4.7 Read 3 reviews
Overview
Data Science - Deep Learning and Neural Networks in Python from Udemy provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.
Students at Data Science - Deep Learning and Neural Networks in Python from Udemy will:
- 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
Programme Structure
Courses include:
- Preliminaries: From Neurons to Neural Networks
- Classifying more than 2 things at a time
- Training a neural network
- Practical Machine Learning
- TensorFlow, exercises, practice, and what to learn next
- Project: Facial Expression Recognition
- Backpropagation Supplementary Lectures
- Higher-Level Discussion
Key information
Duration
- Part-time
- 2 days
Start dates & application deadlines
You can apply for and start this programme anytime.
Language
English
Delivered
Online
Campus Location
- San Francisco, United States
Disciplines
Data Science & Big Data View 467 other Short Courses in Data Science & Big Data in United StatesWhat students do after studying
Join for free or log in to access our complete career info list.
Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
- Basic math (calculus derivatives, matrix arithmetic, probability)
- Install Numpy and Python
- 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
- Students interested in machine learning - you'll get all the tidbits you need to do well in a neural networks course
- Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks.
Tuition Fees
Tuition fees are shown in and the most likely applicable fee is shown based on your nationality.
-
International Applies to you
Applies to youNon-residents90 USD / full≈ 90 USD / full - Out-of-State90 USD / full≈ 90 USD / full
-
Domestic
Applies to youIn-State90 USD / full≈ 90 USD / full
Funding
Improve page content