“…Particular highlights of the volume include Catherine Cessac's analysis of the theme of night in the plays staged at Sceaux for the duchesse du Maine, a notorious insomniac; Dominique Quéro's fascinating investigation into the future marquise de Pompadour's early career on the stage at Étiolles; David Hennebelle's wide-ranging account of the role of music in théâtres de société; Laurence Baudoux-Rousseau's finely illustrated analysis of the architecture of a number of private stages in northern France and modern-day Belgium; Laurence's Macé's richly detailed account of the performances of French plays on private stages in Italy, demonstrating clearly the importance of private theatricals as a site of cultural dialogue and exchange; Stéphanie Massé's judicious survey of representations of the body in clandestine erotic plays; Christophe Cave's timely and sophisticated consideration of the problems involved in using the Mémoires secrets as a source of information about théâtres de société; and Catriona Seth's analysis of representations of théâtres de société in a number of novels from the very end of the eighteenth century. …”

Tabla de Contenidos: “…. -- How it works... -- Fine-tuning a completion model -- How to do it... -- How it works... -- Chapter 5: Staging the OpenAI API for Application Development -- Technical requirements -- Creating a public endpoint server that calls the OpenAI API -- Getting ready -- How to do it... -- How it works... -- Extending the endpoint server to accept parameters and return data -- How to do it... -- How it works... -- Calling the user-created endpoint from no-code applications -- Getting ready -- How to do it... -- How it works... -- Chapter 6: Building Intelligent Applications with the OpenAI API -- Technical requirements -- Creating a wrapper application that generates replies to your emails -- How to do it... -- How it works... -- Creating a multi-modal application that generates travel itineraries -- How to do it... -- How it works... -- Chapter 7: Building Assistants with the OpenAI API -- Technical requirements -- Creating a knowledge-retrieval assistant application -- Getting ready -- How to do it... -- How it works... -- Creating a knowledge-retrieval assistant through the Assistants API -- How to do it... -- How it works... -- Index -- Other Books You May Enjoy…”

“…Infipats va resultar especialment valuós per a CBKT, ja que es beneficien d'estar ben ajustats i de tenir bones relacions amb HCN, així com de fortes xarxes locals que milloren el seu accés al coneixement. …”

“…Quarante ans après son apogée, le temps est venu d'analyser, en historien des idées, la rencontre inattendue entre la « faucille » et le « condor », entre une gauche radicale et le cône Sud de l'Amérique latine. Examinant le tiers-mondisme latino-américain, Mauricio Segura retrace son émergence, son apogée et sa décomposition, et il démonte ses logiques argumentative et narrative. …”

“…He was really intrigued the first time he learned about programming and realized what could be done with it. Rahul thrives on exploring diverse technologies and crafting innovative applications. …”

Tabla de Contenidos: “…Further reading -- Part 2 - Image Classification with TensorFlow -- Chapter 5: Image Classification with Neural Networks -- Technical requirements -- The anatomy of neural networks -- Forward propagation -- Activation functions -- Backward propagation -- Learning rate -- Building an image classifier with a neural network -- Loading the data -- Performing exploratory data analysis -- Building the model -- Compiling the model -- Model visualization -- Model fitting -- Training monitoring -- Evaluating the model -- Model prediction -- Summary -- Questions -- Further reading -- Chapter 6: Improving the Model -- Technical requirements -- Data is key -- Fine-tuning hyperparameters of a neural network -- Increasing the number of epochs -- Early stopping using callbacks -- Adding neurons in the hidden layer -- Changing the optimizers -- Changing the learning rate -- Summary -- Questions -- Further reading -- Chapter 7: Image Classification with Convolutional Neural Networks -- Challenges of image recognition with fully connected networks -- Anatomy of CNNs -- Convolutions -- Impact of the number of filters -- Impact of the size of the filter -- Impact of stride -- The boundary problem -- Impact of padding -- Putting it all together -- Pooling -- The fully connected layer -- Fashion MNIST 2.0 -- Working with real-world images -- Weather dataset classification -- Image data preprocessing -- Summary -- Questions -- Further reading -- Chapter 8: Handling Overfitting -- Technical requirements -- Overfitting in ML -- What triggers overfitting -- Detecting overfitting -- Baseline model -- Early stopping -- Model simplification -- L1 and L2 regularization -- Dropout regularization -- Adjusting the learning rate -- Error analysis -- Data augmentation -- Summary -- Questions -- Further reading -- Chapter 9: Transfer Learning -- Technical requirements…”

“…En els últims anys, s’ha estudiat l’efecte dels forats negres acústics (ABHs) per tal de controlar el soroll en conductes, tot i que originàriament es van desenvolupar per la propagació d’ones estructurals en bigues i plaques. Un ABH es pot caracteritzar de diferents maneres: per mitjà de tècniques matemàtiques analítiques, amb el mètode de les matrius de transferència (TMM) o amb el mètode numèric dels elements finits (FEM), conjuntament amb experiments. …”
Accés lliure

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Part 1: NLP Basics -- Chapter 1: Introduction to NLP -- Introduction to natural language processing -- NLU + NLG = NLP -- NLU -- NLG -- Gensim and its NLP modeling techniques -- BoW and TF-IDF -- LSA/LSI -- Word2Vec -- Doc2Vec -- LDA -- Ensemble LDA -- Topic modeling with BERTopic -- Common NLP Python modules included in this book -- spaCy -- NLTK -- Summary -- Questions -- References -- Chapter 2: Text Representation -- Technical requirements -- What word embedding is -- Simple encoding methods -- One-hot encoding -- BoW -- Bag-of-N-grams -- What TF-IDF is -- Shining applications of BoW and TF-IDF -- Coding - BoW -- Gensim for BoW -- scikit-learn for BoW (CountVectorizer) -- Coding - Bag-of-N-grams -- Gensim for N-grams -- scikit-learn for N-grams -- NLTK for N-grams -- Coding - TF-IDF -- Gensim for TF-IDF -- scikit-learn for TF-IDF -- Summary -- Questions -- References -- Chapter 3: Text Wrangling and Preprocessing -- Technical requirements -- Key steps in NLP preprocessing -- Tokenization -- Lowercase conversion -- Stop word removal -- Punctuation removal -- Stemming -- Lemmatization -- Coding with spaCy -- spaCy for lemmatization -- spaCy for PoS -- Coding with NLTK -- NLTK for tokenization -- NLTK for stop-word removal -- NLTK for lemmatization -- Coding with Gensim -- Gensim for preprocessing -- Gensim for stop-word removal -- Gensim for stemming -- Building a pipeline with spaCy -- Summary -- Questions -- References -- Part 2: Latent Semantic Analysis/Latent Semantic Indexing -- Chapter 4: Latent Semantic Analysis with scikit-learn -- Technical requirements -- Understanding matrix operations -- An orthogonal matrix -- The determinant of a matrix -- Understanding a transformation matrix -- A transformation matrix in daily life examples…”

Tabla de Contenidos: “…Compromising biometric authentication -- Password cracking with GANs -- Malware detection evasion -- GANs in cryptography and stenography -- Generating web attack payloads with GANs -- Generating adversarial attack payloads -- Defenses and mitigations -- Securing GANs -- GAN-assisted adversarial attacks -- Deepfakes, malicious content, and misinformation -- Summary -- Chapter 13: LLM Foundations for Adversarial AI -- A brief introduction to LLMs -- Developing AI applications with LLMs -- Hello LLM with Python -- Hello LLM with LangChain -- Bringing your own data -- How LLMs change Adversarial AI -- Summary -- Chapter 14: Adversarial Attacks with Prompts -- Adversarial inputs and prompt injection -- Direct prompt injection -- Prompt override -- Style injection -- Role-playing -- Impersonation -- Other jailbreaking techniques -- Automated gradient-based prompt injection -- Risks from bringing your own data -- Indirect prompt injection -- Data exfiltration with prompt injection -- Privilege escalation with prompt injection -- RCE with prompt injection -- Defenses and mitigations -- LLM platform defenses -- Application-level defenses -- Summary -- Chapter 15: Poisoning Attacks and LLMs -- Poisoning embeddings in RAG -- Attack scenarios -- Poisoning during embedding generation -- Direct embeddings poisoning -- Advanced embeddings poisoning -- Query embeddings manipulation -- Defenses and mitigations -- Poisoning attacks on fine-tuning LLMs -- Introduction to fine-tuning LLMs -- Fine-tuning poisoning attack scenarios -- Fine-tuning attack vectors -- Poisoning ChatGPT 3.5 with fine-tuning -- Defenses and mitigations against poisoning attacks in fine-tuning -- Summary -- Chapter 16: Advanced Generative AI Scenarios -- Supply-chain attacks in LLMs -- Publishing a poisoned LLM on Hugging Face -- Publishing a tampered LLM on Hugging Face…”

“…Additionally, you'll learn about creating and using LLM-enabling tools, monitoring and fine tuning, LangChain UI tools such as LangFlow, and the LangChain ecosystem. …”

“…Pick up tricks and tips from celebrated engineers and producers across the stylistic spectrum, including Steve Albini, Neal Avron, Roy Thomas Baker, Joe Barresi, Howard Benson, Tchad Blake, T-Bone Burnett, Joe Chiccarelli, Jack Douglas, Gus Dudgeon, Geoff Emerick, Brian Eno, Paul Epworth, Shawn Everett, Humberto Gatica, Nigel Godrich, Imogen Heap, Ross Hogarth, Trevor Horn, Rodney Jerkins, Glyn Johns, Leslie Ann Jones, Eddie Kramer, Jacquire King, Daniel Lanois, Sylvia Massy, Alan Meyerson, Justin Niebank, Hugh Padgham, Gary Paczosa, Tony Platt, Jack Joseph Puig, David Reitzas, Bob Rock, Rafa Sardina, Jim Scott, Laura Sisk, Fraser T Smith, Randy Staub, Bruce Swedien, Butch Vig, Tony Visconti, Butch Walker, Matt Wallace, Young Guru, and many, many more... …”

“…He was really intrigued the first time he learned about programming and realized what could be done with it. He loves to explore different technologies and create applications to build something new. …”

“…This course assumes you have your own laptop, and the code will be done using Colab. About The Author Yiqiao Yin: Yiqiao Yin was a Ph.D. student in statistics at Columbia University from September of 2020 to December 2021. …”

“…Ensure compliance across the top cloud players by diving into AWS, Azure, and GCP cloud auditing to minimize security risks Key Features Leverage best practices and emerging technologies to effectively audit a cloud environment Get better at auditing and unlock career opportunities in cloud audits and compliance Explore multiple assessments of various features in a cloud environment to see how it's done Book Description As more and more companies are moving to cloud and multi-cloud environments, being able to assess the compliance of these environments properly is becoming more important. …”

“…In it, you'll explore the most common use cases such as logging and managing streaming data. When you're done, you'll be ready to handle both basic developer- and admin-based tasks in a Kafka-focused team. about the audience For intermediate Java developers or data engineers. …”

“…They can direct ChatGPT to access up-to-date information, run computations, or use third-party services and to interact with APIs defined by developers, enhancing ChatGPT's capabilities and allowing it to perform a wide range of actions. Done right, they can help businesses provide exceptional customer experiences, increase sales, automate redundant tasks, enhance brand loyalty, and much more. …”

“…As you build up your new application, you'll master the key features of Blazor, such as routing, forms and validation, and dynamic and reusable components. By the time you're done, you'll be ready to develop beautiful sites and apps that seamlessly execute your C# code natively in the browser. …”

“…As you build up your new application, you'll master the key features of Blazor, such as routing, forms and validation, and dynamic and reusable components. By the time you're done, you'll be ready to develop beautiful sites and apps that seamlessly execute your C# code natively in the browser. …”

“…Hands-on case studies show you how each algorithm works in production. By the time you're done, you'll know the benefits, limitations, and practical methods of applying ensemble machine learning to real-world data, and be ready to build more explainable ML systems. …”

“…Hands-on case studies show you how each algorithm works in production. By the time you're done, you'll know the benefits, limitations, and practical methods of applying ensemble machine learning to real-world data, and be ready to build more explainable ML systems. …”