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The TensorFlow Workshop: A hands-on guide to building deep learning models from scratch using real-world datasets
XAF 38135
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Combining clear explanations, realistic examples, and plenty of hands-on practice, it'll quickly get you up and running.
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- Get started with TensorFlow fundamentals to build and train deep learning models with real-world data, practical exercises, and challenging activitiesKey FeaturesUnderstand the fundamentals of tensors, neural networks, and deep learningDiscover how to implement and fine-tune deep learning models for real-world datasetsBuild your experience and confidence with hands-on exercises and activitiesBook DescriptionGetting to grips with tensors, deep learning, and neural networks can be intimidating and confusing for anyone, no matter their experience level. The breadth of information out there, often written at a very high level and aimed at advanced practitioners, can make getting started even more challenging.If this sounds familiar to you, The TensorFlow Workshop is here to help. Combining clear explanations, realistic examples, and plenty of hands-on practice, it’ll quickly get you up and running.You’ll start off with the basics – learning how to load data into TensorFlow, perform tensor operations, and utilize common optimizers and activation functions. As you progress, you’ll experiment with different TensorFlow development tools, including TensorBoard, TensorFlow Hub, and Google Colab, before moving on to solve regression and classification problems with sequential models.Building on this solid foundation, you’ll learn how to tune models and work with different types of neural network, getting hands-on with real-world deep learning applications such as text encoding, temperature forecasting, image augmentation, and audio processing.By the end of this deep learning book, you’ll have the skills, knowledge, and confidence to tackle your own ambitious deep learning projects with TensorFlow.What you will learnGet to grips with TensorFlow’s mathematical operationsPre-process a wide variety of tabular, sequential, and image dataUnderstand the purpose and usage of different deep learning layersPerform hyperparameter-tuning to prevent overfitting of training dataUse pre-trained models to speed up the development of learning modelsGenerate new data based on existing patterns using generative modelsWho this book is forThis TensorFlow book is for anyone who wants to develop their understanding of deep learning and get started building neural networks with TensorFlow. Basic knowledge of Python programming and its libraries, as well as a general understanding of the fundamentals of data science and machine learning, will help you grasp the topics covered in this book more easily.Table of ContentsIntroduction to Machine Learning with TensorFlowLoading and Processing DataTensorFlow DevelopmentRegression and Classification ModelsClassification ModelsRegularization and Hyperparameter TuningConvolutional Neural NetworksPre-Trained NetworksRecurrent Neural NetworksCustom TensorFlow ComponentsGenerative Models
| Publisher | Packt Publishing |
| Publication date | December 15, 2021 |
| Language | English |
| Print length | 600 pages |
| ISBN-10 | 1800205252 |
| ISBN-13 | 978-1800205253 |
| Item Weight | 2.35 pounds (1.07 kg) |
| Dimensions | 7.5 x 1.36 x 9.25 inches (19.1 x 3.5 x 23.5 cm) |
Who Should Buy?
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Aspiring Data Scientists
Perfect for individuals seeking hands-on experience in deep learning and practical applications using TensorFlow.
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Developers Transitioning to AI
Great for software developers wanting to expand their skills into machine learning and artificial intelligence domains.
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Educators and Trainers
Useful for educators looking for a structured way to teach deep learning concepts through practical projects.
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Complete Beginners
Not suited for those with no prior programming or machine learning experience, as it requires foundational knowledge.
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Advanced Researchers
Might not satisfy advanced researchers looking for cutting-edge theories, as it focuses more on practical applications.
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Casual Learners
Not ideal for casual learners seeking quick overviews, as it demands hands-on engagement and commitment.
Product Description
About This Item
Get ready to take your deep learning skills to the next level with The TensorFlow Workshop! This hands-on guide is your ticket to mastering the art of building powerful deep learning models from scratch using real-world datasets. Whether you're an online retailer, e-commerce business, or simply looking to enhance your online store, this workshop is your key to unlocking the full potential of TensorFlow in the e-commerce realm. Dive into practical applications and implementation strategies that will revolutionize the way you analyze real-world datasets and optimize your e-commerce performance. With The TensorFlow Workshop, you'll gain invaluable insights into leveraging deep learning models to elevate your online retail experience. Don't miss out on this opportunity to explore the endless possibilities of real-world datasets and revolutionize the way you do business online.
Take your e-commerce game to the next level with The TensorFlow Workshop today!.
Customer Questions & Answers
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Question:
What prior knowledge is required for this book?
Answer: Basic knowledge of Python programming and its libraries, as well as a general understanding of the fundamentals of data science and machine learning. -
Question:
What types of data processing are covered in this book?
Answer: The book covers pre-processing a wide variety of tabular, sequential, and image data. -
Question:
What are some of the real-world applications covered in the book?
Answer: Real-world deep learning applications such as text encoding, temperature forecasting, image augmentation, and audio processing are covered.
Intelligence & Semantics Editorial Review
## Editorial Review of "The TensorFlow Workshop" "The TensorFlow Workshop" serves as a practical resource for those interested in diving into the intricacies of TensorFlow without getting overwhelmed by complex theoretical underpinnings. Designed primarily for newcomers to TensorFlow — but not necessarily to data science or machine learning as a whole — the book adeptly avoids excessive theory, focusing instead on an engineering-based approach that's much more hands-on. This makes it particularly suitable for individuals looking to develop practical deep learning models quickly. The structure of the book is well-crafted, with early chapters laying a solid foundation around the essentials of TensorFlow 2.x, including tensors, optimization, activation functions, and data processing. The pedagogy shines through in the practical examples that encourage readers to engage actively with the content rather than merely copying code. Readers are equipped with valuable resources such as TensorBoard and TensorFlow Hub, which further enhance the learning experience. While the book excels in offering a clear guide through various project types, there are thematic gaps that some users noted. The discussions on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) could benefit from more updated techniques, such as attention mechanisms and transformer models. Although intended for beginners, the absence of thorough explanations about how models function mathematically might leave some readers lacking the ability to troubleshoot or innovate beyond the examples provided. In addition, although the book covers a respectable range of topics, more advanced features of TensorFlow — such as the handling of unstructured data or reinforcement learning — could be seen as missed opportunities. Nevertheless, for those willing to put in the work of coding alongside the guidance provided, this book stands as an invaluable first step in mastering TensorFlow. Overall, "The TensorFlow Workshop" is an excellent guide for those eager to familiarize themselves with TensorFlow and explore its capabilities to develop various machine learning models. It offers a hands-on approach that is both instructive and engaging, making it a worthy addition to the libraries of budding machine learning practitioners. ###
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Pros
- Practical focus on building deep learning models.
- Hands-on approach encourages active coding and problem-solving.
- Well-structured chapters covering essential TensorFlow components.
- Good range of projects from computer vision to NLP.
- Clear step-by-step instructions with ample examples. ###
Cons
- Limited theoretical background on neural networks.
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XAF 38135
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Ubuy works hard to protect your security and privacy. Our advanced payment security system ensures confidentiality by encrypting your information during transmission using AES (Advanced Encryption Standards) and SSL (Secure Socket Layer) protocols. Your payment details are 100% secure as we do not share your payment details with third party sellers.
Features & Benefits
- Understand the fundamentals of tensors, neural networks, and deep learning
- Discover how to implement and fine-tune deep learning models for real-world datasets
- Build your experience and confidence with hands-on exercises and activities
- Learn to work with different types of neural networks and apply them to real-world applications
- Gain skills to tackle ambitious deep learning projects with TensorFlow

