This book is an impeccable source for data scientists who aim to learn and apply AI in their developments. It covers basics of deep learning, DL tools as well as frameworks. In addition it traces to relevant topics of AI such as Big Data and so on. At the end, it gives a hint of what’s next after deep learning such as Transmission Learning and Capsule Networks. Irrespective of you are a novice or an experienced data scientists I trust you would advantage from this book. As an AI specialist and scientist, I would extremely suggest this book.
Artificial intelligence is the recreation of human intelligence procedures by machines, particularly computer systems. Particular applications of AI comprise skilled systems, natural language processing, and speech recognition as well as machine vision.
Relationship between AI and Data Science:
AI has now expanded and fully-fledged to outperform straight data science approaches in several applications. For this, we must thank the enlarged computing properties at our disposal, mainly computing power. This is something made conceivable due to the graphics processing units (GPUs) becoming inexpensive and at ease to add to a computer, as add-ons. What’s extra, cloud computing has become more typical, enabling more people to have contact to a virtual computer cluster, which they modify and rent, to run their AI projects. This styles AI systems simply accessible and profitable, while at the same time development testing and new use cases for this knowledge. All this collaboration between AI and data science has run to a lot of research interest in AI. Research centers, individual scholars, and the R&D departments of numerous large corporations have been examining new methods to make these AI algorithms more accessible and more vigorous. This obviously boosts the field’s impression on the world and makes it a more good-looking technology, not just for the researchers, but for anyone eager to tinker with it, comprising many industrialists in this field. So yes, data science could carry on to happen without AI. But in several cases, this wouldn’t make much wisdom. It is now clear that the world of data science has a lot of difficulties and boundaries that AI can help address. The edge of these two closely linked fields will only continue to grow as they both progress, so now is the impeccable time to jump into learning AI with both feet.
Topic you will go through in this book:
- This book offers an informal transition for someone with some understanding of the more famous features of AI. As such, we begin with an summary of the deep learning frameworks tracked by a short-lived explanation of the other AI frameworks, concentrating on optimization algorithms as well as unsure logic systems The aim of these first two chapters is to offer you with some frame of reference, before proceeding to the more practical and particular features.
- After that, we inspect the MXNet framework for deep learning as well as how it works on Python. The effort here is on the most elementary deep learning systems, namely feed forward neural networks. The two chapters that track inspect these deep learning systems using other general frameworks: Tensor flow as well as Keras. All of the deep learning chapters comprise some examples for practical practice on these systems.
- Next, we examine optimization algorithms, mainly the more progressive ones. Each chapter efforts on a specific framework of these algorithms, comprising particle swarm optimization, inherited algorithms, and simulated strengthening. We also think through applications of these algorithms, as well as how they can be of use in data science projects. The programming language we are using for these chapters is Julia, for performance details.
- In the final chapter of the book, we discuss about big data, data science specialties, and to help you rehearsal we offer some sources of public datasets. The book accomplishes with some words of advice along with resources for extra learning.