دانلود فیلم آموزش یادگیری عمیق در شبکه های عصبی

Requirements
Install Python, Numpy, Scipy, Matplotlib, Scikit Lea, Theano, and TensorFlow
Lea about backpropagation from Deep Leaing in Python part 1
Lea about Theano and TensorFlow implementations of Neural Networks from Deep Leaing part 2
Description
This is the 3rd part in my Data Science and Machine Leaing series on Deep Leaing in Python. At this point, you already know a lot about neural networks and deep leaing, including not just the basics like backpropagation, but how to improve it using mode techniques like momentum and adaptive leaing rates. You’ve already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU.

This course is all about how to use deep leaing for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.

In this course we are going to up the ante and look at the StreetView House Number (SVHN) dataset – which uses larger color images at various angles – so things are going to get tougher both computationally and in terms of the difficulty of the classification task. But we will show that convolutional neural networks, or CNNs, are capable of handling the challenge!

Because convolution is such a central part of this type of neural network, we are going to go in-depth on this topic. It has more applications than you might imagine, such as modeling artificial organs like the pancreas and the heart. I’m going to show you how to build convolutional filters that can be applied to audio, like the echo effect, and I’m going to show you how to build filters for image effects, like the Gaussian blur and edge detection.

We will also do some biology and talk about how convolutional neural networks have been inspired by the animal visual cortex.

After describing the architecture of a convolutional neural network, we will jump straight into code, and I will show you how to extend the deep neural networks we built last time (in part 2) with just a few new functions to tu them into CNNs. We will then test their performance and show how convolutional neural networks written in both Theano and TensorFlow can outperform the accuracy of a plain neural network on the StreetView House Number dataset.

All the materials for this course are FREE. You can download and install Python, Numpy, Scipy, Theano, and TensorFlow with simple commands shown in previous courses.

This course focuses on “how to build and understand”, not just “how to use”. Anyone can lea to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model inteally. If you want more than just a superficial look at machine leaing models, this course is for you.

NOTES:

All the code for this course can be downloaded from my github: /lazyprogrammer/machine_leaing_examples

In the directory: cnn_class

Make sure you always “git pull” so you have the latest version!

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

calculus
linear algebra
probability
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: matrix and vector operations, loading a CSV file
Can write a feedforward neural network in Theano and TensorFlow

TIPS (for getting through the course):

Watch it at 2x.
Take handwritten notes. This will drastically increase your ability to retain the information.
Write down the equations. If you don’t, I guarantee it will just look like gibberish.
Ask lots of questions on the discussion board. The more the better!
Realize that most exercises will take you days or weeks to complete.
Write code yourself, don’t just sit there and look at my code.

USEFUL COURSE ORDERING:

(The Numpy Stack in Python)
Linear Regression in Python
Logistic Regression in Python
(Supervised Machine Leaing in Python)
(Bayesian Machine Leaing in Python: A/B Testing)
Deep Leaing in Python
Practical Deep Leaing in Theano and TensorFlow
(Supervised Machine Leaing in Python 2: Ensemble Methods)
Convolutional Neural Networks in Python
(Easy NLP)
(Cluster Analysis and Unsupervised Machine Leaing)
Unsupervised Deep Leaing
(Hidden Markov Models)
Recurrent Neural Networks in Python
Artificial Intelligence: Reinforcement Leaing in Python
Natural Language Processing with Deep Leaing in Python

Who is the target audience?
Students and professional computer scientists
Software engineers
Data scientists who work on computer vision tasks
Those who want to apply deep leaing to images
Those who want to expand their knowledge of deep leaing past vanilla deep networks
People who don’t know what backpropagation is or how it works should not take this course, but instead, take parts 1 and 2.
People who are not comfortable with Theano and TensorFlow basics should take part 2 before taking this course.
Courses :
Outline and Review
Convolution
Convolutional Neural Network Description
Convolutional Neural Network in Theano
Convolutional Neural Network in TensorFlow
Practical Tips
Project: Facial Expression Recognition
Appendix

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