Review: Machine Learning with PyTorch and Scikit-Learn

Machine learning and deep learning are moving at such a fast pace that it is sometimes hard keeping up with just the names of new algorithms and architectures, let alone learn them. Take transformers, for instance. Four years ago, they were still an emerging field of research. Today, they are the primary type of neural network used in large language models and have replaced RNNs and even CNNs in many applications.

Generative models such as GANs and VAEs have undergone a similar trend. And in the past year, diffusion and CLIP neural networks have shown much promise.

With so much happening and changing in the field, writing an introductory book on machine learning and deep learning has become a real challenge. Yet Sebastian Raschka, Yuxi Liu, and Vahid Mirjalili have managed to pull a very difficult feat in their latest book, Machine Learning with PyTorch and Scikit-Learn.

The book, which is the PyTorch edition of the acclaimed Python Machine Learning, provides a balanced mix of theory, math, coding, and references to give you a broad overview of the ML and DL landscape and help you trace a roadmap for your future career in artificial intelligence.

Read the full review on TechTalks.

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