This step is concerned with the transformation of data into a usable form. The raw data retrieved in the first step is in most cases unusable by ML algorithms. Formally, data wrangling is the process of cleaning, transforming, and mapping data from one form to another for consumption in later stages of the project life cycle. This step includes missing data imputation, typecasting, handling duplicates and outliers, and so on. We will cover these steps in the context of use-case driven chapters for a better understanding.
Data processing and wrangling
by Tamoghna Ghosh, Raghav Bali, Dipanjan Sarkar
Hands-On Transfer Learning with Python
- Title Page
- Copyright and Credits
- Dedication
- Packt Upsell
- Foreword
- Contributors
- Preface
- Machine Learning Fundamentals
- Deep Learning Essentials
- What is deep learning?
- Deep learning frameworks
- Setting up a cloud-based deep learning environment with GPU support
- Setting up a robust, on-premise deep learning environment with GPU support
- Neural network basics
- A simple linear neuron
- Gradient-based optimization
- The Jacobian and Hessian matrices
- Chain rule of derivatives
- Stochastic Gradient Descent
- Non-linear neural units
- Learning a simple non-linear unit – logistic unit
- Loss functions
- Data representations
- Multilayered neural networks
- Backprop – training deep neural networks
- Challenges in neural network learning
- Initialization of model parameters
- Improvements of SGD
- Overfitting and underfitting in neural networks
- Hyperparameters of the neural network
- Summary
- Understanding Deep Learning Architectures
- Neural network architecture
- Various architectures
- Summary
- Transfer Learning Fundamentals
- Unleashing the Power of Transfer Learning
- Image Recognition and Classification
- Text Document Categorization
- Text categorization
- Word representations
- CNN document model
- Building a review sentiment classifier
- What has embedding changed most?
- Transfer learning – application to the IMDB dataset
- Training on the full IMDB dataset with Word2vec embeddings
- Creating document summaries with CNN model
- Multiclass classification with the CNN model
- Visualizing document embeddings
- Summary
- Audio Event Identification and Classification
- DeepDream
- Style Transfer
- Automated Image Caption Generator
- Understanding image captioning
- Formulating our objective
- Understanding the data
- Approach to automated image captioning
- Image feature extraction with transfer learning
- Building a vocabulary for our captions
- Building an image caption dataset generator
- Building our image language encoder-decoder deep learning model
- Training our image captioning deep learning model
- Evaluating our image captioning deep learning model
- Automated image captioning in action!
- Summary
- Image Colorization
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Data processing and wrangling
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