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Neural networks a classroom approach by satish kumar pdf free 208- Using the power of neural networks, this book aims to make artificial intelligence accessible and understandable to students who are not familiar with the complicated mathematical formulas. The breadth of topics covered in the book includes identifying neuron activity, pattern recognition, dimensionality reduction, neural networks psychopathology and how it can be utilized as a tool for modeling psychiatric disorders. The skills and concepts touched on during the course will enable readers to experiment and build their own AI systems without any programming experience. This book is rigorous yet realistic in its approach towards educating readers on AI topics. Disclaimer: Pdf file is free for personal use only. The digital file of this book is licensed, not sold. We do not charge anything for the service of providing you with pdf files. But still, we hope that you will buy a paper copy of this book and every one else to help the spread of knowledge and awareness. Neural networks a classroom approach by satish kumar pdf free 208- The book covers concepts such as applying neural networks to classification problems such as diagnosis, market analysis and prediction as well as to regression tasks like forecasting. It also details recurrent neural networks (RNN) and their use in speech recognition. The book is coherent and well organized. I like the fact that not only the theory is spelt out but also how it can be applied to real problems. It gives both novice and expert readers a solid grounding in all aspects of neural networks and future applications using them. Read the complete review... The work is structured in three parts: The book has received positive reviews: The teaching style used did not hinder my understanding and made learning easier than other books on this topic, which include entire chapters on specific topics such as backpropagation, whilst here we get through just over half of the book before we begin to look at these topics. The author has a friendly and easy-going style, and I never felt like he was lecturing to me. The depth of the topics covered is great. There's enough information given for a beginner like me to understand what's going on, and enough technical detail for the more experienced machine learning practitioner to really understand how it works. It's really well-written and organized, and there are lots of illustrations, diagrams, and practical examples that help you understand what you're reading about. The only problem is that this book is quite expensive as I would expect it to be published by a big publisher such as "Prentice Hall" or "Pearson Education". I would have given it 5 stars otherwise. The writing style is very clear and concisely explains the concepts. A lot of examples are used which make the concepts more relatable. The book starts off by taking you through all the basics of neural network training techniques. The book also covers neural network architectures. The content of the book is good, but there are a few things that could be improved: 1) The glossary is not comprehensive enough, there are many terms that are not defined in the glossary. 2) Some chapters are too short with only one example or two, which makes understanding difficult for new readers, especially for mathematics students who already have a good background knowledge on the topic. cfa1e77820
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