Who is that This Book For? > 자유게시판

Have you ever not too long ago completed a machine studying or deep studying course and puzzled what to study next? With 30 questions and answers on key concepts in machine learning and AI, this ebook gives bite-sized bits of knowledge in your journey to turning into a machine learning professional. Who Is that this Book For? Machine Learning Q and AI is for people who are already familiar with machine learning and need to learn one thing new. However, this is not a math or coding guide. You won’t need to solve any proofs or run any code while studying. In other words, this ebook is an ideal travel companion or one thing you may learn in your favourite reading chair together with your morning coffee. Paperback: 770 pages Packt Publishing Ltd. Initially, this project started as the 4th edition of Python Machine Learning. However, after placing so much passion and onerous work into the changes and new topics, we thought it deserved a brand new title. Th is a​rticle has  been g en​erat᠎ed by GSA Conte nt Gen​erat​or  DEMO !

So, what’s new? There are many contents and additions, including the switch from TensorFlow to PyTorch, new chapters on graph neural networks and transformers, a brand new part on gradient boosting, and plenty of more that I'll detail in a separate weblog submit. For these who're all for understanding what this e book covers basically, I’d describe it as a complete resource on the basic ideas of machine studying and deep studying. The first half of the e book introduces readers to machine learning utilizing scikit-study, the defacto method for working with tabular datasets. Then, the second half of this guide focuses on deep studying, including functions to natural language processing and computer imaginative and prescient. While basic information of Python is required, this guide will take readers on a journey from understanding machine learning from the bottom up in the direction of coaching advanced deep studying models by the top of the ebook. "I’m assured that you'll discover this book invaluable both as a broad overview of the exciting subject of machine learning and as a treasure of practical insights.

"This 700-page book covers most of today’s broadly used machine studying algorithms, and will probably be especially helpful to anybody who needs to grasp trendy machine learning by means of examples of working code. It covers a variety of approaches, from primary algorithms such as logistic regression to very latest matters in deep learning akin to BERT and Kindle GPT language models and generative adversarial networks. The e book gives examples of practically each algorithm it discusses in the handy form of downloadable Jupyter notebooks that provide both code and entry to datasets. Many readers have instructed us how a lot they love the first 12 chapters of the e-book as a complete introduction to machine learning and Python’s scientific computing stack. To maintain these chapters related and to improve the reasons primarily based on reader feedback, we up to date them to help the latest versions of NumPy, SciPy, and scikit-study. One of the most thrilling occasions in the deep learning world was the discharge of TensorFlow 2. Consequently, all of the TensorFlow-associated deep learning chapters have received a big overhaul.

Since TensorFlow 2 introduced many new options and elementary changes, we rewrote these chapters from scratch. Furthermore, we added a new chapter on Generative Adversarial Networks, which are certainly one of the most well liked topics in deep learning research, in addition to a comprehensive introduction to reinforcement learning primarily based on quite a few requests from readers. Machine learning is consuming the software program world, and now deep learning is extending machine studying. This second edition of Sebastian Raschka’s bestselling book, www.solitaryisles.com Python Machine Learning, is now totally updated using the latest Python open source libraries, so as to understand and work on the slicing-edge of machine studying, neural networks, and deep learning. This highly acclaimed ebook has been modernized to incorporate the favored TensorFlow deep learning library, important coverage of the Keras neural community library, and the most recent scikit-learn machine learning library updates. The result is a brand new edition of this classic ebook on the cutting edge of deep learning and machine learning.

 This a rticle has been g enerat᠎ed  with t he ​he᠎lp　 of　GSA　C​ontent  Generator DE MO .