TensorFlow Developer Certificate Guide : Efficiently Tackle Deep Learning and ML Problems to Ace the Developer Certificate Exam

TensorFlow Developer Certificate Guide Efficiently Tackle Deep Learning and ML Problems to Ace the Developer Certificate Exam

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...

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Detalles Bibliográficos
Otros Autores: Fagbohun, Oluwole, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, England : Packt Publishing Ltd [2023]
Edición:First edition
Materias:
TensorFlow > Examinations > Study guides.
Machine learning > Examinations > Study guides.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009769033106719
Tabla de Contenidos:
  • 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
  • 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
  • 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
  • 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
  • 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
  • Impact of the number of filters
  • Impact of the size of the filter
  • Impact of stride
  • The boundary problem
  • Impact of padding
  • Putting it all together
  • Pooling
  • The fully connected layer
  • Fashion MNIST 2.0
  • Working with real-world images
  • Weather dataset classification
  • Image data preprocessing
  • Summary
  • Questions
  • Further reading
  • Chapter 8: Handling Overfitting
  • Technical requirements
  • Overfitting in ML
  • What triggers overfitting
  • Detecting overfitting
  • Baseline model
  • Early stopping
  • Model simplification
  • L1 and L2 regularization
  • Dropout regularization
  • Adjusting the learning rate
  • Error analysis
  • Data augmentation
  • Summary
  • Questions
  • Further reading
  • Chapter 9: Transfer Learning
  • Technical requirements.
  • Introduction to transfer learning
  • Types of transfer learning
  • Building a real-world image classifier with Transfer learning
  • Loading the data
  • Modeling
  • Modeling with transfer learning
  • Transfer learning as a fine-tuned model
  • Summary
  • Questions
  • Further reading
  • Part 3 - Natural Language Processing with TensorFlow
  • Chapter 10: Introduction to Natural Language Processing
  • Text preprocessing
  • Tokenization
  • Sequencing
  • Padding
  • Out of vocabulary
  • Word embeddings
  • The Yelp Polarity dataset
  • Embedding visualization
  • Improving the performance of the model
  • Increasing the size of the vocabulary
  • Adjusting the embedding dimension
  • Collecting more data
  • Dropout regularization
  • Trying a different optimizer
  • Summary
  • Questions
  • Further reading
  • Chapter 11: NLP with TensorFlow
  • Understanding sequential data processing - from traditional neural networks to RNNs and LSTMs
  • The anatomy of RNNs
  • Variants of RNNs - LSTM and GRU
  • Text classification using the AG News dataset - a comparative study
  • Using pretrained embeddings
  • Text classification using pretrained embedding
  • Using LSTMs to generate text
  • Story generation using LSTMs
  • Summary
  • Questions
  • Further reading
  • Part 4 - Time Series with TensorFlow
  • Chapter 12: Introduction to Time Series, Sequences, and Predictions
  • Time series analysis - characteristics, applications, and forecasting techniques
  • Characteristics of time series
  • Types of time series data
  • Applications of time series
  • Techniques for forecasting time series
  • Evaluating time series forecasting techniques
  • Retail store forecasting
  • Data partitioning
  • Naïve forecasting
  • Moving average
  • Differencing
  • Time series forecasting with machine learning
  • Sales forecasting using neural networks
  • Summary
  • Questions.
  • Chapter 13: Time Series, Sequences, and Prediction with TensorFlow
  • Understanding and applying learning rate schedulers
  • In-built learning rate schedulers
  • Custom learning rate scheduler
  • CNNs for time series forecasting
  • RNNs in time series forecasting
  • LSTMs in time series forecasting
  • CNN-LSTM architecture for time series forecasting
  • Forecasting Apple stock price data
  • Note from the author
  • Questions
  • References
  • Index
  • About Packt
  • Other Books You May Enjoy.

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