Data Science: Deep Learning in Python

Learn python and how to use it to analyze, visualize and present data! Udemy.Learn Anything.Anywhere. Advance your Career. Risk Free Learning. Learn on your Schedule. 30M+ Students Enrolled. Affordable Learning. 100,000+ Courses The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow

4.6 (4,874 ratings)

34,468 students enrolled

Created by Lazy Programmer Inc.

Last updated 10/2018

English

English [Auto-generated], Portuguese [Auto-generated], 1 more

Description

This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.

We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.

Next, we implement a neural network using Google's new TensorFlow library.

You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features.

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

Requirements

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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Data Science: Deep Learning in Python