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Unsupervised Deep Learning in Python

#artificialintelligence

Online Courses Udemy - Unsupervised Deep Learning in Python, Theano / Tensorflow: Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA Created by Lazy Programmer Inc. English [Auto] Students also bought Machine Learning and AI: Support Vector Machines in Python Recommender Systems and Deep Learning in Python Natural Language Processing with Deep Learning in Python Data Science: Natural Language Processing (NLP) in Python Ensemble Machine Learning in Python: Random Forest, AdaBoost Preview this course GET COUPON CODE Description This course is the next logical step in my deep learning, data science, and machine learning series. I've done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? In these course we'll start with some very basic stuff - principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding). Next, we'll look at a special type of unsupervised neural network called the autoencoder.


Unsupervised Deep Learning in Python

#artificialintelligence

Online Courses Udemy Unsupervised Deep Learning in Python, Theano / Tensorflow: Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA Created by Lazy Programmer Inc. Students also bought Advanced AI: Deep Reinforcement Learning in Python Deep Learning: Recurrent Neural Networks in Python Ensemble Machine Learning in Python: Random Forest, AdaBoost Deep Learning: GANs and Variational Autoencoders Deep Learning Prerequisites: Linear Regression in Python Machine Learning and AI: Support Vector Machines in Python Preview this course GET COUPON CODE Description This course is the next logical step in my deep learning, data science, and machine learning series. I've done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? In these course we'll start with some very basic stuff - principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding). Next, we'll look at a special type of unsupervised neural network called the autoencoder.


Deep Learning: Convolutional Neural Networks in Python

#artificialintelligence

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.


Cluster Analysis and Unsupervised Machine Learning in Python

#artificialintelligence

Cluster analysis is a staple of unsupervised machine learning and data science. It is very useful for data mining and big data because it automatically finds patterns in the data, without the need for labels, unlike supervised machine learning. In a real-world environment, you can imagine that a robot or an artificial intelligence won't always have access to the optimal answer, or maybe there isn't an optimal correct answer. You'd want that robot to be able to explore the world on its own, and learn things just by looking for patterns. Do you ever wonder how we get the data that we use in our supervised machine learning algorithms?