Deep learning behind the scenes…
4 stars
While the book shows its age, it was a great introduction to the very basics of deep learning and ML engineering, and also taught me - who became a non-coder over the years - some interesting concepts of the Python programming language. As I dived into loss functions, gradient descents, learning rates, collaborative filtering, sentiment analysis, decision trees, recurrent and convolutional neural networks, ReLUs and whatnot, I'm super eager to learn more about the practical side now, such as building solutions upon foundation models and heading from ML engineering to AI engineering practices. Together with lots of free video lessons and great Kaggle notebooks to practice with (which I still have to do more of), this is pure gold if you are interested in how machine learning works "under the hood" (some prior knowledge in practical statistic is certainly helpful). Not surprisingly, as the cut-off date of this book's knowledge …
While the book shows its age, it was a great introduction to the very basics of deep learning and ML engineering, and also taught me - who became a non-coder over the years - some interesting concepts of the Python programming language. As I dived into loss functions, gradient descents, learning rates, collaborative filtering, sentiment analysis, decision trees, recurrent and convolutional neural networks, ReLUs and whatnot, I'm super eager to learn more about the practical side now, such as building solutions upon foundation models and heading from ML engineering to AI engineering practices. Together with lots of free video lessons and great Kaggle notebooks to practice with (which I still have to do more of), this is pure gold if you are interested in how machine learning works "under the hood" (some prior knowledge in practical statistic is certainly helpful). Not surprisingly, as the cut-off date of this book's knowledge is 2020, some newer concepts and insights are lacking in depth.