Textbook in PDF format

Designed with both beginners and professionals in mind, the book is meticulously structured to cover a broad spectrum of concepts, applications, and hands-on practices that form the core of the TensorFlow Developer Certificate exam. Starting with foundational concepts, the book guides you through the fundamental aspects of TensorFlow, Machine Learning algorithms, and Deep Learning models.
The initial chapters focus on data preprocessing, exploratory analysis, and essential tools required for building robust models. The book then delves into Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and advanced neural network techniques such as GANs and Transformer Architecture. Emphasizing practical application, each chapter is peppered with detailed explanations, code snippets, and real-world examples, allowing you to apply the concepts in various domains such as text classification, sentiment analysis, object detection, and more.
A distinctive feature of the book is its focus on various optimization and regularization techniques that enhance model performance. As the book progresses, it navigates through the complexities of deploying TensorFlow models into production. It includes exhaustive sections on TensorFlow Serving, Kubernetes Cluster, and edge computing with TensorFlow Lite. The book provides practical insights into monitoring, updating, and handling possible errors in production, ensuring a smooth transition from development to deployment.
The final chapters are devoted to preparing you for the TensorFlow Developer Certificate exam. From strategies, tips, and coding challenges to a summary of the entire learning journey, these sections serve as a robust toolkit for exam readiness. With hints and solutions provided for challenges, you can assess your knowledge and fine-tune your problem solving skills.
The organization of the book has been planned in such a way that it can cater to readers with varying degrees of knowledge. The first few chapters are broken down into manageable chunks that lay a strong foundation in data preprocessing, model building, and evaluation. Beginners will find these chapters to be approachable. The hands-on examples that illustrate the practical implementation of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and other advanced techniques are sure to be appreciated by readers who are further along in their reading journey. Discover valuable insights into cutting-edge topics such as Generative Adversarial Networks (GANs), Transformer architecture, and Edge Computing with TensorFlow Lite. This book is intended for professionals with a significant amount of professional experience.
Last but not least, this book is more than just a technical manual; rather, it is a reflection of the ongoing evolution of Machine Learning and the transformative potential that it possesses. It is about giving people the tools they need to harness the power of data, to innovate and create, and to contribute to a future in which humanity and technology coexist harmoniously.
This book is your companion no matter where you are in your journey through the world of TensorFlow; whether you are just beginning your journey or are looking to deepen your understanding. It isn't just about picking up new information; rather, it's about feeling the thrill of discovery, the accomplishment of mastery, and the fulfillment of creation. You have arrived at the beginning of a trip that will hopefully be interesting, educational, and beneficial to you. We are excited to have you join us in the world of TensorFlow.
In essence, this book is more than a mere certification guide; it's a complete roadmap to mastering TensorFlow. It aligns perfectly with the objectives of the TensorFlow Developer Certificate exam, ensuring that you are not only well-versed in the theoretical aspects but are also skilled in practical applications.
Key Learnings:
Comprehensive guide to TensorFlow, covering fundamentals to advanced topics, aiding seamless learning.
Alignment with TensorFlow Developer Certificate exam, providing targeted preparation and confidence.
In-depth exploration of neural networks, enhancing understanding of model architecture and function.
Hands-on examples throughout, ensuring practical understanding and immediate applicability of concepts.
Detailed insights into model optimization, including regularization, boosting model performance.
Extensive focus on deployment, from TensorFlow Serving to Kubernetes, for real-world applications.
Exploration of innovative technologies like BiLSTM, attention mechanisms, Transformers, fostering creativity.
Step-by-step coding challenges, enhancing problem-solving skills, mirroring real-world scenarios.
Coverage of potential errors in deployment, offering practical solutions, ensuring robust applications.
Continual emphasis on practical, applicable knowledge, making it suitable for all levels
Contents:
Introduction to Machine Learning and TensorFlow 2.x
Up and Running with Neural Networks
Building Basic Machine Learning Models
Image Recognition with CNN
Object Detection Algorithms
Text Recognition and Natural Language Processing
Strategies to Prevent Overfitting & Underfitting
Advanced Neural Networks for NLP
Productionizing TensorFlow Models
Preparing for TensorFlow Developer Certificate Exam