theano. Lasagne library helps design neural networks for experimenting faster. With this help, we'll address object localization, a common computer vision challenge, with Spatial Transformer modules to improve our classification scores. Chapter 7, Classifying Images with Residual Networks, classifies any type of images at the best accuracy. In the mean time, to build more complex nets with ease, we introduce a library based on Theano framework, Lasagne, with many already implemented components to help implement neural nets faster for Theano. Chapter 8, Translating and Explaining through Encoding – decoding Networks, presents encoding-decoding techniques: applied to text, these techniques are heavily used in machine-translation and simple chatbots systems. Applied to images, they serve scene segmentations and object localization. Last, image captioning is a mixed, encoding images and decoding to texts. This chapter goes one step further with a very popular high level library, Keras, that simplifies even more the development of neural nets with Theano. Chapter 9, Selecting Relevant Inputs or Memories with the Mechanism of Attention, for solving more complicated tasks, the machine learning world has been looking for higher level of intelligence, inspired by nature: reasoning, attention and memory. In this chapter, the reader will discover the memory networks on the main purpose of artificial intelligence for natural language processing (NLP): the language understanding. Chapter 10, Predicting Times Sequence with Advanced RNN, time sequences are an important field where machine learning has been used heavily. This chapter will go for advanced techniques with Recurrent Neural Networks (RNN), to get state-of-art results. Chapter 11, Learning from the Environment with Reinforcement, reinforcement learning is the vast area of machine learning, which consists in training an agent to behave in an environment (such as a video game) so as to optimize a quantity (maximizing the game score), by performing certain actions in the environment (pressing buttons on the controller) and observing what happens. Reinforcement learning new paradigm opens a complete new path for designing algorithms and interactions between computers and real world. Chapter 12, Learning Features with Unsupervised Generative Networks, unsupervised learning consists in new training algorithms that do not require the data to be labeled to be trained. These algorithms try to infer the hidden labels from the data, called the factors, and, for some of them, to generate new synthetic data. Unsupervised training is very useful in many cases, either when no labeling exists, or when labeling the data with humans is too expensive, or lastly when the dataset is too small and feature engineering would overfit the data. In this last case, extra amounts of unlabeled data train better features as a basis for supervised learning. Chapter 13, Extending Deep Learning with Theano, extends the set of possibilities in Deep Learning with Theano. It addresses the way to create new operators for the computation graph, either in Python for simplicity, or in C to overcome the Python overhead, either for the CPU or for the GPU. Also, introduces the basic concept of parallel programming for GPU. Lastly, we open the field of General Intelligence, based on the first skills developped in this book, to develop new skills, in a gradual way, to improve itself one step further.

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