Best products from r/MLQuestions
We found 16 comments on r/MLQuestions discussing the most recommended products. We ran sentiment analysis on each of these comments to determine how redditors feel about different products. We found 12 products and ranked them based on the amount of positive reactions they received. Here are the top 20.
1. Pattern Recognition and Machine Learning (Information Science and Statistics)
- Springer
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2. Data Mining: Practical Machine Learning Tools and Techniques (The Morgan Kaufmann Series in Data Management Systems)
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3. Beyond Bullet Points: Using PowerPoint to tell a compelling story that gets results (4th Edition)
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4. Introduction to Stochastic Control Theory (Dover Books on Electrical Engineering)
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5. MSI VR Ready GE62VR Apache Pro-026 15.6" Powerful Gaming Laptop Geforce GTX 1060 i7-6700HQ 12GB 128GB M.2 SATA + 1TB Windows 10
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7. Guerrilla Analytics: A Practical Approach to Working with Data
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8. Foundations for Architecting Data Solutions: Managing Successful Data Projects
9. Deep Learning with Python
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10. Machine Learning and Security: Protecting Systems with Data and Algorithms
11. EVGA GeForce GTX 1080 Ti Founders Edition Gaming, 11GB GDDR5X, LED, DX12 OSD Support (PXOC) Graphic Cards 11G-P4-6390-KR
Real Base Clock: 1480 MHz / Real Boost Clock: 1582 MHz; Memory Detail: 11264 MB GDDR5XThe EVGA GeForce GTX 1080 Ti is the latest addition to the ultimate gaming platform, this card is packed with extreme horsepower, next-gen 11 Gbps GDDR5X memory, and a massive 11 GB frame buffer.What you see is wh...
I used Weka a lot when I was first starting out, and I can confidently recommend it. Data Mining: Practical Machine Learning Tools and Techniques is essentially a companion volume to Weka and its documentation, and it provides a great introduction to machine learning methodology in general; I recommend it, too. For user friendliness and visualization, I think it's a very good place to start.
Over time, I moved to R, which has the advantage of being more likely to incorporate new, cutting-edge methods that people have coded and released in packages. (There are also other R-based ML suites, such as Rattle.) If you like Weka, the transition into R can be pretty smooth, since R and Weka can talk to each other through R's Java interface. R is also good for applying command-line options (which can also be done in Weka's console), which you will eventually want to do as you get more familiar with your techniques of choice, whether they're found in Weka or not.
Python is a popular option for a lot of users (and with it you can use, among other things, Google's open-source TensorFlow suite), and it has the advantage of generally having pretty easy-to-read code, good visualization options, and a huge and very dedicated user base.
Good luck!
EDIT: I remembered a good book, "Beyond Bullet Points" by Cliff Atkinson that might have some good tips or ideas for you.
A good textbook will do you wonders. Get one that is fairly general and includes exercises. Do the exercises. This will be hard, but it'll make you learn an enormous amount faster.
My personal favourite book is Christopher Bishop's Pattern Recognition and Machine Learning. It's very comprehensive, has a decent amount of maths as well as good examples and illustrations. The exercises are difficult and numerous.
That being said, it is entirely Machine Learning. You mention wanting to learn about 'AI' so potentially you may want to look at a different book for some grounding in the wider more classical field of AI than just Machine Learning. For this I'd recommend Russel and Norvig's [AI: A Modern Approach](https://smile.amazon.co.uk/Artificial- Intelligence-Modern-Approach-Global/dp/1292153962). It has a good intro which you can use to understand the structure and history of the field more generally, and following on from that has a load of content in various areas such as search, logic, planning, probabilistic reasoning, Machine Learning, natural language processing, etc. It also has exercises, but I've never done them so I can't comment much on them.
These two books, if you were to study them deeply would give you at least close to a graduate level of understanding. You may have to step back and drill down into mathematical foundations if you're serious about doing exercises in Bishop's book.
On top of this, there are many really good video series on youtube for times when you want to do more passive learning. I must say though, that this should not be where most of your attention rests.
Here are some of my favourite relevant playlists on YouTube, ordered in roughly difficulty / relevance. Loosely start at the top, but don't be afraid to jump around. Some are only very tenuously related, but in my opinion they all have some value.
Gilbert Strang - Linear Algebra
Gilbert Strang - Calculus Overview
Andrew Ng - Machine Learning (Gentle coursera version)
Mathematical Monk - Machine Learning
Mathematical Monk - Probability
Mathematical Monk - Information Theory
Andrew Ng - Machine Learning (Full Stanford Course)
Ali Ghodsi - Data Visualisation (Unsupervised Learning)
Nando de Freitas - Deep Learning
The late great David MacKay - Information Theory
Berkeley Deep Unsupervised Learning
Geoff Hinton - Neural Networks for ML
Stephen Boyd - Convex Optimisation
Frederic Schuller - Winter School on Gravity and Light
Frederic Schuller - Geometrical Anatomy of Theoretical Physics
Yaser Abu-Mostafa - Machine Learning (statistical learning)
Daniel Cremers - Multiple View Geometry
/r/Nader_Nazemi please do your homework. This question has been asked a dozen at least in the past years.
Likewise, a quick Google search will find you that he wrote on of the popular ML books
Stochastic control theory, though that's not necessarily AI/ML related. You may still find that a good research subject and include methods or components you could mix into your overarching model.
ex.
https://www.amazon.com/Introduction-Stochastic-Control-Electrical-Engineering/dp/0486445313
I can't give a personal recommendation for a specific, current model. There is no way around doing some research and looking at the models in person. We have a mix of gaming laptops where I work, for QA testing. Our QA lead ranks them from best to worst brand as Razer->Asus->MSI->Alienware, in terms of which he has to get serviced the most, with Asus and MSI being roughly comparable. The Razer laptops with discrete Nvidia chips are great, but big bucks though compared to the MSI and Asus. Something in your budget would be a unit like this: https://www.amazon.com/MSI-GE62VR-Apache-Pro-026-i7-6700HQ/dp/B01IS33QWY/ref=pd_lpo_147_lp_t_3?_encoding=UTF8&psc=1&refRID=YXMX7D915FEQB0KMJ7GQ
Worth noting, gaming laptops with discrete graphics tend to be much bulkier than those with intel graphics, and with relatively poor battery life. The Alienware M17x R5 I have next to me for testing is more of a portable computer than a laptop; I wouldn't want to bring it on a plane or to Starbucks like I could with a Macbook or Razer Ultrabook. There are paving stones lighter than this thing, and it's noisy too.
You have servers with 8+ GPUs. ML is not the only field that uses GPU in big server farms. The movie industry used them first, but for your budget you will only be able to buy the server rack anyway. Also your budget is very low for ML specifically so you wont be able to buy GPU specialized for ML (no Tensor Cores). Your only choice is a consumer PC really pre-built.
For reference you also have things like [this][2] and [this][3]
[3]: https://www.dell.com/en-us/work/shop/povw/poweredge-r740
[2]: https://www.dell.com/en-us/work/shop/productdetailstxn/poweredge-t640
https://www.amazon.com/Machine-Learning-Security-Protecting-Algorithms-ebook/dp/B079C7LKKY
Foundations for Architecting Data Solutions by Ted Malaska and Jonathan Seidman
This book is your new bible: Guerrilla Analytics: A Practical Approach to Working with Data
Deep Learning with Python https://www.amazon.com/dp/1617294438/ref=cm_sw_r_cp_apa_i_9YmTCbSDTWXB7
The price jumped up a ton from when you bought it.
https://www.amazon.com/Nvidia-GEFORCE-GTX-1080-Ti/dp/B06XH5ZCLP
https://www.amazon.com/EVGA-GeForce-Founders-Support-11G-P4-6390-KR/dp/B06XH2P8DD
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https://www.ebay.com/p/16026507463?iid=123919351872&rt=nc&thm=1000
The used ones are 500, but the pages literally say that they are almost out of stock. Even the new ones are 800.
Nvidia is a freaking monopoly that needs to get split up to bring back competition, lower prices and innovation. But that's an entirely different thread on its own.