Nulled Oreilly – Deep Learning with R, Second Edition, Video Edition 2022-10

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Oreilly – Deep Learning with R, Second Edition, Video Edition 2022-10

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Description

Deep Learning with R course, Second Edition, Video Edition. In this course you will learn:

• Deep learning from the basics
• Image classification and image segmentation
• Time series forecasting
• Text classification and machine translation
• Text production, neural style transfer and image production

Deep learning has become essential for data scientists, researchers and software developers. The R language APIs for Keras and TensorFlow make deep learning accessible to all R users, even if they have no experience with advanced machine learning or neural networks. This book shows you how to get started using R for core DL tasks such as computer vision, natural language processing, and more.

What you will learn in this course

• Image classification and image segmentation
• Time series forecasting
• Text classification and machine translation
• Text production, neural style transfer and image production

This course is suitable for people who

• Have intermediate skills in R.
• No prior experience with Keras, TensorFlow, or deep learning is necessary.

Course details

• Publisher: Oreilly
• Instructor: Francois Chollet ,  Sigrid Keydana ,  Tomasz Kalinowski ,  JJ Allaire
• Training level: beginner to advanced
• Training duration: 15 hours and 28 minutes

Course headings

• Chapter 1. What is deep learning?
  Chapter 1. Before deep learning: A brief history of machine learning
  Chapter 1. Why deep learning? Why now?
• Chapter 2. The mathematical building blocks of neural networks
  Chapter 2. Data representations for neural networks
  Chapter 2. The gears of neural networks: Tensor operations
  Chapter 2. The engine of neural networks: Gradient-based optimization
  Chapter 2. Looking back at our first example
  Chapter 2. Summary
• Chapter 3. Introduction to Keras and TensorFlow
  Chapter 3. What’s Keras?
  Chapter 3. Keras and TensorFlow: A brief history
  Chapter 3. Python and R interfaces: A brief history
  Chapter 3. Setting up a deep learning workspace
  Chapter 3. First steps with TensorFlow
  Chapter 3. Tensor attributes
  Chapter 3. Anatomy of a neural network : Understanding core Keras APIs
  Chapter 3. Summary
• Chapter 4. Getting started with neural networks: Classification and regression
  Chapter 4. Classifying newswires: A multiclass classification example
  Chapter 4. Predicting house prices: A regression example
  Chapter 4. Summary
• Chapter 5. Fundamentals of machine learning
  Chapter 5. Evaluating machine learning models
  Chapter 5. Improving model fit
  Chapter 5. Improving generalization
  Chapter 5. Summary
• Chapter 6. The universal workflow of machine learning
  Chapter 6. Develop a model
  Chapter 6. Deploy the model
  Chapter 6. Summary
• Chapter 7. Working with Keras: A deep dive
  Chapter 7. Different ways to build Keras models
  Chapter 7. Using built-in training and evaluation loops
  Chapter 7. Writing your own training and evaluation loops
  Chapter 7. Summary
• Chapter 8. Introduction to deep learning for computer vision
  Chapter 8. Training a convnet from scratch on a small dataset
  Chapter 8. Leveraging a pretrained model
  Chapter 8. Summary
• Chapter 9. Advanced deep learning for computer vision
  Chapter 9. An image segmentation example
  Chapter 9. Modern convnet architecture patterns
  Chapter 9. Interpreting what convnets learn
  Chapter 9. Summary
• Chapter 10. Deep learning for time series
  Chapter 10. A temperature-forecasting example
  Chapter 10. Understanding recurrent neural networks
  Chapter 10. Advanced use of recurrent neural networks
  Chapter 10. Summary
• Chapter 11. Deep learning for text
  Chapter 11. Preparing text data
  Chapter 11. Two approaches for representing groups of words: Sets and sequences
  Chapter 11. The Transformer architecture
  Chapter 11. Beyond text classification: Sequence-to-sequence learning
  Chapter 11. Summary
• Chapter 12. Generative deep learning
  Chapter 12. DeepDream
  Chapter 12. Neural style transfer
  Chapter 12. Generating images with variational autoencoders
  Chapter 12. Introduction to generative adversarial networks
  Chapter 12. Summary
• Chapter 13. Best practices for the real world
  Chapter 13. Scaling-up model training
  Chapter 13. Summary
• Chapter 14. Conclusions
  Chapter 14. The limitations of deep learning
  Chapter 14. Setting the course toward greater generality in AI
  Chapter 14. Implementing intelligence: The missing ingredients
  Chapter 14. The future of deep learning
  Chapter 14. Staying up-to-date in a fast-moving field
  Chapter 14. Final words

Deep Learning with R course images, Second Edition, Video Edition

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