![]() ![]() Data preprocessing, feature engineering, regularisation techniques are elaborated. ![]() ![]() Chapter 4 is basically a primer to rewind the concepts of machine learning. K-Fold cross-validation is implemented on the Boston housing prices dataset. Finally, getting predictions on new data are demonstrated with three different data sets: IMDB reviews, Reuters, Boston housing price. In the third chapter, you would get to understand the deep learning framework: Keras, loading the datasets, preparing the data, building and compiling the model, configuring the optimizer, using loss function, metrics, training, validating the model and plotting training loss versus validation loss. Data representations, data batches, data formats, tensors, gradient descent algorithm, and back propagation algorithm are some of the important concepts of DL included in this chapter. Chapter 2 introduces basic architecture of neural network with MNIST image dataset. This chapter has thrown light on how the improvements in hardware, open source data and algorithmic advances speed up the deep learning for the past 2 decades. ![]() The history and evolution of machine learning and deep learning thoroughly discussed. I am elaborating some key contents of the book in the following 2 paragraphs:Ĭhapter 1 introduces the basic concepts of machine learning and deep learning. ![]()
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