كتاب تعلم الذكاء الاصطناعي باستخدام بايثون Machine Learning and Deep Learning with Python

كتاب تعلم الذكاء الاصطناعي باستخدام بايثون Machine Learning and Deep Learning with Python ، استكمالا لسلسلة تعلم بايثون للمبتدئين Learn Python نقدم لكم في هذه المقالة كتاب تعلم الذكاء الاصطناعي باستخدام بايثون Machine Learning and Deep Learning with Python ، من تأليف Sebastian Raschka and Vahid Mirjalili.

Introduction book Machine Learning and Deep Learning with Python

In this age of modern technology, there is one resource that we have in abundance: a large amount of structured and unstructured data. In the second half of the twentieth century, machine learning evolved as a subfield of Artificial Intelligence (AI) that involved self-learning algorithms that derived knowledge from data in order to make predictions. Instead of requiring humans to manually derive rules and build models from analyzing large amounts of data, machine learning offers a more efficient alternative for capturing the knowledge in data to gradually improve the performance of predictive models and make data-driven decisions. Not only is machine learning becoming increasingly important in computer science research, but it also plays an ever greater role in our everyday lives. Thanks to machine learning, we enjoy robust email spam filters, convenient text and voice recognition software, reliable web search engines, challenging chess-playing programs, and, hopefully soon, safe and efficient self-driving cars.

Python is one of the most popular programming languages for data science and therefore enjoys a large number of useful add-on libraries developed by its great developer and and open-source community.

Although the performance of interpreted languages, such as Python, for computation-intensive tasks is inferior to lower-level programming languages, extension libraries such as NumPy and SciPy have been developed that build upon lower-layer Fortran and C implementations for fast and vectorized operations on multidimensional arrays. 

For machine learning programming tasks, we will mostly refer to the scikit-learn library, which is currently one of the most popular and accessible open source machine learning libraries.

Parallelizing Neural Network Training with TensorFlow, we trained a multilayer perceptron to classify MNIST digits, using various aspects of the TensorFlow Python API. That was a great way to dive us straight into some handson experience with TensorFlow neural network training and machine learning.

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