| Description |
1 online resource |
| Contents |
Cover -- Title Page -- Copyright and Credits -- Dedication -- Contributors -- Table of Contents -- Preface -- Part 1 -- Introduction to TensorFlow -- Chapter 1: Introduction to Machine Learning -- What is ML? -- Types of ML algorithms -- Supervised learning -- Unsupervised learning -- Semi-supervised learning -- Reinforcement Learning -- ML life cycle -- The business case -- Data gathering and understanding -- Modeling -- Error analysis -- Model deployment and monitoring -- Exploring ML use cases -- Healthcare -- The retail industry -- The entertainment industry -- Education -- Agriculture |
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Introducing the learning journey -- Why take the exam? -- What is the exam all about? -- How to ace the exam -- When to take the exam -- Exam tips -- What to expect after the exam -- Summary -- Questions -- Further reading -- Chapter 2: Introduction to TensorFlow -- Technical requirements -- What is TensorFlow? -- Setting up our environment -- Data representation -- Creating tensors -- Tensor rank -- Properties of tensors -- Basic tensor operations -- Hello World in TensorFlow -- Debugging and solving error messages -- Summary -- Questions -- Further reading |
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Chapter 3: Linear Regression with TensorFlow -- Technical requirements -- Linear regression with TensorFlow -- Evaluating regression models -- Salary prediction with TensorFlow -- Loading the data -- Data preprocessing -- Model building -- Model evaluation -- Making predictions -- Saving and loading models -- Summary -- Questions -- Further reading -- Chapter 4: Classification with TensorFlow -- Technical requirements -- Classification with TensorFlow -- Evaluating classification models -- Confusion matrix -- A student dropout prediction -- Loading the data -- Exploratory data analysis |
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Data preprocessing -- Model building -- Classification performance evaluation -- Summary -- Questions -- 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 |
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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 |
| Summary |
Achieve TensorFlow certification with this comprehensive guide covering all exam topics using a hands-on, step-by-step approach--perfect for aspiring TensorFlow developers Key Features Build real-world computer vision, natural language, and time series applications Learn how to overcome issues such as overfitting with techniques such as data augmentation Master transfer learning--what it is and how to build applications with pre-trained models Purchase of the print or Kindle book includes a free PDF eBook Book Description The TensorFlow Developer Certificate Guide is an indispensable resource for machine learning enthusiasts and data professionals seeking to master TensorFlow and validate their skills by earning the certification. This practical guide equips you with the skills and knowledge necessary to build robust deep learning models that effectively tackle real-world challenges across diverse industries. You'll embark on a journey of skill acquisition through easy-to-follow, step-by-step explanations and practical examples, mastering the craft of building sophisticated models using TensorFlow 2.x and overcoming common hurdles such as overfitting and data augmentation. With this book, you'll discover a wide range of practical applications, including computer vision, natural language processing, and time series prediction. To prepare you for the TensorFlow Developer Certificate exam, it offers comprehensive coverage of exam topics, including image classification, natural language processing (NLP), and time series analysis. With the TensorFlow certification, you'll be primed to tackle a broad spectrum of business problems and advance your career in the exciting field of machine learning. Whether you are a novice or an experienced developer, this guide will propel you to achieve your aspirations and become a highly skilled TensorFlow professional. What you will learn Prepare for success in the TensorFlow Developer Certification exam Master regression and classification modelling with TensorFlow 2.x Build, train, evaluate, and fine-tune deep learning models Combat overfitting using techniques such as dropout and data augmentation Classify images, encompassing preprocessing and image data augmentation Apply TensorFlow for NLP tasks like text classification and generation Predict time series data, such as stock prices Explore real-world case studies and engage in hands-on exercises Who this book is for This book is for machine learning and data science enthusiasts, as well as data professionals aiming to demonstrate their expertise in building deep learning applications with TensorFlow. Through a comprehensive hands-on approach, this book covers all the essential exam prerequisites to equip you with the skills needed to excel as a TensorFlow developer and advance your career in machine learning. A fundamental grasp of Python programming is the only prerequisite |
| Notes |
Includes index |
| SUBJECT |
TensorFlow -- Examinations -- Study guides
|
| Subject |
Machine learning -- Examinations -- Study guides
|
|
Examinations
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| Genre/Form |
Study guides
|
| Form |
Electronic book
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| ISBN |
9781803249209 |
|
180324920X |
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