Reduce IT Cost 101 Questions for Business and Technology Leaders to Save Millions in It Spending

Notion Press
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Are you a Business, Finance or IT leader looking for answers to the following questions? 

• Why is IT so expensive?
• How do we keep IT costs low?
• Are we overpaying IT vendors?
• Why do IT projects exceed their budget?
• Is IT governance unbiased and objective?
• Do we know if our IT Assets are fully utilized?
• Are we paying for software that we are not using?
• The IT team is always busy—are they doing relevant work?

Explained in simple, non-technical language, 101 IT Cost saving ideas presented in this book, are proven techniques that will help you ask the right questions from your IT team and discover hidden opportunities to reduce IT spending.
Be it spending on IT hardware, software, staffing or outsourced services, even if you get your IT teams to answer a few of the 101 questions, the savings opportunities will exceed your investment in this book.
Pick up one more copy. When more managers in your company read this book, the higher will be your savings.
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About the author

ABHINAV MITTAL, is an MBA graduate and university gold medallist, with over a decade of experience in driving IT Financial Management, IT Strategy and Governance for multi-billion dollar companies with global operations. He has worked directly with heads of business, CFOs and CIOs of large, professional and family-run enterprises. He has managed programs on IT transformation with business consultants from Mckinsey, Bain, and Ernst and Young. He is certified in COBIT, ITIL and 6 Sigma, and holds leadership certification from Harvard Business School (HBSP Division).

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Additional Information

Notion Press
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Published on
Sep 11, 2018
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Computers / Computer Science
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Content Protection
This content is DRM free.
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Available on Android devices
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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”
—Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.

Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

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