The Advanced Guide to Deep Learning and Artificial Intelligence Bundle

The Advanced Guide to Deep Learning and Artificial Intelligence Bundle

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14.5 Hours
Deal Price$32.00
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$480.00
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The Advanced Guide to Deep Learning and Artificial Intelligence Bundle
$32.00$480.0093% OFF
Deep Learning: Convolutional Neural Networks in Python

25 Lessons (3h)

  • Outline and Review
    Introduction and Outline1:50
    Review of Important Concepts3:42
    Where to get the data for this course3:12
    How to load the SVHN data and benchmark a vanilla deep network5:03
  • Convolution
    What is convolution?5:18
    Convolution example with audio: Echo6:39
    Convolution example with images: Gaussian Blur5:32
    Convolution example with images: Edge Detection3:21
    Write Convolution Yourself9:15
  • Convolutional Neural Network Description
    Architecture of a CNN5:08
    Relationship to Biology2:18
    Convolution and Pooling Gradients2:39
    LeNet - How the Shapes Go Together12:52
  • Convolutional Neural Network in Theano
    Theano - Building the CNN components4:19
    Theano - Full CNN and Test on SVHN17:26
    Visualizing the Learned Filters3:35
  • Convolutional Neural Network in TensorFlow
    TensorFlow - Building the CNN components3:39
    TensorFlow - Full CNN and Test on SVHN10:41
  • Practical Tips
    Practical Image Processing Tips3:07
  • Project: Facial Expression Recognition
    Facial Expression Recognition Problem Description12:21
    The class imbalance problem6:01
    Utilities walkthrough5:45
    Convolutional Net in Theano21:04
    Convolutional Net in TensorFlow19:03
  • Appendix
    How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow17:22
The Advanced Guide to Deep Learning and Artificial Intelligence Bundle
$32.00$480.0093% OFF
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DescriptionInstructorImportant DetailsRelated Products

This High-Intensity 14.5 Hour Bundle Will Help You Help Computers Address Some of Humanity's Biggest Problems

LP
Lazy ProgammerThe Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons to communicate with their family and caregivers.

He has worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. He has created new big data pipelines using Hadoop/Pig/MapReduce, and created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.

He has taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.

Multiple businesses have benefitted from his web programming expertise. He does all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies he has used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases he has used MySQL, Postgres, Redis, MongoDB, and more.

Description

In this course, intended to expand upon your knowledge of neural networks and deep learning, you'll harness these concepts for computer vision using convolutional neural networks. Going in-depth on the concept of convolution, you'll discover its wide range of applications, from generating image effects to modeling artificial organs.

  • Access 25 lectures & 3 hours of content 24/7
  • Explore the StreetView House Number (SVHN) dataset using convolutional neural networks (CNNs)
  • Build convolutional filters that can be applied to audio or imaging
  • Extend deep neural networks w/ just a few functions
  • Test CNNs written in both Theano & TensorFlow
Note: we strongly recommend taking The Deep Learning & Artificial Intelligence Introductory Bundle before this course.

Specs

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: advanced, but you must have some knowledge of calculus, linear algebra, probability, Python, Numpy, and be able to write a feedforward neural network in Theano and TensorFlow.
  • All code for this course is available for download here, in the directory nlp_class2

Terms

  • Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.
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