Reddit reviews Make Your Own Neural Network

Reading some books would be a good idea.

• Superintelligence: Paths, Dangers, Strategies by Nick Bostrom is a good introduction to some philosophy and general tech behind AI. It's really a lovely read.
  
  The following are textbooks:
  
  General AI
  
  
• Artificial Intelligence: A Modern Approach by Russell and Norvig is a conical book on AI. It has an AMAZING companion site ran by UC Berkeley
  
  
• Artifical Intelligence for Humans by Heaton has some overlap with Artificial Intelligence: A Modern Approach, but is also a good overview
  
  Machine Learning
  
  
• Deep Learning by Goodfellow, Bengio, and Courville is a great book on deep learning
  
  
• Introduction to the Math of Neural Networks by Heaton is a great book on the math behind neural networks
  
  
• Make Your Own Neural Network by Rashid is a good guide to making your own neural network
  
  
• Reinforcement Learning: An Introduction by Sutton and Barto
  
  Statistics for Machine Learning
  
  
• Machine Learning: A Probabilistic Perspective by Murphy
  
  
• The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman
  
  There are many other topics within AI which none of these books focus on, such as Natural Language Processing, Computer Vision, AI Alignment/Control/Ethics, and Philosophy of AI. libgen.io may be of great help to you.

To gain a good overview of AI, I recommend the book The Master Algorithm by Pedro Domingos. It's totally readable for a layperson.

Then, learn Python and become familiar with libraries and packages such as numpy, scipy, and scikit-learn. Perhaps you could start with Code Academy to get the basics of Python, but I feel like the best way to force yourself to really know useful stuff is through implementing some project with a goal.

Some other frameworks and tools are listed here. Spend a lot more time doing than reading, but reading can help you learn how to approach different tasks and problems. Norvig and Russell's AI textbook is a good resource to have on hand for this.

Some more resources include:

Make Your Own Neural Network book

OpenAI Gym

CS231N Course Notes

Udacity's Free Deep Learning Course

I started with this book where you code a neural net with 1 hidden layer

This looks like exciting stuff! I really want to understand all of it better. Does anyone have suggestions on courses surrounding the fundamentals? (I'm a full stack web dev, currently.)

Edit: After a bit of searching, I think I'll start here: https://smile.amazon.com/gp/product/B01EER4Z4G/ref=dbs_a_def_rwt_hsch_vapi_tkin_p1_i0

This book is a good place to start.

I'm very new to ML myself (so take this with a grain of salt) but I'd recommend checking out Make Your Own Neural Network, which guides you through the process of building a 2-layer net from scratch using Python and numpy.

That will help you build an intuition for how neural networks are structured, how the forward / backward passes work, etc.

Then, I'd probably recommend checking out Stanford's online course notes / assignments for CS231n. The assignments guide you through building a computation graph, which is a more flexible, powerful way of approaching neural network architectures (it's the same concept behind Tensorflow, Torch, etc.)

have you checked out the gym - OpenAI library? I explored a tiny bit with it during my software development class and by tiny I mean supervised learning for the Cartpole game

https://github.com/openai/gym
https://gym.openai.com/

there are some guides and videos explaining certain games in there that'll make learning and implementing learning algorithms fun. My introduction into Machine Learning was through Make Your Own Neural Network, its a great book with for learning about perceptrons, layers, acitvations and such; theres also a video.

You might be interested in this.

This free book supposedly contains most of what you need to get into machine learning (focus on deep learning). Also, this book seems like a nice introduction.

You might want to check this book out, it really breaks things down into manageable and understandable chunks. As the title implies, it's around neural networks. Machine Learning Mastery is also a website that does well at breaking things down - I'm pretty sure you've already come across it

Hey! I'm not OP but I think I can help. It's kind of difficult to summarize how machine learning (ML) works in just a few lines since it has a lot going on, but hopefully I can briefly summarize how it generally works (I've worked a bit with them, if you're interested in how to get into learning how to make one you can check out this book)

In a brief summary, a neural network takes a collection of data (like all the characteristics of a college application), inputs all its variables (like each part of the application like its AP scores, GPA, extraciriculars, etc.) into the input nodes and through some magic math shit, the neural network finds patterns through trial and error to output what you need, so that if you give it a new data set (like a new application) it can predict the chance that something is what you want it to be (that it can go to a certain college)

How it works is each variable that you put into the network is a number that is able to represent the data you're inputting. For example, maybe for one input node you put the average AP score, or the amount of AP scores that you got a 5 on, or your GPA, or somehow representing extraciriculars with a number. This is then multiplied in what are called weights (the Ws in this picture) and then is sent off into multiple other neurons to be added with the other variables and then normalized so the numbers don't get gigantic. You do this with each node in the first hidden layer, and then repeat the process again in how many node layers you have until you get your outputs. Now, this is hopefully where everything clicks:

Let's say the output node is just one number that represents the chance you get into the college. On the first go around, all the weights that are multiplied with the inputs at first are chosen at random (kinda, they're within a certain range so they're roughly where they need to be) and thus, your output at first is probably not close to the real chance that you'll get into the college. So this is the whole magic behind the neural network. You take how off your network's guess was compared to the real life % that you get accepted, and through something called back propagation (I can't explain how you get the math for it, it actually is way too much but here's an example of a formula used for it) you adjust the weights so that the data is closer when put in to the actual answer. When you do this thousands or millions of times your network gets closer and closer to guessing the reality of the situation, which allows you to put in new data so that you can get a good idea on what your chance is you get into college. Of course, even with literal millions of examples you'll never be 100% accurate because humans decisions are too variable to sum up in a mathematical sense, but you can get really close to what will probably happen, which is better than nothing at all :)

The beauty of ML is it's all automated once you set up the neural network and test that it works properly. It takes a buttload of data but you can sit and do what you want while it's all processing, which is really cool.

I don't think I explained this well. Sorry. I'd recommend the book I sent if you want to learn about it since it's a really exciting emerging field in computer science (and science in general) and it's really rewarding to learn and use. It goes step by step and explains it gradually so you feel really familiar with the concepts.

In addition to the 3blue1brown video someone else described this book is a great introduction to the algorithms, without going into much math (though you should go into the math to fully undestand what is going on).

Make Your Own Neural Network
https://www.amazon.com/dp/B01EER4Z4G/ref=cm_sw_em_r_mt_dp_U_NkqpDbM5J6QBG

tariq rasheed has a great book on ML and he breaks it down for total beginners. he breaks down the math as if your in elementry school. I think its called ML for beginners.

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Book link:

https://www.amazon.com/Make-Your-Own-Neural-Network-ebook/dp/B01EER4Z4G/ref=sr_1_1?crid=3H9PBLPVUWBQ4&keywords=tariq+rashid&qid=1565319943&s=gateway&sprefix=tariq+ra%2Caps%2C142&sr=8-1

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I got the kindle edition bc im broke. Its just as good as the actual book.