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Applied generative AI for beginners : practical knowledge on diffusion models, ChatGPT, and other LLMs / / by Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, Dilip Gudivada
| Applied generative AI for beginners : practical knowledge on diffusion models, ChatGPT, and other LLMs / / by Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, Dilip Gudivada |
| Autore | Kulkarni Akshay |
| Pubbl/distr/stampa | [Berkeley, California] : , : Apress : , : Imprint : Apress, , 2023 |
| Descrizione fisica | 1 online resource (xvi, 212 pages) |
| Disciplina | 006.31 |
| Altri autori (Persone) |
ShivanandaAdarsha
KulkarniAnoosh GudivadaDilip |
| Soggetto topico | Artificial intelligence |
| ISBN | 1-4842-9994-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Introduction to Generative AI -- Chapter 2: The Evolution of Neural Networks to Large Language Models -- Chapter 3: LLMs and Transformers -- Chapter 4: The ChatGPT Architecture: An In-Depth Exploration of OpenAI's Conversational Language Model -- Chapter 5: Google Bard and Beyond. - Chapter 6: Implement LLM’ using Sklearn -- Chapter 7: LLMs for Enterprise and LLMOps 8: Diffusion Model & Generative AI for Images. - Chapter 9: ChatGTP Use Cases. |
| Record Nr. | UNINA-9910767561003321 |
Kulkarni Akshay
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| [Berkeley, California] : , : Apress : , : Imprint : Apress, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Applied Recommender Systems with Python : Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques / / by Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V Adithya Krishnan
| Applied Recommender Systems with Python : Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques / / by Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V Adithya Krishnan |
| Autore | Kulkarni Akshay |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023 |
| Descrizione fisica | 1 online resource (257 pages) |
| Disciplina | 006.3 |
| Soggetto topico |
Recommender systems (Information filtering)
Machine learning Neural networks (Computer science) Python (Computer program language) Artificial intelligence |
| ISBN | 1-4842-8954-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Introduction to Recommender Systems -- Chapter 2: Association Rule Mining -- Chapter 3: Content and Knowledge-Based Recommender System -- Chapter 4: Collaborative Filtering using KNN -- Chapter 5: Collaborative Filtering Using Matrix Factorization, SVD and ALS -- Chapter 6: Hybrid Recommender System -- Chapter 7: Clustering Algorithm-Based Recommender System -- Chapter 8: Classification Algorithm-Based Recommender System -- Chapter 9: Deep Learning and NLP Based Recommender System -- Chapter 10: Graph-Based Recommender System. - Chapter 11: Emerging Areas and Techniques in Recommender System. |
| Record Nr. | UNINA-9910739476103321 |
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Kulkarni Akshay
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| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Computer vision projects with PyTorch : design and develop production-grade models / / Akshay Kulkarni, Adarsha Shivananda, Nitin Ranjan Sharma
| Computer vision projects with PyTorch : design and develop production-grade models / / Akshay Kulkarni, Adarsha Shivananda, Nitin Ranjan Sharma |
| Autore | Kulkarni Akshay |
| Pubbl/distr/stampa | New York, New York : , : Apress, , [2022] |
| Descrizione fisica | 1 online resource (355 pages) |
| Disciplina | 006.37 |
| Soggetto topico |
Computer vision
Pattern recognition systems Machine learning |
| ISBN | 1-4842-8273-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Table of Contents -- About the Authors -- About the Technical Reviewer -- Introduction -- Chapter 1: The Building Blocks of Computer Vision -- What Is Computer Vision -- Applications -- Classification -- Object Detection and Localization -- Image Segmentation -- Anomaly Detection -- Video Analysis -- Channels -- Convolutional Neural Networks -- Receptive Field -- Local Receptive Field -- Global Receptive Field -- Pooling -- Max Pooling -- Average Pooling -- Global Average Pooling -- Calculation: Feature Map and Receptive Fields -- Kernel -- Stride -- Pooling -- Padding -- Input and Output -- Calculation of Receptive Field -- Understanding the CNN Architecture Type -- Understanding Types of Architecture -- AlexNet -- VGG -- ResNet -- Inception Architectures -- Working with Deep Learning Model Techniques -- Batch Normalization -- Dropouts -- Data Augmentation Techniques -- Introduction to PyTorch -- Installation -- Basic Start -- Summary -- Chapter 2: Image Classification -- Topics to Cover -- Defining the Problem -- Overview of the Approach -- Creating an Image Classification Pipeline -- First Basic Model -- Data -- Data Exploration -- Data Loader -- Define the Model -- The Training Process -- The Second Variation of Model -- The Third Variation of the Model -- The Fourth Variation of the Model -- Summary -- Chapter 3: Building an Object Detection Model -- Object Detection Using Boosted Cascade -- R-CNN -- The Region Proposal Network -- Fast Region-Based Convolutional Neural Network -- How the Region Proposal Network Works -- The Anchor Generation Layer -- The Region Proposal Layer -- Mask R-CNN -- Prerequisites -- YOLO -- YOLO V2/V3 -- Project Code Snippets -- Step 1: Getting Annotated Data -- Step 2: Fixing the Configuration File and Training -- The Model File -- Summary -- Chapter 4: Building an Image Segmentation Model.
Image Segmentation -- Pretrained Support from PyTorch -- Semantic Segmentation -- Instance Segmentation -- Fine-Tuning the Model -- Summary -- Chapter 5: Image-Based Search and Recommendation System -- Problem Statement -- Approach and Methodology -- Implementation -- The Dataset -- Installing and Importing Libraries -- Importing and Understanding the Data -- Feature Engineering -- ResNet18 -- Calculating Similarity and Ranking -- Visualizing the Recommendations -- Taking Image Input from Users and Recommending Similar Products -- Summary -- Chapter 6: Pose Estimation -- Top-Down Approach -- Bottom-Up Approach -- OpenPose -- Branch-1 -- Branch-2 -- HRNet (High-Resolution Net) -- Higher HRNet -- PoseNet -- How Does PoseNet Work? -- Single Person Pose Estimation -- Multi-Person Pose Estimation -- Pros and Cons of PoseNet -- Applications of Pose Estimation -- Test Cases Performed Retail Store Videos -- Implementation -- Step 1: Identify the List of Human Keypoints to Track -- Step 2: Identify the Possible Connections Between the Keypoints -- Step 3: Load the Pretrained Model from the PyTorch Library -- Step 4: Input Image Preprocessing and Modeling -- Step 5: Build Custom Functions to Plot the Output -- Step 6: Plot the Output on the Input Image -- Summary -- Chapter 7: Image Anomaly Detection -- Anomaly Detection -- Approach 1: Using a Pretrained Classification Model -- Step 1: Import the Required Libraries -- Step 2: Create the Seed and Deterministic Functions -- Step 3: Set the Hyperparameter -- Step 4: Import the Dataset -- Step 5: Image Preprocessing Stage -- Step 6: Load the Pretrained Model -- Step 7: Freeze the Model -- Step 8: Train the Model -- Step 9: Evaluate the Model -- Approach 2: Using Autoencoder -- Step 1: Prepare the Dataset Object -- Step 2: Build the Autoencoder Network -- Step 3: Train the Autoencoder Network. Step 4: Calculate the Reconstruction Loss Based on the Original Data -- Step 5: Select the Most Anomalous Digit Based on the Error Metric Score -- Output -- Summary -- Chapter 8: Image Super-Resolution -- Up-Scaling Using the Nearest Neighbor Concept -- Understanding Bilinear Up-Scaling -- Variational Autoencoders -- Generative Adversarial Networks -- The Model Code -- Model Development -- Imports -- Running the Application -- Summary -- Chapter 9: Video Analytics -- Problem Statement -- Approach -- Implementation -- Data -- Uploading the Required Videos to Google Colab -- Convert the Video to a Series of Images -- Image Extraction -- Data Preparation -- Identify the Hotspots in a Retail Store -- Importing Images -- Getting Crowd Counts -- Security and Surveillance -- Identify the Demographics (Age and Gender) -- Summary -- Chapter 10: Explainable AI for Computer Vision -- Grad-CAM -- Grad-CAM++ -- NBDT -- Step 1 -- Step 2 -- Steps 3 and 4 -- Grad-CAM and Grad-CAM++ Implementation -- Grad-CAM and Grad-CAM++ Implementation on a Single Image -- NBDT Implementation on a Single Image -- Summary -- Index. |
| Record Nr. | UNINA-9910735390103321 |
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Kulkarni Akshay
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| New York, New York : , : Apress, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Introduction to Prescriptive AI : A Primer for Decision Intelligence Solutioning with Python / / by Akshay Kulkarni, Adarsha Shivananda, Avinash Manure
| Introduction to Prescriptive AI : A Primer for Decision Intelligence Solutioning with Python / / by Akshay Kulkarni, Adarsha Shivananda, Avinash Manure |
| Autore | Kulkarni Akshay |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023 |
| Descrizione fisica | 1 online resource (205 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
ShivanandaAdarsha
ManureAvinash |
| Soggetto topico |
Artificial intelligence
Decision making - Data processing Python (Computer program language) |
| ISBN | 1-4842-9568-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Decision Intelligence Overview -- Chapter 2: Decision Intelligence Requirements -- Chapter 3: Decision Intelligence Methodologies -- Chapter 4: Interpreting Results from Different Methodologies -- Chapter 5: Augmenting Decision Intelligence Results into the Business Workflow -- Chapter 6: Actions, Biases and Human-in-the-Loop -- Chapter 7: Case Studies. |
| Record Nr. | UNINA-9910736015903321 |
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Kulkarni Akshay
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| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Natural language processing projects : build next -generation NLP applications using AI techniques / / Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
| Natural language processing projects : build next -generation NLP applications using AI techniques / / Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni |
| Autore | Kulkarni Akshay |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2022 |
| Descrizione fisica | 1 online resource (327 pages) |
| Disciplina | 006.35 |
| Soggetto topico |
Artificial intelligence
Natural language processing (Computer science) |
| ISBN | 1-4842-7386-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Natural Language Processing and Artificial Intelligence Overview -- Chapter 2: Product360 - Sentiment and Emotion Detector -- Chapter 3: TED Talks Segmentation and Topics Extraction Using Machine Learning -- Chapter 4: Enhancing E-commerce Using an Advanced Search Engine and Recommendation System -- Chapter 5: Creating a Resume Parsing, Screening, and Shortlisting System -- Chapter 6: Creating an E-commerce Product Categorization Model Using Deep Learning -- Chapter 7: Predicting Duplicate Questions in Quora -- Chapter 8: Named Entity Recognition Using CRF and BERT -- Chapter 9: Building a Chatbot Using Transfer Learning -- Chapter 10: News Headline Summarization -- Chapter 11: Text Generation - Next Word Prediction -- Chapter 12: Conclusion and Future Trends. |
| Record Nr. | UNINA-9910735382703321 |
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Kulkarni Akshay
|
||
| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Natural language processing recipes : unlocking text data with machine learning and deep learning using Python / / Akshay Kulkarni, Adarsha Shivananda
| Natural language processing recipes : unlocking text data with machine learning and deep learning using Python / / Akshay Kulkarni, Adarsha Shivananda |
| Autore | Kulkarni Akshay |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | [Place of publication not identified] : , : Apress, , [2021] |
| Descrizione fisica | 1 online resource (302 pages) |
| Disciplina | 006.35 |
| Soggetto topico | Natural language processing (Computer science) |
| ISBN |
1-5231-5091-2
1-4842-7351-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Table of Contents -- About the Authors -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Extracting the Data -- Introduction -- Client Data -- Free Sources -- Web Scraping -- Recipe 1-1. Collecting Data -- Problem -- Solution -- How It Works -- Step 1-1. Log in to the Twitter developer portal -- Step 1-2. Execute query in Python -- Recipe 1-2. Collecting Data from PDFs -- Problem -- Solution -- How It Works -- Step 2-1. Install and import all the necessary libraries -- Step 2-2. Extract text from a PDF file -- Recipe 1-3. Collecting Data from Word Files -- Problem -- Solution -- How It Works -- Step 3-1. Install and import all the necessary libraries -- Step 3-2. Extract text from a Word file -- Recipe 1-4. Collecting Data from JSON -- Problem -- Solution -- How It Works -- Step 4-1. Install and import all the necessary libraries -- Step 4-2. Extract text from a JSON file -- Recipe 1-5. Collecting Data from HTML -- Problem -- Solution -- How It Works -- Step 5-1. Install and import all the necessary libraries -- Step 5-2. Fetch the HTML file -- Step 5-3. Parse the HTML file -- Step 5-4. Extract a tag value -- Step 5-5. Extract all instances of a particular tag -- Step 5-6. Extract all text from a particular tag -- Recipe 1-6. Parsing Text Using Regular Expressions -- Problem -- Solution -- How It Works -- Tokenizing -- Extracting Email IDs -- Replacing Email IDs -- Extracting Data from an eBook and Performing regex -- Recipe 1-7. Handling Strings -- Problem -- Solution -- How It Works -- Replacing Content -- Concatenating Two Strings -- Searching for a Substring in a String -- Recipe 1-8. Scraping Text from the Web -- Problem -- Solution -- How It Works -- Step 8-1. Install all the necessary libraries -- Step 8-2. Import the libraries -- Step 8-3. Identify the URL to extract the data.
Step 8-4. Request the URL and download the content using Beautiful Soup -- Step 8-5. Understand the website's structure to extract the required information -- Step 8-6. Use Beautiful Soup to extract and parse the data from HTML tags -- Step 8-7. Convert lists to a data frame and perform an analysis that meets business requirements -- Step 8-8. Download the data frame -- Chapter 2: Exploring and Processing Text Data -- Recipe 2-1. Converting Text Data to Lowercase -- Problem -- Solution -- How It Works -- Step 1-1. Read/create the text data -- Step 1-2. Execute the lower() function on the text data -- Recipe 2-2. Removing Punctuation -- Problem -- Solution -- How It Works -- Step 2-1. Read/create the text data -- Step 2-2. Execute the replace() function on the text data -- Recipe 2-3. Removing Stop Words -- Problem -- Solution -- How It Works -- Step 3-1. Read/create the text data -- Step 3-2. Remove punctuation from the text data -- Recipe 2-4. Standardizing Text -- Problem -- Solution -- How It Works -- Step 4-1. Create a custom lookup dictionary -- Step 4-2. Create a custom function for text standardization -- Step 4-3. Run the text_std function -- Recipe 2-5. Correcting Spelling -- Problem -- Solution -- How It Works -- Step 5-1. Read/create the text data -- Step 5-2. Execute spelling correction on the text data -- Recipe 2-6. Tokenizing Text -- Problem -- Solution -- How It Works -- Step 6-1. Read/create the text data -- Step 6-2. Tokenize the text data -- Recipe 2-7. Stemming -- Problem -- Solution -- How It Works -- Step 7-1. Read the text data -- Step 7-2. Stem the text -- Recipe 2-8. Lemmatizing -- Problem -- Solution -- How It Works -- Step 8-1. Read the text data -- Step 8-2. Lemmatize the data -- Recipe 2-9. Exploring Text Data -- Problem -- Solution -- How It Works -- Step 9-1. Read the text data -- Step 9-2. Import necessary libraries. Step 9-3 Check the number of words in the data -- Step 9-4. Compute the frequency of all words in the reviews -- Step 9-5. Consider words with length greater than 3 and plot -- Step 9-6. Build a word cloud -- Recipe 2-10. Dealing with Emojis and Emoticons -- Problem -- Solution -- How It Works -- Step 10-A1. Read the text data -- Step 10-A2. Install and import necessary libraries -- Step 10-A3. Write a function that coverts emojis into words -- Step 10-A4. Pass text with an emoji to the function -- Problem -- Solution -- How It Works -- Step 10-B1. Read the text data -- Step 10-B2. Install and import necessary libraries -- Step 10-B3. Write a function to remove emojis -- Step 10-B4. Pass text with an emoji to the function -- Problem -- Solution -- How It Works -- Step 10-C1. Read the text data -- Step 10-C2. Install and import necessary libraries -- Step 10-C3. Write function to convert emoticons into word -- Step 10-C4. Pass text with emoticons to the function -- Problem -- Solution -- How It Works -- Step 10-D1 Read the text data -- Step 10-D2. Install and import necessary libraries -- Step 10-D3. Write function to remove emoticons -- Step 10-D4. Pass text with emoticons to the function -- Problem -- Solution -- How It Works -- Step 10-E1. Read the text data -- Step 10-E2. Install and import necessary libraries -- Step 10-E3. Find all emojis and determine their meaning -- Recipe 2-11. Building a Text Preprocessing Pipeline -- Problem -- Solution -- How It Works -- Step 11-1. Read/create the text data -- Step 11-2. Process the text -- Chapter 3: Converting Text to Features -- Recipe 3-1. Converting Text to Features Using One-Hot Encoding -- Problem -- Solution -- How It Works -- Step 1-1. Store the text in a variable -- Step 1-2. Execute a function on the text data -- Recipe 3-2. Converting Text to Features Using a Count Vectorizer -- Problem. Solution -- How It Works -- Recipe 3-3. Generating n-grams -- Problem -- Solution -- How It Works -- Step 3-1. Generate n-grams using TextBlob -- Step 3-2. Generate bigram-based features for a document -- Recipe 3-4. Generating a Co-occurrence Matrix -- Problem -- Solution -- How It Works -- Step 4-1. Import the necessary libraries -- Step 4-2. Create function for a co-occurrence matrix -- Step 4-3. Generate a co-occurrence matrix -- Recipe 3-5. Hash Vectorizing -- Problem -- Solution -- How It Works -- Step 5-1. Import the necessary libraries and create a document -- Step 5-2. Generate a hash vectorizer matrix -- Recipe 3-6. Converting Text to Features Using TF-IDF -- Problem -- Solution -- How It Works -- Step 6-1. Read the text data -- Step 6-2. Create the features -- Recipe 3-7. Implementing Word Embeddings -- Problem -- Solution -- How It Works -- skip-gram -- Continuous Bag of Words (CBOW) -- Recipe 3-8. Implementing fastText -- Problem -- Solution -- How It Works -- Recipe 3-9. Converting Text to Features Using State-of-the-Art Embeddings -- Problem -- Solution -- ELMo -- Sentence Encoders -- doc2vec -- Sentence-BERT -- Universal Encoder -- InferSent -- Open-AI GPT -- How It Works -- Step 9-1. Import a notebook and data to Google Colab -- Step 9-2. Install and import libraries -- Step 9-3. Read text data -- Step 9-4. Process text data -- Step 9-5. Generate a feature vector -- Sentence-BERT -- Universal Encoder -- Infersent -- Open-AI GPT -- Step 9-6. Generate a feature vector function automatically using a selected embedding method -- Chapter 4: Advanced Natural Language Processing -- Recipe 4-1. Extracting Noun Phrases -- Problem -- Solution -- How It Works -- Recipe 4-2. Finding Similarity Between Texts -- Solution -- How It Works -- Step 2-1. Create/read the text data -- Step 2-2. Find similarities -- Phonetic Matching. Recipe 4-3. Tagging Part of Speech -- Problem -- Solution -- How It Works -- Step 3-1. Store the text in a variable -- Step 3-2. Import NLTK for POS -- Recipe 4-4. Extracting Entities from Text -- Problem -- Solution -- How It Works -- Step 4-1. Read/create the text data -- Step 4-2. Extract the entities -- Using NLTK -- Using spaCy -- Recipe 4-5. Extracting Topics from Text -- Problem -- Solution -- How It Works -- Step 5-1. Create the text data -- Step 5-2. Clean and preprocess the data -- Step 5-3. Prepare the document term matrix -- Step 5-4. Create the LDA model -- Recipe 4-6. Classifying Text -- Problem -- Solution -- How It Works -- Step 6-1. Collect and understand the data -- Step 6-2. Text processing and feature engineering -- Step 6-3. Model training -- Recipe 4-7. Carrying Out Sentiment Analysis -- Problem -- Solution -- How It Works -- Step 7-1. Create the sample data -- Step 7-2. Clean and preprocess the data -- Step 7-3. Get the sentiment scores -- Recipe 4-8. Disambiguating Text -- Problem -- Solution -- How It Works -- Step 8-1. Import libraries -- Step 8-2. Disambiguate word sense -- Recipe 4-9. Converting Speech to Text -- Problem -- Solution -- How It Works -- Step 9-1. Define the business problem -- Step 9-2. Install and import necessary libraries -- Step 9-3. Run the code -- Recipe 4-10. Converting Text to Speech -- Problem -- Solution -- How It Works -- Step 10-1. Install and import necessary libraries -- Step 10-2. Run the code with the gTTs function -- Recipe 4-11. Translating Speech -- Problem -- Solution -- How It Works -- Step 11-1. Install and import necessary libraries -- Step 11-2. Input text -- Step 11-3. Run the goslate function -- Chapter 5: Implementing Industry Applications -- Recipe 5-1. Implementing Multiclass Classification -- Problem -- Solution -- How It Works -- Step 1-1. Get the data from Kaggle. Step 1-2. Import the libraries. |
| Record Nr. | UNINA-9910735393903321 |
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Kulkarni Akshay
|
||
| [Place of publication not identified] : , : Apress, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Natural Language Processing Recipes : Unlocking Text Data with Machine Learning and Deep Learning using Python / / by Akshay Kulkarni, Adarsha Shivananda
| Natural Language Processing Recipes : Unlocking Text Data with Machine Learning and Deep Learning using Python / / by Akshay Kulkarni, Adarsha Shivananda |
| Autore | Kulkarni Akshay |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2019 |
| Descrizione fisica | 1 online resource (XXV, 234 p. 54 illus.) |
| Disciplina | 006.3 |
| Soggetto topico |
Artificial intelligence
Python (Computer program language) Open source software Computer programming Artificial Intelligence Python Open Source |
| ISBN | 1-4842-4267-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Extracting the data -- Chapter 2: Exploring and processing text data -- Chapter 3: Converting text to features -- Chapter 4: Advanced natural language processing -- Chapter 5: Implementing Industry Applications -- Chapter 6: Deep learning for NLP. |
| Record Nr. | UNINA-9910735383503321 |
|
Kulkarni Akshay
|
||
| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||