Data Science: Deep Learning and Neural Networks in Python

Data Science: Deep Learning and Neural Networks in Python

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Data Science: Deep Learning in Python

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

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