Wanna understand deep learning and neural networks so well, you could code them from scratch? In this course, we'll do exactly that.
The course starts by motivating and explaining perceptrons, and then gradually works its way toward deriving and coding a multiclass neural network with stochastic gradient descent that can recognize hand-written digits from the famous MNIST dataset.
Course Goals
This course is all about understanding the fundamentals of neural networks. So, it does not discuss TensorFlow, PyTorch, or any other neural network libraries. However, by the end of this course, you should understand neural networks so well that learning TensorFlow and PyTorch should be a breeze!
Challenges
In this course, I present a number of coding challenges inside the video lectures. The general approach is, we'll discuss an idea and the theory behind it, and then you're challenged to implement the idea / algorithm in Python. I'll discuss my solution to every challenge, and my code is readily available on github.
Prerequisites
In this course, we'll be using Python, NumPy, Pandas, and good bit of calculus. ..but don't let the math scare you. I explain everything in great detail with examples and visuals.
If you're rusty on your NumPy or Pandas, check out my free courses Python NumPy For Your Grandma and Python Pandas For Your Grandpa.
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Hi I’m Ben. I spent five years doing actuarial work for an insurance company before ditching the cubicle and opening up my freelance machine learning consultancy, GormAnalysis. Now I build predictive models for companies spanning a range of industries including insurance, ecommerce, tech, real estate, and others. I'm also a Kaggle Master, for whatever that's worth..
I'm also the creator of Practice Probs - a platform where you can find much of my content in written form.
