| Description |
1 online resource (465 pages) |
| Contents |
Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Building Deep Learning Environments; Building a common DL environment; Get focused and into the code!; DL environment setup locally; Downloading and installing Anaconda; Installing DL libraries; Setting up a DL environment in the cloud; Cloud platforms for deployment ; Prerequisites; Setting up the GCP; Automating the setup process; Summary; Chapter 2: Training NN for Prediction Using Regression; Building a regression model for prediction using an MLP deep neural network. |
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Exploring the MNIST datasetIntuition and preparation; Defining regression; Defining the project structure; Let's code the implementation!; Defining hyperparameters; Model definition; Building the training loop; Overfitting and underfitting ; Building inference; Concluding the project; Summary; Chapter 3: Word Representation Using word2vec; Learning word vectors; Loading all the dependencies; Preparing the text corpus; Defining our word2vec model; Training the model; Analyzing the model; Plotting the word cluster using the t-SNE algorithm. |
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Visualizing the embedding space by plotting the model on TensorBoardBuilding language models using CNN and word2vec; Exploring the CNN model; Understanding data format; Integrating word2vec with CNN; Executing the model ; Deploy the model into production; Summary; Chapter 4: Building an NLP Pipeline for Building Chatbots; Basics of NLP pipelines; Tokenization; Part-of-Speech tagging; Extracting nouns; Extracting verbs; Dependency parsing; NER; Building conversational bots; What is TF-IDF?; Preparing the dataset; Implementation; Creating the vectorizer; Processing the query; Rank results. |
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Advanced chatbots using NERInstalling Rasa; Preparing dataset; Training the model; Deploying the model; Serving chatbots; Summary; Chapter 5: Sequence-to-Sequence Models for Building Chatbots; Introducing RNNs; RNN architectures; Implementing basic RNNs; Importing all of the dependencies; Preparing the dataset; Hyperparameters; Defining a basic RNN cell model; Training the RNN Model; Evaluation of the RNN model; LSTM architecture; Implementing an LSTM model; Defining our LSTM model; Training the LSTM model; Evaluation of the LSTM model; Sequence-to-sequence models; Data preparation. |
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Defining a seq2seq modelHyperparameters; Training the seq2seq model; Evaluation of the seq2seq model; Summary; Chapter 6: Generative Language Model for Content Creation; LSTM for text generation; Data pre-processing; Defining the LSTM model for text generation; Training the model; Inference and results; Generating lyrics using deep (multi-layer) LSTM; Data pre-processing; Defining the model; Training the deep TensorFlow-based LSTM model; Inference; Output; Generating music using a multi-layer LSTM; Pre-processing data; Defining the model and training; Generating music; Summary. |
| Note |
Chapter 7: Building Speech Recognition with DeepSpeech2. |
| Summary |
Python Deep Learning Projects book will simplify and ease how deep learning works, and demonstrate how neural networks play a vital role in exploring predictive analytics across different domains. You will explore projects in the field of computational linguistics, computer vision, machine translation, pattern recognition and many more. |
| Bibliography |
Includes bibliographical references. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Python (Computer program language)
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Mathematical theory of computation. |
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Machine learning. |
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Neural networks & fuzzy systems. |
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Artificial intelligence. |
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Computers -- Machine Theory. |
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Computers -- Neural Networks. |
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Computers -- Intelligence (AI) & Semantics. |
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Python (Computer program language) |
| Added Author |
Rahul Kumar.
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Nagaraja, Abhishek.
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| Other Form: |
Print version: Lamons, Matthew. Python Deep Learning Projects : 9 Projects Demystifying Neural Network and Deep Learning Models for Building Intelligent Systems. Birmingham : Packt Publishing Ltd, ©2018 9781788997096 |
| ISBN |
9781789134759 (electronic bk.) |
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1789134757 (electronic bk.) |
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9781788997096 print |
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