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I'm a freshman undergraduate student (mentioning this so you may forgive my unfamiliarity) who is currently doing research using neural networks. I've coded a three-node neural network (that works) based on my professor's guidance. However, I'd like to pursue a career in AI and Data Science, and I'd like to teach myself more about these properly in-depth. Are there any books or resources that will teach me more about neural network structures, deep learning, etc. Are there any recommendations?

Note: I'm proficient in Java, Python, Bash, JavaScript, Matlab, and know a bit of C++.

If you want a good and solid start for deep learning, I would suugest to start with the appropriately named book "Deep Learning" by Ian Goodfellow et al. After that you'll have a good base that you can expend by the many different tutorials, articles and courses available online.

However, I would also add that before doing that, you should take some basic "machine learning" class (should be available at your University). Many people these days go straight to deep learning and implementing Neural networks because it is relatively easy, but than they lack the understanding to improve it or use it to its fullest potential.

I suggest starting with Google’s Crash Course on ML if you want to revisit the basics. I then suggest to follow fast.ai’s ML and DL lessons. For reading I suggest Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan.
Have a nice day!

As other suggested are very good resources. If you want in depth Knowledge, I would suggest course by Andrew Ng on coursera. It covers in depth knowledge of basics of ML and if you are confused about whether you begin with AI, ML or deep learning You could follow the blog link in my profile.I recently posted how to go with these technologies.

PS: I am not advertising here my blog. I am just helping. If you want to follow you may follow otherwise just go with Andrew Ng

I highly suggest you to read this great book: hands on machine learning with Scikit and Tensorflow. Neural networks are presented succinctly in chapters 9 and 10. There are a lot of examples for you to practice. To effectively understand the script of examples you should have background of Python programming.
Have a nice day!

Deep Learning with Python by François Chollet is a great, high-level introduction into deep learning by the author of Keras.

I have a Master's in Computer Science and my thesis was about time-series prediction using Neural Networks.

The book Hands on machine learning with Scikit and Tensorflow was extremely helpful from a practical point of view. It really lays things very clearly, without much theory and math. I strongly recommend it.

On the other hand, the book by Ian Goodfellow is also a must (kind of the bible of DL). There you'll find the theoretical explanations, also it will leave you much much more knowledgeable with regards to deep learning and the humble beginning of the field till now.

Another, as others have suggested, is of course, Deep Learning with Python by Chollet. I indulged reading this book. Indeed it was very well written, and again, it teaches you tricks and concepts that you hardly grasp from tutorials and courses online.

Furthermore, I see you are familiar with Matlab, so maybe you have taken some stats/probability classes, otherwise, all these will overwhelm you a bit.

There are many good websites for self-learning. One such example is https://machinelearningmastery.com/start-here/#deeplearning

These are especially helpful for practical aspects, maybe less so for theoretical background.