Neural Networks and Deep Learning
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks.
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- http://neuralnetworksanddeeplearning.com/
Coded everything from scratch, first in elixir, then rewritten some parts in C.
- For the basics read Micheal's Neural Nets & Deep learning - http://neuralnetworksanddeeplearning.com/
The Watch the Caltech telecourse - https://work.caltech.edu/telecourse
Read tutorials on Pytorch, Tensorflow & Keras.
Read, source codes on hugging face and deploys, test, train toy models.
Test your skills by participating in Data scientist competitions like Kaggle or Numerai.
It will give you a great way of guaging your competence with other data scientists.
- Neural Networks and Deep Learning, a free online book. http://neuralnetworksanddeeplearning.com/
- An online book by Michael Nielsen : http://neuralnetworksanddeeplearning.com/
It starts from basics and builds a CNN to recognize handwritten digits. I found it very helpful during my start of the journey.
- Machine learning is an enormous field, and if you are after a explanation/exploration from the ground up, then Kevin Murphy's Probabilistic Machine Learning: An Introduction is very good, but a bit of a tome.
If instead, you want to focus on neural networks, I found Michael Nielsen's Neural Networks and Deep Learning an excellent resource for implementing them from first principles (available at http://neuralnetworksanddeeplearning.com/).
- Michael Nielson's book/site on Neural Networks helped me really grasp the topic. http://neuralnetworksanddeeplearning.com/
H.L. Royden's Analysis was a book that made real analysis click when I read it on my own time in High School. It's simple enough so an intelligent kid can read it and think that simplicity can allow a solid understanding that some people can miss reading more sophisticated book.
It's also true that being forced to really work at a problem, whether by a class or a job, can also be invaluable way for concepts to click. Facing a problem that doesn't budge with your habitual approach forces you to try backing up and looking from a wider perspective as well as working at the problem with greater exactness and attention.
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