Pythonic Ai
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Pythonic AI
Unlock the power of AI with Python: Your Journey from Novice to Neural Nets KEY FEATURES ● Learn to code in Python and use Google Colab's hardware accelerators (GPU and TPU) to train and deploy AI models efficiently. ● Develop Convolutional Neural Networks (CNNs) using the TensorFlow 2 library for computer vision tasks. ● Develop sequence, attention-based, and Transformer models using the TensorFlow 2 library for Natural Language Processing (NLP) tasks. DESCRIPTION “Pythonic AI” is a book that teaches you how to build AI models using Python. It also includes practical projects in different domains so you can see how AI is used in the real world. Besides teaching how to build AI models, the book also teaches how to understand and explore the opportunities that AI presents. It includes several hands-on projects that walk you through successful AI applications, explaining concepts like neural networks, computer vision, natural language processing (NLP), and generative models. Each project in the book also reiterates and reinforces the important aspects of Python scripting. You'll learn Python coding and how it can be used to build cutting-edge AI applications. The author explains each essential line of Python code in detail, taking into account the importance and difficulty of understanding. By the end of the book, you will learn how to develop a portfolio of AI projects that will help you land your dream job in AI. WHAT YOU WILL LEARN ● Create neural network models using the TensorFlow 2 library. ● Develop Convolutional Neural Networks (CNNs) for computer vision tasks. ● Develop Sequence models for Natural Language Processing (NLP) tasks. ● Create Attention-based and Transformer models. ● Learn how to create Generative Adversarial Networks (GANs). WHO THIS BOOK IS FOR This book is for everyone who wants to learn how to build AI applications in Python, regardless of their experience level. Whether you're a student, a tech professional, a non-techie, or a technology enthusiast, this book will teach you the fundamentals of Python and AI, and show you how to apply them to real-world problems. TABLE OF CONTENTS 1. Python Kickstart: Concepts, Libraries, and Coding 2. Setting up AI Lab 3. Design My First Neural Network Model 4. Explore Designing CNN with TensorFlow 5. Develop CNN-based Image Classifier Apps 6. Train and Deploy Object Detection Models 7. Create a Text and Image Reader 8. Explore NLP for Advanced Text Analysis 9. Up and Running with Sequence Models 10. Using Sequence Models for Automated Text Classification 11. Create Attention and Transformer Models 12. Generating Captions for Images 13. Learn to Build GAN Models 14. Generate Artificial Faces Using GAN
Python for AI
"Python for AI: Applying Machine Learning in Everyday Projects" is a comprehensive guide designed for anyone keen to delve into the transformative world of artificial intelligence using the potent yet accessible Python programming language. This book meticulously covers essential AI concepts, offering readers a structured path from understanding basic Python syntax to implementing sophisticated machine learning models. With a blend of foundational theories and practical applications, each chapter deftly guides readers through relevant techniques and tools, such as TensorFlow, Keras, and scikit-learn, that are crucial for modern AI development. Whether you are a beginner taking your first steps into AI or someone with programming experience seeking to expand your skill set, this book ensures you are equipped with the knowledge needed to tackle real-world challenges. It goes beyond mere theory, providing insights into deploying and integrating AI models, handling large datasets, and effectively developing solutions applicable across various industries. By the end of this journey, readers will not only grasp the intricacies of AI projects but also gain the confidence to innovate and contribute significantly to the evolving landscape of artificial intelligence.
Learn AI with Python
Build AI applications using Python to intelligently interact with the world around you. KEY FEATURES ● Covers the practical aspects of Machine Learning and Deep Learning concepts with the help of this example-rich guide to Python. ● Includes graphical illustrations of Natural Language Processing and its implementation in NLTK. ● Covers deep learning models such as R-CNN and YOLO for object recognition and teaches how to build an image classifier using CNN. DESCRIPTION The book ‘Learn AI with Python’ is intended to provide you with a thorough understanding of artificial intelligence as well as the tools necessary to create your intelligent applications. This book introduces you to artificial intelligence and walks you through the process of establishing an AI environment on a variety of platforms. It dives into machine learning models and various predictive modeling techniques, including classification, regression, and clustering. Additionally, it provides hands-on experience with logic programming, ASR, neural networks, and natural language processing through real-world examples and fully functional Python implementation. Finally, the book deals with profound models of learning such as R-CNN and YOLO. Object detection in images is also explained in detail using Convolutional Neural Networks (CNNs), which are also explained. By the end of this book, you will have a firm grasp of machine learning and deep learning techniques, as well as a steered methodology for formulating and solving related problems. WHAT YOU WILL LEARN ● Learn to implement various machine learning and deep learning algorithms to achieve smart results. ● Understand how ML algorithms can be applied to real-life applications. ● Explore logic programming and learn how to use it practically to solve real-life problems. ● Learn to develop different types of artificial neural networks with Python. ● Understand reinforcement learning and how to build an environment and agents using Python. ● Work with NLTK and build an automatic speech recognition system. WHO THIS BOOK IS FOR This book is for anyone interested in learning about artificial intelligence and putting it into practice with Python. This book is also valuable for intermediate Machine Learning practitioners as a reference guide. Readers should be familiar with the fundamental understanding of Python programming and machine learning techniques. TABLE OF CONTENTS 1. Introduction to AI and Python 2. Machine Learning and Its Algorithms 3. Classification and Regression Using Supervised Learning 4. Clustering Using Unsupervised Learning 5. Solving Problems with Logic Programming 6. Natural Language Processing with Python 7. Implementing Speech Recognition with Python 8. Implementing Artificial Neural Network (ANN) with Python 9. Implementing Reinforcement Learning with Python 10. Implementing Deep Learning and Convolutional Neural Network