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sslinux
@sslinux
42
Linux技术爱好者
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China
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https://www.sslinux.com
Created
2018-05-06 13:27
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sslinux
deep
2018-05-31 17:53
Deep Dream - Unconventional Neural Networks p.7
Welcome To Subcribe My Channel: In this part, we're going to get into deep dreaming in TensorFlow. If you are not familiar with deep dream, it's a method we can use to allow a neural network to
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sslinux
learning
2018-05-31 17:43
Generative Model Basics - Unconventional Neural Networks p.1
Welcome To Subcribe My Channel: Hello and welcome to a series where we will just be playing around with neural networks. The idea here is to poke around with various neural networks, doing unconventional
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1
sslinux
python
2018-05-31 16:35
Generating with MNIST - Unconventional Neural Networks p.3
Welcome To Subcribe My Channel: Hello and welcome to part 3 of our series of experimenting with neural networks. In this tutorial, we're going to take the same generative model that we've been working
sslinux
python
2018-05-31 16:29
Classification Generator Training Attempt - Unconventional Neural Networks p.4
Welcome To Subcribe My Channel: Hello and welcome to part 4 of our series of having some fun with neural networks, currently with generative networks. Wher we left off, we're building the dataset that
sslinux
python
2018-05-31 16:23
Classification Generator Testing Attempt - Unconventional Neural Networks p.5
Welcome To Subcribe My Channel: Hello and welcome to part 5 of our neural network shenanigans series. Lately, we've been working on doing classification with a generative model. I'll class this a
sslinux
python
2018-05-31 16:18
Drawing a Number by Request with Generative Model - Unconventional Neural Networks p.6
Welcome To Subcribe My Channel: Hello and welcome to part 6 of our neural network antics. In the previous tutorial, we attempted to use a generative model to generate classes of MNIST numbers, using the
sslinux
tensorflow
2018-05-31 16:04
Deep Dream Frames- Unconventional Neural Networks p.8
Welcome To Subcribe My Channel: In this Tutorial, we cover the idea of taking deep dream images, and using them as frames in a video. Text tutorials and sample code: Chat with us on Discord: Support the
sslinux
python
2018-05-31 15:59
Deep Dream Video- Unconventional Neural Networks p.9
Welcome To Subcribe My Channel: We've created many deep dream images up to this point, and now we're looking to convert them to video. Text tutorials and sample code: Chat with us on Discord: Support the
sslinux
python
2018-05-31 15:54
Doing Math with Deep Learning (Addition)- Unconventional Neural Networks p.10
Welcome To Subcribe My Channel: Can we do math with deep learning? Let's see how far we can go! Github specific commit for the seq2seq chatbot used here: Text tutorials and sample code: Chat with us on
sslinux
tensorflow
2018-05-31 15:47
More Complex Math with Deep Learning - Unconventional Neural Networks p.11
Welcome To Subcribe My Channel: Trying to do some more complex types of math equations with deep learning! Text tutorials and sample code: Chat with us on Discord: Support the content: Twitter: Facebook:
sslinux
deep
2018-05-31 15:38
Complex Math Results - Unconventional Neural Networks p.12
Welcome To Subcribe My Channel: Can we do math with deep learning? Let's see how far we can go! Github specific commit for the seq2seq chatbot used here: Text tutorials and sample code: Chat with us on
sslinux
learning
2018-05-31 15:33
Generating Pythonic code with Neural Network - Unconventional Neural Networks p.2
Welcome To Subcribe My Channel: Hello and welcome to part 2 of our series of just poking around with neural networks. In the previous tutorial, we played with a generative model, and now have already set
sslinux
tutorial
2018-05-31 15:11
How to program the Best Fit Slope - Practical Machine Learning Tutorial with Python p.8
Welcome To Subcribe My Channel: Welcome to the 8th part of our machine learning regression tutorial within our Machine Learning with Python tutorial series. Where we left off, we had just realized that
sslinux
tutorial
2018-05-28 16:55
Regression How it Works - Practical Machine Learning Tutorial with Python p.7
Welcome To Subcribe My Channel: Welcome to the seventh part of our machine learning regression tutorial within our Machine Learning with Python tutorial series. Up to this point, you have been shown the
sslinux
machine
2018-05-28 16:30
Pickling and Scaling - Practical Machine Learning Tutorial with Python p.6
Welcome To Subcribe My Channel: In the previous Machine Learning with Python tutorial we finished up making a forecast of stock prices using regression, and then visualizing the forecast with Matplotlib.
sslinux
artificial
2018-05-28 16:18
Regression forecasting and predicting - Practical Machine Learning Tutorial with Python p.5
Welcome To Subcribe My Channel: In this video, make sure you define the X's like so. I flipped the last two lines by mistake: X = np.array(df.drop(['label'],1)) X = preprocessing.scale(X) X_lately =
sslinux
learning
2018-05-28 16:02
Regression Training and Testing - Practical Machine Learning Tutorial with Python p.4
Welcome To Subcribe My Channel: Welcome to part four of the Machine Learning with Python tutorial series. In the previous tutorials, we got our initial data, we transformed and manipulated it a bit to
sslinux
machine
2018-05-28 13:13
Regression Features and Labels - Practical Machine Learning Tutorial with Python p.3
Welcome To Subcribe My Channel: We'll be using the numpy module to convert data to numpy arrays, which is what Scikit-learn wants. We will talk more on preprocessing and cross_validation when we get to
sslinux
learning
2018-05-28 11:11
Regression Intro - Practical Machine Learning Tutorial with Python p.2
Welcome To Subcribe My Channel: To begin, what is regression in terms of us using it with machine learning? The goal is to take continuous data, find the equation that best fits the data, and be able forecast
sslinux
tutorial
2018-05-28 11:00
Practical Machine Learning Tutorial with Python Intro p.1
Welcome Subcribe My Channel: The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and
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