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nkit
nkit
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I am familiar with few deep learning models and I understand how (little bit why) CNN/RNN works. But I still cannot make sense of new research papers. I want to dive deeper into field of deep learning specifically reinforcement learning. How should I approach learning mathematics as a self-learner?

My Background:

  • I'm familiar with high school mathematics (basic integration/differentiation/limits etc.).
  • I know just enough of deep learning to understand what different layers/optimization functions means.
  • I have participated in one kaggle competition Deepfake Detection Challenge and got 132th place on leaderboard.
  • I have fairly good grasp on programming.

What Do I want to achieve:

  • I want to be able to read research papers and understand what they're trying to convey with intuition.
  • Able to reason about new reinforcement learning algorithms.
  • Design new algorithms

Currently What I'm planning to do (in order):

  • calculus from CALCULUS WITH ANALYTIC GEOMETRY
  • Linear algebra book by Gilbert S.
  • Pattern Recognition and Machine Learning Book by Christopher Bishop
  • Probability (suggestion from comments)
  • Statistics (suggestion from comments)
  • Optimization (suggestion from comments)
  • -> All with help from various MIT OCW and youtube videos

I value every suggestion and I'm ready to devote however much time it takes. Also I understand that I'm aiming too high here so I'm open to any advice related.

I am familiar with few deep learning models and I understand how (little bit why) CNN/RNN works. But I still cannot make sense of new research papers. I want to dive deeper into field of deep learning specifically reinforcement learning. How should I approach learning mathematics as a self-learner?

My Background:

  • I'm familiar with high school mathematics (basic integration/differentiation/limits etc.).
  • I know just enough of deep learning to understand what different layers/optimization functions means.
  • I have participated in one kaggle competition Deepfake Detection Challenge and got 132th place on leaderboard.
  • I have fairly good grasp on programming.

What Do I want to achieve:

  • I want to be able to read research papers and understand what they're trying to convey with intuition.
  • Able to reason about new reinforcement learning algorithms.
  • Design new algorithms

Currently What I'm planning to do (in order):

  • calculus from CALCULUS WITH ANALYTIC GEOMETRY
  • Linear algebra book by Gilbert S.
  • Pattern Recognition and Machine Learning Book by Christopher Bishop
  • -> All with help from various MIT OCW and youtube videos

I value every suggestion and I'm ready to devote however much time it takes. Also I understand that I'm aiming too high here so I'm open to any advice related.

I am familiar with few deep learning models and I understand how (little bit why) CNN/RNN works. But I still cannot make sense of new research papers. I want to dive deeper into field of deep learning specifically reinforcement learning. How should I approach learning mathematics as a self-learner?

My Background:

  • I'm familiar with high school mathematics (basic integration/differentiation/limits etc.).
  • I know just enough of deep learning to understand what different layers/optimization functions means.
  • I have participated in one kaggle competition Deepfake Detection Challenge and got 132th place on leaderboard.
  • I have fairly good grasp on programming.

What Do I want to achieve:

  • I want to be able to read research papers and understand what they're trying to convey with intuition.
  • Able to reason about new reinforcement learning algorithms.
  • Design new algorithms

Currently What I'm planning to do (in order):

  • calculus from CALCULUS WITH ANALYTIC GEOMETRY
  • Linear algebra book by Gilbert S.
  • Pattern Recognition and Machine Learning Book by Christopher Bishop
  • Probability (suggestion from comments)
  • Statistics (suggestion from comments)
  • Optimization (suggestion from comments)
  • -> All with help from various MIT OCW and youtube videos

I value every suggestion and I'm ready to devote however much time it takes. Also I understand that I'm aiming too high here so I'm open to any advice related.

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Source Link
nkit
nkit
  • 11
  • 2

What mathematical background do I need to do research in field of deep learning

I am familiar with few deep learning models and I understand how (little bit why) CNN/RNN works. But I still cannot make sense of new research papers. I want to dive deeper into field of deep learning specifically reinforcement learning. How should I approach learning mathematics as a self-learner?

My Background:

  • I'm familiar with high school mathematics (basic integration/differentiation/limits etc.).
  • I know just enough of deep learning to understand what different layers/optimization functions means.
  • I have participated in one kaggle competition Deepfake Detection Challenge and got 132th place on leaderboard.
  • I have fairly good grasp on programming.

What Do I want to achieve:

  • I want to be able to read research papers and understand what they're trying to convey with intuition.
  • Able to reason about new reinforcement learning algorithms.
  • Design new algorithms

Currently What I'm planning to do (in order):

  • calculus from CALCULUS WITH ANALYTIC GEOMETRY
  • Linear algebra book by Gilbert S.
  • Pattern Recognition and Machine Learning Book by Christopher Bishop
  • -> All with help from various MIT OCW and youtube videos

I value every suggestion and I'm ready to devote however much time it takes. Also I understand that I'm aiming too high here so I'm open to any advice related.