Practical Deep Learning in Theano and TensorFlow

Practical Deep Learning in Theano and TensorFlow

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3 Hours
Deal Price$29.00
Suggested Price
$120.00
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Practical Deep Learning in Theano and TensorFlow
$29.00$120.0075% OFF
Data Science: Practical Deep Learning in Theano & TensorFlow

23 Lessons (3h)

  • Outline, the MNIST dataset, and Linear (Logistic Regression) Benchmark
    Outline - what did you learn previously, and what will you learn in this course?2:35
    Where to get the MNIST dataset and Establishing a Linear Benchmark4:31
  • Gradient Descent: Full vs Batch vs Stochastic
    What are full, batch, and stochastic gradient descent?2:45
    Full vs Batch vs Stochastic Gradient Descent in code5:38
  • Momentum and adaptive learning rates
    Momentum1:56
    Code for training a neural network using momentum6:41
    Variable and adaptive learning rates3:13
    Constant learning rate vs. RMSProp in Code4:05
    Hyperparameter Optimization: Cross-validation, Grid Search, and Random Search3:19
  • Theano
    Theano Basics: Variables, Functions, Expressions, Optimization7:47
    Building a neural network in Theano9:17
  • TensorFlow
    TensorFlow Basics: Variables, Functions, Expressions, Optimization7:27
    Building a neural network in TensorFlow9:43
  • Modern Regularization Techniques
    Dropout Regularization11:38
  • GPU Speedup and Homework
    Setting up a GPU Instance on Amazon Web Services7:06
    Exercises and Concepts Still to be Covered2:13
  • Project: Facial Expression Recognition
    Facial Expression Recognition Problem Description12:21
    The class imbalance problem6:01
    Utilities walkthrough5:45
    Class-Based ANN in Theano19:09
    Class-Based ANN in TensorFlow15:28
  • Appendix
    Manually Choosing Learning Rate and Regularization Penalty4:08
    How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow17:22
Practical Deep Learning in Theano and TensorFlow
$29.00$120.0075% OFF
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DescriptionInstructorImportant DetailsRelated Products

Build & Understand Neural Networks Using Two of the Most Popular Deep Learning Techniques

LP
Lazy ProgrammerThe 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

The applications of Deep Learning are many, and constantly growing, just like the neural networks that it supports. In this course, you'll delve into advanced concepts of Deep Learning, starting with the basics of TensorFlow and Theano, understanding how to build neural networks with these popular tools. Using these tools, you'll learn how to build and understand a neural network, knowing exactly how to visualize what is happening within a model as it learns.

  • Access 23 lectures & 3 hours of programming 24/7
  • Discover batch & stochastic gradient descent, two techniques that allow you to train on a small sample of data at each iteration, greatly speeding up training time
  • Discuss how momentum can carry you through local minima
  • Learn adaptive learning rate techniques like AdaGrad & RMSprop
  • Explore dropout regularization & other modern neural network techniques
  • Understand the variables & expressions of TensorFlow & Theano
  • Set up a GPU-instance on AWS & compare the speed of CPU vs GPU for training a deep neural network
  • Look at the MNIST dataset & compare against known benchmarks
Like what you're learning? Try out the The Advanced Guide to Deep Learning and Artificial Intelligence next.

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: all levels, but you must have some knowledge of calculus, linear algebra, probability, Python, and Numpy
  • All code for this course is available for download here, in the directory ann_class2

Compatibility

  • Internet required

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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