Hands-On Keras for Machine Learning Engineers [Video]
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Free ChapterIntroduction
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Foundations
- Theano
- TensorFlow
- Artificial Neural Network Anatomy
- Deep Learning
- Keras Life Cycle
- Keras Code Anatomy
- Demo: Case Study on Pima Indian Diabetes Dataset: Load Data
- Demo: Case Study on Pima Indian Diabetes Dataset: Define and Compile
- Demo: Case Study on Pima Indian Diabetes Dataset: Fit and Evaluate
- Performance Evaluation on Neural Networks
- Demo: Case Study on Data Segmentation
- Scikit-Learn for General Machine Learning
- Evaluate Models with Cross-Validation
- Grid Search Deep Learning Model Parameters
- Demo: Case Study on Multiclass Classification
- Demo: Case Study on Multiclass Classification: Part 2
- Demo: Case Study on Binary Classification
- Demo: Case Study on Binary Classification: Part 2
- Demo: Case Study on Binary Classification: Part 3
- Demo: Case Study on Binary Classification: Part 4
- Demo: Case Study on Regression
- Demo: Case Study on Regression: Part 2
- Demo: Case Study on Regression: Part 3
-
Going Deeper with Keras
- Model Serialization
- Save Neural Network to JSON
- Save Neural Network to YAML
- Demo: Case Study on Checkpointing
- Demo: Case Study on Checkpointing: Part 2
- Plotting History
- Visualize Model Training History in Keras
- Demo: Case Study on Dropping Out
- Demo: Case Study on Dropping Out: Part 2
- Dropout Tips
- Learning Rate Defined
- Configure Learning Rate
- Demo: Case Study on Learning Rates
- Demo: Case Study on Learning Rates: Part 2
- Demo: Case Study on Learning Rates: Part 3
-
Convolutional Neural Networks
- Convolutional Neural Networks
- Demo: Case Study on Handwritten Digit Recognition
- Demo: Case Study Handwritten Digit Recognition: Part 2
- Demo: Case Study on Handwritten Digit Recognition: Part 3
- Demo: Case Study on Handwritten Digit Recognition: Part 4
- Image Augmentation
- Demo: Case Study on Image Augmentation
- Demo: Case Study on Image Augmentation: Part 2
- Image Augmentation Tips
- Object Recognition
- Demo: Case Study on Object Recognition
- Improving Model Performance
- Sentiment Analysis in Keras
- IMDB Dataset Properties
- Word Embedding Defined
- Demo: Case Study on Word Embedding
- Demo: Case Study on Word Embedding: Part 2
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Recurrent Neural Networks
- Recurrent Neural Networks
- Demo: Case Study on Time Series Prediction
- Demo: Case Study on Time Series Prediction: Part 2
- Demo: Case Study on Time Series Prediction: Part 3
- Demo: Case Study on Time Series Prediction with LSTM
- Demo: Case Study on Time Series Prediction with LSTM: Part 2
- Demo: Case Study on Time Series Prediction with LSTM: Part 3
- Demo: Case Study on Sequence Classification
- Demo: Case Study on Sequence Classification: Part 2
About this
video
Welcome to hands-on Keras for machine learning engineers. This is a carefully structured course to guide you in your journey to learn deep learning in Python with Keras. Discover the Keras Python library for deep learning and learn the process of developing and evaluating deep learning models using it.
There are two top numerical platforms for developing deep learning models; they are Theano, developed by the University of Montreal, and TensorFlow developed at Google. Both were developed for use in Python and both can be leveraged by the super-simple-to-use Keras library. Keras wraps the numerical computing complexity of Theano and TensorFlow, providing a concise API that we will use to develop our own neural network and deep learning models. Keras has become the gold standard in the applied space for rapid prototyping deep learning models.
This course is a hands-on guide. It is a playbook and a workbook intended for you to learn by doing and then apply your new understanding to your own deep learning Keras models.
All resources and code files for this course are placed here: https://github.com/PacktPublishing/Hands-On-Keras-for-Machine-Learning-Engineers
- Publication date:
- November 2021
- Publisher
- Packt
- Duration
- 2 hours 17 minutes
- ISBN
- 9781803232522