deep learning notes下载

weixin_39820780 2019-10-06 05:30:27

这是吴恩达机器学习的课程笔记，关于机器学习部分的主要几个算法
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Deep Learning Toolbox Release Notes,Deep Learning Toolbox Release Notes,Deep Learning Toolbox Release Notes

Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. See these course notes for a brief introduction to Machine Learning for AI and an introduction to Deep Learning algorithms.

1. 概述类首先是概述类论文，先后有2013年的“Representation Learning: A Review and New Perspectives”和2015年的”Deep Learning in Neural Networks: An Overview”两篇。上传了较新的一篇。 3. 分布式计算分布式计算方面论文涉及到具体解决计算能力的问题。有2012年的两篇论文Building High-level Features Using Large Scale Unsupervised Learning和Large Scale Distributed Deep Networks，其中后篇较好，其中第一次提到GPU对深度学习计算进行提速，其描述的情形大致是如何对多个GPGPU并行计算的深度学习框架进行编程。故上传了此篇 4. 具体算法而后便是具体的算法方面的典型论文，包括K-means、单层非监督网络、卷积网络CNN、多级架构、Maxout和增强学习，论文列举如下： 2006年Notes on Convolutional Neural Networks 2009年What is the Best Multi-Stage Architecture for Object Recognition 2011年An Analysis of Single-Layer Networks in Unsupervised Feature Learning 2012年Learning Feature Representations with K-means 2012年Sparse Filtering (其中有RBM，auto-encoder等) 2014年Improving deep neural network acoustic models using generalized maxout networks 2014年Adolescent-specific patterns of behavior and neural activity during social reinforcement learning 2015年Reinforcement learning models and their neural correlates: An activation likelihood estimation meta-analysis和Human-level control through deep reinforcement learning

https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow/ Deep Learning: Convolutional Neural Networks in Python Computer Vision and Data Science and Machine Learning combined! In Theano and TensorFlow Created by Lazy Programmer Inc. Last updated 5/2017 English What Will I Learn? Understand convolution Understand how convolution can be applied to audio effects Understand how convolution can be applied to image effects Implement Gaussian blur and edge detection in code Implement a simple echo effect in code Understand how convolution helps image classification Understand and explain the architecture of a convolutional neural network (CNN) Implement a convolutional neural network in Theano Implement a convolutional neural network in TensorFlow Requirements Install Python, Numpy, Scipy, Matplotlib, Scikit Learn, Theano, and TensorFlow Learn about backpropagation from Deep Learning in Python part 1 Learn about Theano and TensorFlow implementations of Neural Networks from Deep Learning part 2 Description This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning 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 learning 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 turn 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 learn 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 internally. If you want more than just a superficial look at machine learning models, this course is for you. NOTES: All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_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 Learning in Python) (Bayesian Machine Learning in Python: A/B Testing) Deep Learning in Python Practical Deep Learning in Theano and TensorFlow (Supervised Machine Learning in Python 2: Ensemble Methods) Convolutional Neural Networks in Python (Easy NLP) (Cluster Analysis and Unsupervised Machine Learning) Unsupervised Deep Learning (Hidden Markov Models) Recurrent Neural Networks in Python Artificial Intelligence: Reinforcement Learning in Python Natural Language Processing with Deep Learning 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 learning to images Those who want to expand their knowledge of deep learning 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.

https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/ Deep Learning: Recurrent Neural Networks in Python GRU, LSTM, + more modern deep learning, machine learning, and data science for sequences Created by Lazy Programmer Inc. Last updated 5/2017 English What Will I Learn? Understand the simple recurrent unit (Elman unit) Understand the GRU (gated recurrent unit) Understand the LSTM (long short-term memory unit) Write various recurrent networks in Theano Understand backpropagation through time Understand how to mitigate the vanishing gradient problem Solve the XOR and parity problems using a recurrent neural network Use recurrent neural networks for language modeling Use RNNs for generating text, like poetry Visualize word embeddings and look for patterns in word vector representations Requirements Calculus Linear algebra Python, Numpy, Matplotlib Write a neural network in Theano Understand backpropagation Probability (conditional and joint distributions) Write a neural network in Tensorflow Description Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences – but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not – and as a result, they are more expressive, and more powerful than anything we’ve seen on tasks that we haven’t made progress on in decades. So what’s going to be in this course and how will it build on the previous neural network courses and Hidden Markov Models? In the first section of the course we are going to add the concept of time to our neural networks. I’ll introduce you to the Simple Recurrent Unit, also known as the Elman unit. We are going to revisit the XOR problem, but we’re going to extend it so that it becomes the parity problem – you’ll see that regular feedforward neural networks will have trouble solving this problem but recurrent networks will work because the key is to treat the input as a sequence. In the next section of the course, we are going to revisit one of the most popular applications of recurrent neural networks – language modeling. You saw when we studied Markov Models that we could do things like generate poetry and it didn’t look too bad. We could even discriminate between 2 different poets just from the sequence of parts-of-speech tags they used. In this course, we are going to extend our language model so that it no longer makes the Markov assumption. Another popular application of neural networks for language is word vectors or word embeddings. The most common technique for this is called Word2Vec, but I’ll show you how recurrent neural networks can also be used for creating word vectors. In the section after, we’ll look at the very popular LSTM, or long short-term memory unit, and the more modern and efficient GRU, or gated recurrent unit, which has been proven to yield comparable performance. We’ll apply these to some more practical problems, such as learning a language model from Wikipedia data and visualizing the word embeddings we get as a result. All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Theano. I am always available to answer your questions and help you along your data science journey. This course focuses on “how to build and understand“, not just “how to use”. Anyone can learn 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 internally. If you want more than just a superficial look at machine learning models, this course is for you. See you in class! NOTES: All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples In the directory: rnn_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 (conditional and joint distributions) Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file Deep learning: backpropagation, XOR problem Can write a 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 Learning in Python) (Bayesian Machine Learning in Python: A/B Testing) Deep Learning in Python Practical Deep Learning in Theano and TensorFlow (Supervised Machine Learning in Python 2: Ensemble Methods) Convolutional Neural Networks in Python (Easy NLP) (Cluster Analysis and Unsupervised Machine Learning) Unsupervised Deep Learning (Hidden Markov Models) Recurrent Neural Networks in Python Artificial Intelligence: Reinforcement Learning in Python Natural Language Processing with Deep Learning in Python Who is the target audience? If you want to level up with deep learning, take this course. If you are a student or professional who wants to apply deep learning to time series or sequence data, take this course. If you want to learn about word embeddings and language modeling, take this course. If you want to improve the performance you got with Hidden Markov Models, take this course. If you’re interested the techniques that led to new developments in machine translation, take this course. If you have no idea about deep learning, don’t take this course, take the prerequisites.

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