دانلود کتاب یادگیری ماشین عملی با PyTorch و Scikit-Learn

In 2006, Geoffrey Hinton et al. published a paper showing how to train a deep neural network capable of recognizing handwritten digits with state-of-the-art precision (>98%). They branded this technique “deep learning”. A deep neural network is a (very) simplified model of our cerebral cortex, composed of a stack of layers of artificial neurons. Training a deep neural net was widely considered impossible at the time, and most researchers had abandoned the idea in the late 1990s. This paper revived the interest of the scientific community, and before long many new papers demonstrated that deep learning was not only possible, but capable of mind-blowing achievements that no other machine learning (ML) technique could hope to match (with the help of tremendous computing power and great amounts of data). This enthusiasm soon extended to many other areas of machine learning. A decade later, machine learning had already conquered many industries, ranking web results, recommending videos to watch and products to buy, sorting items on production lines, sometimes even driving cars. Machine learning often made the headlines, for example when DeepMind’s AlphaFold machine learning system solved a long-standing protein-folding problem that had stomped researchers for decades. But most of the time, machine learning was just working discretely in the background. However, another decade later came the rise of A.I. assistants: from ChatGPT in 2022, Gemini, Claude, and Grok in 2023, and many others since then. A.I. has now truly taken off and it is rapidly transforming every single industry: what used to be Sci-Fi is now very real.