udemy deep learning

　　Preface: This section describes sparse autoencoder, a well-known Algorithm in deep learning, that is, automatic coding in Sparse Mode. We know that deep learning is also called unsupervised learning, so sparse autoencoder here should also be unsupervised. According to the

. You'll need to the know how-to-use this functions for future assignments. 1.1-sigmoid function, Np.exp () Before using Np.exp (), you'll use MATH.EXP () to implement the Sigmoid function. You'll then why Np.exp () is preferable to Math.exp (). Exercise: Build a function that returns the sigmoid's a real number X. Use MATH.EXP (x) for the exponential funct Ion. Reminder:Sigmoid (x) =11+e−x sigmoid (x) = \frac{1}{1+e^{-x} is sometimes also known as the The logistic function. It is a non-linear f

distribution of samples, Discriminant model as far as possible to distinguish between real training samples and generated model expression of the sample, the two adversarial optimization, and finally can make the generated model can generate and train a sample with the same distribution, so that the network can predict the distribution of the samples not seenAs well as OpenAI's latest work, generating images from a picture setGenerative models edited on 2016-06-23 6 reviews thanks for sharing c

Click on the "ZTE developer community" above to follow us Read a first-line developer, a good article every day about the author The author Dai is a deep learning enthusiast who focuses on the NLP direction. This article introduces the current status of machine translation, and the basic principles and processes involved, to beginners who are interested in deep

Deep learning target detection (object detection) series (i) r-cnnDeep learning target detection (object detection) series (ii) spp-netDeep learning target detection (object detection) series (iii) Fast R-CNNDeep learning target detection (object detection) series (iv) Faste

In fact, starting from this blog post, we are really into the field of deep learning. In the field of deep learning, the proven mature algorithm, currently has deep convolutional network (DNN) and recursive Network (RNN), in the field of image recognition, video recognition,

Setting up a deep learning machine from Scratch (software)A detailed guide-to-setting up your machine for deep learning. Includes instructions to the install drivers, tools and various deep learning frameworks. This is tested on a

Source: Michael Nielsen's "Neural Network and Deep leraning", click the end of "read the original" To view the original English.This section translator: Hit Scir master Xu Wei (https://github.com/memeda)Statement: We will be in every Monday, Thursday, Sunday regularly serialized the Chinese translation of the book, if you need to reprint please contact [email protected], without authorization shall not be reproduced."This article is reproduced from" h

Today's two network structures are the latest in the industry for image processing problems proposed by the latest structure, the main solution is the Super deep network in training optimization problems encountered. To tell the truth, both models are not mathematically complex in themselves, but it does have a very good effect in combat (the deep residual network helps Microsoft's team to gain the 2015 Ima

Tags: arc update. So dia switch Linu HTTPS installation tutorial DevelopThe Deep learning Framework Keras is based on TensorFlow, so installing Keras requires the installation of TensorFlow:1. The installation tutorial is mainly referenced in two blog tutorials:Https://www.cnblogs.com/HSLoveZL/archive/2017/10/27/7742606.htmlHttps://www.jianshu.com/p/5b708817f5d8?from=groupmessage2. This tutorial starts with

What I-READ for deep-learningToday, I spent some time on the new papers proposing a new from training very deep neural networks (Highway-networks) an d A new activation function for auto-encoders (Zero-bias autoencoders and the benefits ofCo-adapting FEATURES) which evades the use of any regularization methods such as Contraction or denoising.Lets start with the first one. Highway-networks proposes a new ac

Recently in the NetEase Cloud classroom learning "depth study" micro-professional, the class after the programming work record down. Deep Learning – Wunda The logical regression written in Python before comparison deeplearning operation Logistic regression with a neural network mindset Welcome to your (required) programming assignment! You'll build a logistic reg

The algorithm of deep learning Word2vec notesStatement:This article turns from a blog post in HTTP://WWW.TUICOOL.COM/ARTICLES/FMUYAMF, if there is a mistake to hope HaihanObjectiveWhen looking at the information of Word2vec, often will be called to see that several papers, and that several papers also did not systematically explain the specific principles and algorithms Word2vec, so swaiiow on a dare to tid

Original English: Teach Yourself Deep Learning with TensorFlow Author: Vincent Vanhoucke,google chief Scientist. Translation: Guokai Han. In recent years, deep learning has become one of the hottest topics in the field of machine learning. TensorFlow, as an open source proje

Deep Learning Neural Network pure C language basic Edition Today, Deep Learning has become a field of fire, and the performance of Deep Learning Neural Networks (DNN) in the field of computer vision is remarkable. Of course, convo

[Deep Learning] Implementing a game-based AI, starting with wuziqi (1) I haven't written a blog for a long time. How long has it taken, about 8 years ??? I recently picked up writing again ...... Recently, I 've been tossing AI and writing an AI-related question to my team's friends. After so many years of machine learning, from classification to clustering, fro

Introduction of recursive neural network in Tan Yin-layer neural network word embedding and sharing the criticism conclusion thanks From: https://colah.github.io/posts/2014-07-NLP-RNNs-Representations/Posted on July 7, 2014Neural network, depth learning, characterization, NLP, recursive neural network Introduction In the past few years, deep neural networks have dominated pattern recognition. They surface