Data Science: Deep Learning in Python, The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow
Created by Lazy Programmer Inc.
What you'll learn
- Learn how Deep Learning REALLY works (not just some diagrams and magical black box code)
- Learn how a neural network is built from basic building blocks (the neuron)
- Code a neural network from scratch in Python and numpy
- Code a neural network using Google's TensorFlow
- Describe different types of neural networks and the different types of problems they are used for
- Derive the backpropagation rule from first principles
- Create a neural network with an output that has K > 2 classes using softmax
- Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward"
- Install TensorFlow
- How to take partial derivatives and log-likelihoods (ex. finding the maximum likelihood estimations for a die)
- Install Numpy and Python (approx. latest version of Numpy as of Jan 2016)
- Don't worry about installing TensorFlow, we will do that in the lectures.
- Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course
Preview This Course - GET COUPON CODE