Matt R. Cole - Deep Learning with C#, .Net and Kelp.Net
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C#, .NET and Kelp.NET
Deep Learning Guide
Matt R. Cole
20, Ansari Road, Darya Ganj
New Delhi-110002
Ph: 23254990/23254991 MICRO MEDIA
Shop No. 5, Mahendra Chambers,
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Ph: 23861747 Published by Manish Jain for BPB Publications, 20, Ansari Road, Darya Ganj, New Delhi-110002 and Printed by Repro India Pvt Ltd, Mumbai
In his spare time, he continues his education taking every available course in advanced Mathematics, AL/ML/DL, Quantum Mechanics/Physics, String Theory and Computational Neuroscience and contributes to open source efforts such as Kelp.Net.
This book is designed to give you all you need to accomplish this goal. You will find that Kelp.Net is an invaluable tool used to create powerful and expressive deep learning models. But I know that along the way, someone might ask you what is happening behind the scenes. This is why I have also chosen to include and deeply integrate ReflectInsight into this book, so that all the output goes into this power logging application. One of the hardest questions to answer is what your deep learning models are doing behind the scenes. This book makes it easy to answer this question.
Not only will you be able to easily create your own deep learning models, but you will also gain a better understanding about what is going on behind the scenes. This book is a must for every C# developer who wants to learn deep learning and is the penultimate reference guide for Kelp. Net.
I wouldnt have it any other way! And finally, a big thank you all the readers for purchasing this book and taking your first (or next) step down the road to deep learning. May your journey be a good one!
bundle and colored images:
Code Bundle and the Colored Images of the book: https://rebrand.ly/50f4c
and ___ It
The chances are high that many of us have. How could it be that the chances of all of us picking the same two words without thinking about it are so very high? I am sure we can all recall hearing this phrase somewhere in the past (and maybe one or two of us said it), but is the pattern recognition in our brains so good that these two words immediately were the ones that came to mind when we saw a partial implementation of that sentence? And how could so many of us, seemingly unconnected from each other, arrive at the same two words and complete the sentence in the same way in probably close to the same amount of time? Did we just witness a marvelous example of unsupervised deep learning at its finest or was it something else? How can such advanced pattern recognition ability happen so fast and be so predictable among a huge population of people? Or maybe it was reinforcement learning that brought this to the mind (Lord knows I have received negative reinforcement for saying it!). Regardless of how, something marvelous surely has just happened. Let us think about this for a second, shall we? We have an area in our brains called the hippocampus and this area stores episodic memories (stuff that happened to you and me). Within the hippocampus, many neurons just fired when we saw that sentence, and the exciting neurons which they were connected to and so on, which made them fire until the pattern was complete. And all this happened in the blink of an eye.
But did any of us have to give any thought into having to complete that sentence, or did it seem to pop up from our subconscious mind with the greatest of ease? The truth is that we may never know, at least not fully, but theories are abounded. So how many of you work in the real world as developers? For those who do, you know more often than not we walk through the doors, put our firemans hat on, and off we go. How we long for some pure R & D time where we can work on our code, create better algorithms, enhance the UI we have been putting off for what seems like forever. Ah! To be like the academic world for even the smallest amount of time! But alas, we are in the real world! We have products to deliver and timelines to meet. Some of us work around an agile approach to development, some do not. Everyone seems to be doing the same things, albeit a little bit differently.
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