Generative Ai With Python And Tensorflow
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Generative Ai With Python And Tensorflow
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Author : Anand Vemula
language : en
Publisher: Independently Published
Release Date : 2024-07-03
Generative Ai With Python And Tensorflow written by Anand Vemula and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-03 with Computers categories.
Generative AI with Python and TensorFlow: A Complete Guide to Mastering AI Models is a comprehensive resource for anyone looking to delve into the world of generative artificial intelligence. Introduction Overview of Generative AI: Understand the basic concepts, history, and significance of generative AI. Importance of Generative AI: Learn about the transformative potential of generative AI in various industries. Applications and Use Cases: Explore real-world applications of generative AI in fields such as art, music, text generation, and data augmentation. Overview of Python and TensorFlow: Get an introduction to the essential tools and libraries used for building generative AI models. Getting Started: Set up your development environment, install necessary libraries, and take your first steps with TensorFlow. Fundamentals of Machine Learning Supervised vs. Unsupervised Learning: Understand the differences and use cases of these two primary types of machine learning. Neural Networks Basics: Learn the fundamental concepts of neural networks and their role in AI. Introduction to Deep Learning: Dive deeper into the advanced techniques of deep learning and its applications in generative AI. Key Concepts in Generative AI: Familiarize yourself with the essential concepts and terminologies in generative AI. Generative Models Understanding Generative Models: Explore the theoretical foundations of generative models. Types of Generative Models: Learn about various types of generative models, including VAEs, GANs, autoregressive models, and flow-based models. Variational Autoencoders (VAEs): Delve into the theory behind VAEs, build and train VAEs with TensorFlow, and explore their use cases. Generative Adversarial Networks (GANs): Get introduced to GANs, understand their architecture, implement GANs with TensorFlow, and learn advanced GAN techniques. Autoregressive Models: Understand autoregressive models, implement them with TensorFlow, and explore their applications. Flow-based Models: Learn about flow-based models, build them with TensorFlow, and explore their practical applications. Advanced Topics Transfer Learning for Generative Models: Explore how transfer learning can be applied to generative models. Conditional Generative Models: Understand and implement models that generate outputs conditioned on specific inputs. Multimodal Generative Models: Learn about models that can generate multiple types of data simultaneously. Reinforcement Learning in Generative AI: Explore the intersection of reinforcement learning and generative AI. Practical Applications Image Generation and Style Transfer: Create stunning images and apply style transfer techniques. Text Generation and Natural Language Processing: Generate coherent and contextually relevant text using advanced NLP techniques. Music and Sound Generation: Compose music and generate new sounds using generative AI. Data Augmentation for Machine Learning: Improve your machine learning models by augmenting your datasets with generative models. Hands-On Projects Project 1: Creating Art with GANs: Step-by-step guide to building a GAN to generate art. Project 2: Text Generation with LSTM: Implement an LSTM model for generating text. Project 3: Building a VAE for Image Reconstruction: Learn how to build and train a VAE for image reconstruction. Project 4: Music Generation with RNNs: Create a music generation model using RNNs.
Generative Ai With Python And Tensorflow 2
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Author : Joseph Babcock
language : en
Publisher:
Release Date : 2021-04-30
Generative Ai With Python And Tensorflow 2 written by Joseph Babcock and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-30 with categories.
Packed with intriguing real-world projects as well as theory, Generative AI with Python and TensorFlow 2 enables you to leverage artificial intelligence creatively and generate human-like data in the form of speech, text, images, and music.
Generative Ai With Python And Tensorflow 2
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Author : Joseph Babcock
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-04-30
Generative Ai With Python And Tensorflow 2 written by Joseph Babcock and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-30 with Computers categories.
This edition is heavily outdated and we have a new edition with PyTorch examples published! Key Features Code examples are in TensorFlow 2, which make it easy for PyTorch users to follow along Look inside the most famous deep generative models, from GPT to MuseGAN Learn to build and adapt your own models in TensorFlow 2.x Explore exciting, cutting-edge use cases for deep generative AI Book DescriptionMachines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI? In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks. There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation.What you will learn Export the code from GitHub into Google Colab to see how everything works for yourself Compose music using LSTM models, simple GANs, and MuseGAN Create deepfakes using facial landmarks, autoencoders, and pix2pix GAN Learn how attention and transformers have changed NLP Build several text generation pipelines based on LSTMs, BERT, and GPT-2 Implement paired and unpaired style transfer with networks like StyleGAN Discover emerging applications of generative AI like folding proteins and creating videos from images Who this book is for This is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.
Hands On Ai Programming With Python
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Author : Khushabu Gupta
language : en
Publisher: Subrat Gupta
Release Date : 2025-09-30
Hands On Ai Programming With Python written by Khushabu Gupta and has been published by Subrat Gupta this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-30 with Computers categories.
Unlock the full potential of artificial intelligence with 'Hands-On AI Programming with Python.' This comprehensive guide empowers beginners and seasoned developers alike to master modern AI techniques from the ground up. Dive into practical, real-world projects that cover machine learning, deep learning, and generative AI using powerful frameworks like TensorFlow, PyTorch, and FastAPI. Learn to build, train, and deploy smarter applications using Python, tackle hands-on projects such as image recognition, natural language processing, and AI-powered APIs, and grasp industry best practices for performance and scalability. This 2025 edition is updated to reflect the latest trends, tools, and workflows in the rapidly-evolving AI landscape. With step-by-step instructions, code examples, and expert insights, you’ll develop the confidence to innovate and create robust AI solutions. Whether you're an aspiring data scientist, an AI enthusiast, or a developer seeking to expand your skill set, this book is the key to mastering applied AI programming and advancing your career in today’s tech-driven world.
Generative Adversarial Networks Projects
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Author : Kailash Ahirwar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-01-31
Generative Adversarial Networks Projects written by Kailash Ahirwar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-31 with Mathematics categories.
Explore various Generative Adversarial Network architectures using the Python ecosystem Key FeaturesUse different datasets to build advanced projects in the Generative Adversarial Network domainImplement projects ranging from generating 3D shapes to a face aging applicationExplore the power of GANs to contribute in open source research and projectsBook Description Generative Adversarial Networks (GANs) have the potential to build next-generation models, as they can mimic any distribution of data. Major research and development work is being undertaken in this field since it is one of the rapidly growing areas of machine learning. This book will test unsupervised techniques for training neural networks as you build seven end-to-end projects in the GAN domain. Generative Adversarial Network Projects begins by covering the concepts, tools, and libraries that you will use to build efficient projects. You will also use a variety of datasets for the different projects covered in the book. The level of complexity of the operations required increases with every chapter, helping you get to grips with using GANs. You will cover popular approaches such as 3D-GAN, DCGAN, StackGAN, and CycleGAN, and you’ll gain an understanding of the architecture and functioning of generative models through their practical implementation. By the end of this book, you will be ready to build, train, and optimize your own end-to-end GAN models at work or in your own projects. What you will learnTrain a network on the 3D ShapeNet dataset to generate realistic shapesGenerate anime characters using the Keras implementation of DCGANImplement an SRGAN network to generate high-resolution imagesTrain Age-cGAN on Wiki-Cropped images to improve face verificationUse Conditional GANs for image-to-image translationUnderstand the generator and discriminator implementations of StackGAN in KerasWho this book is for If you’re a data scientist, machine learning developer, deep learning practitioner, or AI enthusiast looking for a project guide to test your knowledge and expertise in building real-world GANs models, this book is for you.
Hands On Deep Learning Architectures With Python
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Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-04-30
Hands On Deep Learning Architectures With Python written by Yuxi (Hayden) Liu and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-30 with Computers categories.
Concepts, tools, and techniques to explore deep learning architectures and methodologies Key FeaturesExplore advanced deep learning architectures using various datasets and frameworksImplement deep architectures for neural network models such as CNN, RNN, GAN, and many moreDiscover design patterns and different challenges for various deep learning architecturesBook Description Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems. Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations. By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world. What you will learnImplement CNNs, RNNs, and other commonly used architectures with PythonExplore architectures such as VGGNet, AlexNet, and GoogLeNetBuild deep learning architectures for AI applications such as face and image recognition, fraud detection, and many moreUnderstand the architectures and applications of Boltzmann machines and autoencoders with concrete examples Master artificial intelligence and neural network concepts and apply them to your architectureUnderstand deep learning architectures for mobile and embedded systemsWho this book is for If you’re a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book
Machine Learning Mastery With Scikit Learn Tensorflow And Keras
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Author : Geoffrey Andrew
language : en
Publisher: Independently Published
Release Date : 2025-11
Machine Learning Mastery With Scikit Learn Tensorflow And Keras written by Geoffrey Andrew and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11 with Computers categories.
What if you could make machines think, learn, and create, even if you've never written a single line of AI code before? The truth is, most beginners feel lost when they first try to learn Machine Learning. Endless tutorials, confusing math, and intimidating jargon make it feel like AI is only for experts. But it doesn't have to be that way. Machine Learning Mastery with Scikit-Learn, TensorFlow, and Keras is not just another technical manual. It's your step-by-step mentor, written in plain English, that takes you from zero to confidently building real AI systems that work. Whether you're a student, developer, or complete beginner, this book makes complex topics feel simple and practical. You'll start small, learning how machines learn from data, and soon you'll be building intelligent systems that can see, read, predict, and even create. Inside, you'll learn to: Build powerful Machine Learning models with Scikit-Learn and TensorFlow Understand deep learning and design your own neural networks Work on real projects like image recognition, text analysis, and generative AI Clean and prepare data the right way so your models actually perform Avoid the beginner mistakes that ruin most ML projects Turn your ideas into working AI applications you can be proud of Each chapter feels like having a friendly mentor by your side, one who explains, demonstrates, and guides you as you learn by doing. You won't just read about AI. You'll build it. This isn't just a book. It's a roadmap. A companion. A bridge between confusion and confidence. So if you've ever looked at AI and thought, "I wish I could do that,", this is your moment. Stop watching the AI revolution happen. Start leading it.
Generative Ai With Python
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Author : CODE. PLANET
language : en
Publisher: Independently Published
Release Date : 2025-01-27
Generative Ai With Python written by CODE. PLANET and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-27 with Computers categories.
The limitless potential of generative AI and transform your ideas into reality with Generative AI with Python! This comprehensive guide is your gateway to the fascinating world of generative artificial intelligence, where creativity meets cutting-edge technology. Perfect for developers, data scientists, and AI enthusiasts, this book takes you on an exciting journey to master the art of building intelligent systems capable of creating text, images, music, and more. Through clear explanations, hands-on projects, and real-world examples, you'll discover: The fundamentals of generative AI, including machine learning, deep learning, and neural networks. How to leverage Python and powerful frameworks like TensorFlow, PyTorch, and Hugging Face to create AI-driven models. Techniques to build and fine-tune text generators, image creators, chatbots, and other generative applications. The underlying principles behind popular tools like ChatGPT, DALL-E, and Stable Diffusion-and how to design your own custom systems. Ethical considerations and best practices to ensure responsible AI development. Whether you're crafting unique solutions for businesses, exploring creative pursuits, or simply diving into one of the most transformative technologies of our time, Generative AI with Python equips you with the knowledge and skills to stay ahead in this rapidly evolving field. Step into the future of AI innovation and start building systems that inspire and innovate today!
Practical Generative Ai With Python
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Author : Anand Vemula
language : en
Publisher: Anand Vemula
Release Date :
Practical Generative Ai With Python written by Anand Vemula and has been published by Anand Vemula this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
This book covers the fundamentals of generative AI, providing an in-depth understanding of key concepts, algorithms, and techniques that power AI-driven content creation. Starting with an introduction to the basics of generative AI, the book explains the theoretical foundations and evolution of generative models, highlighting the significance of this technology in various domains such as image synthesis, text generation, and more. Readers will explore the different types of machine learning, including supervised, unsupervised, and reinforcement learning, and understand their role in the development of generative models. The guide dives into essential Python libraries like TensorFlow, PyTorch, NumPy, and Pandas, offering a hands-on approach to building generative models from scratch. Each chapter is packed with practical examples, case studies, and real-world scenarios that demonstrate the application of these models in various fields, including art, music, and conversational AI. Key topics include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based models, autoregressive models, and transformer-based models like GPT. The book also addresses the ethical considerations surrounding generative AI, providing insights into the challenges of bias, fairness, and misinformation. Readers will benefit from step-by-step tutorials that guide them through the process of implementing and optimizing generative models, complete with code examples and hands-on exercises. Additionally, the book offers advanced techniques for improving model performance and stability, ensuring that readers are well-prepared to tackle complex AI projects. Whether you're a beginner looking to understand the basics of generative AI or an experienced developer aiming to enhance your skills, "Mastering Generative AI with Python: A Hands-On Guide" serves as an essential resource for anyone interested in the rapidly evolving field of generative AI.
Machine Learning And Deep Learning Using Python And Tensorflow
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Author : Venkata Reddy Konasani
language : en
Publisher: McGraw Hill Professional
Release Date : 2021-04-29
Machine Learning And Deep Learning Using Python And Tensorflow written by Venkata Reddy Konasani and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-29 with Technology & Engineering categories.
Understand the principles and practices of machine learning and deep learning This hands-on guide lays out machine learning and deep learning techniques and technologies in a style that is approachable, using just the basic math required. Written by a pair of experts in the field, Machine Learning and Deep Learning Using Python and TensorFlow contains case studies in several industries, including banking, insurance, e-commerce, retail, and healthcare. The book shows how to utilize machine learning and deep learning functions in today’s smart devices and apps. You will get download links for datasets, code, and sample projects referred to in the text. Coverage includes: Machine learning and deep learning concepts Python programming and statistics fundamentals Regression and logistic regression Decision trees Model selection and cross-validation Cluster analysis Random forests and boosting Artificial neural networks TensorFlow and Keras Deep learning hyperparameters Convolutional neural networks Recurrent neural networks and long short-term memory