Eugeny Shtoltc - Machine learning in practice – from PyTorch model to Kubeflow in the cloud for BigData

The book is structured like a textbook – from simple to complex. The reader will be able to:

* in the first three chapters, create the simplest neural network for image recognition and classification,

* in the following – to delve into the device and architecture for optimization,

* further expand the understanding of the company's ecosystem as a whole, in which neural networks operate, as its integral part,

and she interacts with and uses surrounding technologies,

* finish the study by deploying a full-scale production system in the full-cycle cloud.

Almost every chapter begins with the general information needed for the practical part that follows. In the practical part:

* demonstrates the process of preparing the environment, but more often free ready-made cloud services are used,

* demonstrates the writing process when with a parsing of the written and an overview of alternative solutions,

* analysis of the result and the formation of a vision of options for further development.

The book consists of sections:

* Introduction to Machine Learning. This is the only chapter without a practical part to get you started.

understanding the limits of their applicability, advantages over other methods and their general structure for beginners. Also produced

classification of neural networks according to the principles laid down in them, and the selection of a group, which will be discussed in the book.

* Basics for writing networks. It provides the basic knowledge necessary to write the first network in PyTorch, familiarity with the development environment

Jupyter in the Google Colab cloud service, which is a simplified version of the Google ML cloud platform, running the code in it and using the PyTorch framework for writing neural networks.

* We create the first network. The author demonstrates for the reader's practice how to create a simple neural network on PyTorch in

Colab with a detailed analysis of the written code, training it on the MNIST image dataset and launch it.

* Improving the recognition of the neural network on complex images. Here the author demonstrates to the reader not practice

training and prediction of neural networks for color pictures, methods to improve the quality of network predictions. Understands in detail

device, pitfalls in writing and training effective neural networks.

* Modern architectures of neural networks. The architectural principles used in modern neural networks for

improving the quality of predictions. An analysis of various neural network architectures that have made a breakthrough in the quality of training is given.

and brought approaches. Various architectural universal quality enhancement patterns such as ensemble are discussed.

* Using pre-trained networks. The use of already trained layers in their networks is demonstrated.

* ML scaling. Examples of preparing the environment for launching them in a cloud infrastructure are given.

* Receiving data from BigData. It tells how you can connect to various sources from Jupyter

data, including BigData, for training models.

* Big data preparation. This section describes BigData technologies such as Hadoop and Spark, which

are data sources for training models.

* ML in an industrial environment. This section covers systems such as Kubeflow and MLflow. The reader can try to expand

platform, set up the learning process and run the model in the cloud, as is done in companies.

Artificial intelligence is a field at the intersection of many sciences. One of the ways to achieve it is machine learning, when we supply it with data and, on its basis, we learn how to find solutions and identify patterns and data that were not there before. For training, statistical algorithms can be used, for example, in the R language, depth-first search in Prolog or breadth-first search in Refal, as well as adaptive structures – neural networks. Neural networks, depending on the tasks, will hide according to different principles, have a structure and learn in different ways. Recently, the greatest breakthrough has been received by neural networks of data representation (Representation learning), which are engaged in identifying patterns in information, since they cannot remember the information itself due to its size. Deep neural networks with many levels of features give great effects, features at subsequent levels are built on the basis of features from previous levels, which will be discussed in this chapter.