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Deep Learning with TensorFlow - Second Edition [[electronic resource] /] / Zaccone, Giancarlo
| Deep Learning with TensorFlow - Second Edition [[electronic resource] /] / Zaccone, Giancarlo |
| Autore | Zaccone Giancarlo |
| Edizione | [2nd edition] |
| Pubbl/distr/stampa | Packt Publishing, , 2018 |
| Descrizione fisica | 1 online resource (484 pages) |
| Soggetto genere / forma | Electronic books. |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910467490703321 |
Zaccone Giancarlo
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| Packt Publishing, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Deep Learning with TensorFlow - Second Edition [[electronic resource] /] / Zaccone, Giancarlo
| Deep Learning with TensorFlow - Second Edition [[electronic resource] /] / Zaccone, Giancarlo |
| Autore | Zaccone Giancarlo |
| Edizione | [2nd edition] |
| Pubbl/distr/stampa | Packt Publishing, , 2018 |
| Descrizione fisica | 1 online resource (484 pages) |
| Disciplina | 006.31 |
| Soggetto topico |
Machine learning
Artificial intelligence Python (Computer program language) |
| ISBN | 1-78883-183-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910794625603321 |
|
Zaccone Giancarlo
|
||
| Packt Publishing, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Deep Learning with TensorFlow - Second Edition / / Zaccone, Giancarlo
| Deep Learning with TensorFlow - Second Edition / / Zaccone, Giancarlo |
| Autore | Zaccone Giancarlo |
| Edizione | [2nd edition] |
| Pubbl/distr/stampa | Packt Publishing, , 2018 |
| Descrizione fisica | 1 online resource (484 pages) |
| Disciplina | 006.31 |
| Soggetto topico |
Machine learning
Artificial intelligence Python (Computer program language) |
| ISBN |
9781788831833
1788831837 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Copyright -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Getting Started with Deep Learning -- A soft introduction to machine learning -- Supervised learning -- Unbalanced data -- Unsupervised learning -- Reinforcement learning -- What is deep learning? -- Artificial neural networks -- The biological neurons -- The artificial neuron -- How does an ANN learn? -- ANNs and the backpropagation algorithm -- Weight optimization -- Stochastic gradient descent -- Neural network architectures -- Deep Neural Networks (DNNs) -- Multilayer perceptron -- Deep Belief Networks (DBNs) -- Convolutional Neural Networks (CNNs) -- AutoEncoders -- Recurrent Neural Networks (RNNs) -- Emergent architectures -- Deep learning frameworks -- Summary -- Chapter 2: A First Look at TensorFlow -- A general overview of TensorFlow -- What's new in TensorFlow v1.6? -- Nvidia GPU support optimized -- Introducing TensorFlow Lite -- Eager execution -- Optimized Accelerated Linear Algebra (XLA) -- Installing and configuring TensorFlow -- TensorFlow computational graph -- TensorFlow code structure -- Eager execution with TensorFlow -- Data model in TensorFlow -- Tensor -- Rank and shape -- Data type -- Variables -- Fetches -- Feeds and placeholders -- Visualizing computations through TensorBoard -- How does TensorBoard work? -- Linear regression and beyond -- Linear regression revisited for a real dataset -- Summary -- Chapter 3: Feed-Forward Neural Networks with TensorFlow -- Feed-forward neural networks (FFNNs) -- Feed-forward and backpropagation -- Weights and biases -- Activation functions -- Using sigmoid -- Using tanh -- Using ReLU -- Using softmax -- Implementing a feed-forward neural network -- Exploring the MNIST dataset -- Softmax classifier -- Implementing a multilayer perceptron (MLP) -- Training an MLP -- Using MLPs.
Dataset description -- Preprocessing -- A TensorFlow implementation of MLP for client-subscription assessment -- Deep Belief Networks (DBNs) -- Restricted Boltzmann Machines (RBMs) -- Construction of a simple DBN -- Unsupervised pre-training -- Supervised fine-tuning -- Implementing a DBN with TensorFlow for client-subscription assessment -- Tuning hyperparameters and advanced FFNNs -- Tuning FFNN hyperparameters -- Number of hidden layers -- Number of neurons per hidden layer -- Weight and biases initialization -- Selecting the most suitable optimizer -- GridSearch and randomized search for hyperparameters tuning -- Regularization -- Dropout optimization -- Summary -- Chapter 4: Convolutional Neural Networks -- Main concepts of CNNs -- CNNs in action -- LeNet5 -- Implementing a LeNet-5 step by step -- AlexNet -- Transfer learning -- Pretrained AlexNet -- Dataset preparation -- Fine-tuning implementation -- VGG -- Artistic style learning with VGG-19 -- Input images -- Content extractor and loss -- Style extractor and loss -- Merger and total loss -- Training -- Inception-v3 -- Exploring Inception with TensorFlow -- Emotion recognition with CNNs -- Testing the model on your own image -- Source code -- Summary -- Chapter 5: Optimizing TensorFlow Autoencoders -- How does an autoencoder work? -- Implementing autoencoders with TensorFlow -- Improving autoencoder robustness -- Implementing a denoising autoencoder -- Implementing a convolutional autoencoder -- Encoder -- Decoder -- Fraud analytics with autoencoders -- Description of the dataset -- Problem description -- Exploratory data analysis -- Training, validation, and testing set preparation -- Normalization -- Autoencoder as an unsupervised feature learning algorithm -- Evaluating the model -- Summary -- Chapter 6: Recurrent Neural Networks -- Working principles of RNNs. Implementing basic RNNs in TensorFlow -- RNN and the long-term dependency problem -- Bi-directional RNNs -- RNN and the gradient vanishing-exploding problem -- LSTM networks -- GRU cell -- Implementing an RNN for spam prediction -- Data description and preprocessing -- Developing a predictive model for time series data -- Description of the dataset -- Pre-processing and exploratory analysis -- LSTM predictive model -- Model evaluation -- An LSTM predictive model for sentiment analysis -- Network design -- LSTM model training -- Visualizing through TensorBoard -- LSTM model evaluation -- Human activity recognition using LSTM model -- Dataset description -- Workflow of the LSTM model for HAR -- Implementing an LSTM model for HAR -- Summary -- Chapter 7: Heterogeneous and Distributed Computing -- GPGPU computing -- The GPGPU history -- The CUDA architecture -- The GPU programming model -- The TensorFlow GPU setup -- Update TensorFlow -- GPU representation -- Using a GPU -- GPU memory management -- Assigning a single GPU on a multi-GPU system -- The source code for GPU with soft placement -- Using multiple GPUs -- Distributed computing -- Model parallelism -- Data parallelism -- The distributed TensorFlow setup -- Summary -- Chapter 8: Advanced TensorFlow Programming -- tf.estimator -- Estimators -- Graph actions -- Parsing resources -- Flower predictions -- TFLearn -- Installation -- Titanic survival predictor -- PrettyTensor -- Chaining layers -- Normal mode -- Sequential mode -- Branch and join -- Digit classifier -- Keras -- Keras programming models -- Sequential model -- Functional API -- Summary -- Chapter 9: Recommendation Systems Using Factorization Machines -- Recommendation systems -- Collaborative filtering approaches -- Content-based filtering approaches -- Hybrid recommender systems -- Model-based collaborative filtering. Movie recommendation using collaborative filtering -- The utility matrix -- Description of the dataset -- Ratings data -- Movies data -- Users data -- Exploratory analysis of the MovieLens dataset -- Implementing a movie RE -- Training the model with the available ratings -- Inferencing the saved model -- Generating the user-item table -- Clustering similar movies -- Movie rating prediction by users -- Finding top k movies -- Predicting top k similar movies -- Computing user-user similarity -- Evaluating the recommender system -- Factorization machines for recommendation systems -- Factorization machines -- Cold-start problem and collaborative-filtering approaches -- Problem definition and formulation -- Dataset description -- Workflow of the implementation -- Preprocessing -- Training the FM model -- Improved factorization machines -- Neural factorization machines -- Dataset description -- Using NFM for the movie recommendation -- Summary -- Chapter 10: Reinforcement Learning -- The RL problem -- OpenAI Gym -- OpenAI environments -- The env class -- Installing and running OpenAI Gym -- The Q-Learning algorithm -- The FrozenLake environment -- Deep Q-learning -- Deep Q neural networks -- The Cart-Pole problem -- Deep Q-Network for the Cart-Pole problem -- The Experience Replay method -- Exploitation and exploration -- The Deep Q-Learning training algorithm -- Summary -- Other Books You May Enjoy -- Leave a review - let other readers know what you think -- Index. |
| Record Nr. | UNINA-9910971559903321 |
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Zaccone Giancarlo
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| Packt Publishing, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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New Land, New Life : A Success Story of New Land Resettlement in Bangladesh
| New Land, New Life : A Success Story of New Land Resettlement in Bangladesh |
| Autore | Jenkins Andrew |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | CABI, 2020 |
| Descrizione fisica | 1 online resource (100 pages) : colour illustrations; digital, PDF file(s) |
| Disciplina | 333.315492 |
| Altri autori (Persone) |
HaiderNatasha
KarimBazlul ChakrabortyMihir Kumar SarkerKiran Sankar KarimRezaul IslamRobiul BegumNujulee MallorieEdward WildeKoen de |
| Soggetto topico |
Deltas, estuaries, coastal regions
Development economics & emerging economies |
| Soggetto non controllato |
resettlement
land development Bangladesh Commonwealth of Nations development projects Asia livelihoods rural development Least Developed Countries Developing Countries South Asia |
| ISBN |
9781789246063
1789246067 9781789246056 1789246059 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Intro -- Front Cover -- Title Page -- Copyright -- Contents -- Figures and Tables -- Contributors -- Glossary -- Acknowledgements -- 1. The Coastal Chars of Bangladesh -- 2. The Birth of CDSP-IV -- 3. Managing Char Development and Settlement: A Complex Process -- 4. Involving the Communities and Civil Society -- 5. Role of Women in Development -- 6. Developing the Infrastructure -- 7. The Land Settlement Process -- 8. The Power of Agriculture -- 9. Money Matters - Savings and Loans -- 10. Environmental Improvement with Trees -- 11. Income and Quality of Life -- 12. Our Pride -- 13. When the Project Leaves -- References -- Back Cover. |
| Record Nr. | UNINA-9910420941403321 |
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Jenkins Andrew
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| CABI, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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