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[null,"1.","Record Nr.",null,"UNINA9910289347403321"]
[null,null,"Autore",null,"Mendes Pedro Rosa <1968->"]
[null,null,"Titolo",null,"Business and security sector reform : the case for corporate security responsibility / / Pedro Rosa Mendes"]
[null,null,"Pubbl/distr/stampa",null,"London : , : Ubiquity Press, , 2018"]
[null,null,"ISBN",null,"1-911529-40-4"]
[null,null,"Descrizione fisica",null,"1 online resource (49)"]
[null,null,"Collana",null,"SSR paper ; ; 13, , 2571-9297"]
[null,null,"Disciplina",null,"343.034"]
[null,null,"Soggetti",null,"Human rights - Economic aspects"]
[{"$nulls":4},"Social responsibility of business"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","Challenges to security and human rights involving extractive and other industries are addressed in a framework known as business and human rights (BHR), which shares many challenges and goals with SSR. This paper describes the grounds where BHR and SSR coincide in principles, actors and activities and which synergies can be built on that base. Opportunities for bridging BHR and SSR are drawn from a systematic comparison of case studies on Guinea, Colombia and Papua New Guinea. BHR and SSR should ideally cohere instead of collide."]
[null,"2.","Record Nr.",null,"UNINA9910300755003321"]
[null,null,"Autore",null,"Michelucci Umberto"]
[null,null,"Titolo",null,"Applied Deep Learning : A Case-Based Approach to Understanding Deep Neural Networks / / by Umberto Michelucci"]
[null,null,"Pubbl/distr/stampa",null,"Berkeley, CA : , : Apress : , : Imprint : Apress, , 2018"]
[null,null,"ISBN",null,"9781484237908"]
[{"$nulls":4},"1484237900"]
[null,null,"Edizione",null,"[1st ed. 2018.]"]
[null,null,"Descrizione fisica",null,"1 online resource (425 pages)"]
[null,null,"Disciplina",null,"006.31"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Python (Computer program language)"]
[{"$nulls":4},"Open source software"]
[{"$nulls":4},"Computer programming"]
[{"$nulls":4},"Big data"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Python"]
[{"$nulls":4},"Open Source"]
[{"$nulls":4},"Big Data"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Chapter 1: Introduction -- Chapter 2: Single Neurons -- Chapter 3: Fully connected Neural Network with more neurons -- Chapter 4: Neural networks error analysis -- Chapter 5: Dropout technique -- Chapter 6: Hyper parameters tuning -- Chapter 7: Tensorflow and optimizers (Gradient descent, Adam, momentum, etc.) -- Chapter 8: Convolutional Networks and image recognition -- Chapter 9: Recurrent Neural Networks -- Chapter 10: A practical COMPLETE example from scratch (put everything together) -- Chapter 11: Logistic regression implement from scratch in Python without libraries. ."]
[null,null,null,"Sommario/riassunto","Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. The next section talks about more "]
[{"$nulls":4},"complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions. Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy). You will: Implement advanced techniques in the right way in Python and TensorFlow Debug and optimize advanced methods (such as dropout and regularization) Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on) Set up a machine learning project focused on deep learning on a complex dataset."]