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Welcome to part 3 of the sockets tutorial with Python. We've learned how to send and receive string data via sockets, and now I want to talk about is pickles. Not the food, but the serialization technique in Python.
Text-based tutorial and sample code: https://pythonprogramming.net/....pickle-objects-socke
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In a quest to teach neural networks via transformers to write Python code. Project name: Generative Python Transformers!
Neural Networks from Scratch book: https://nnfs.io
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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 them in the code, but preprocessing is the module used to do some cleaning/scaling of data prior to machine learning, and cross_ alidation is used in the testing stages. Finally, we're also importing the LinearRegression algorithm as well as svm from Scikit-learn, which we'll be using as our machine learning algorithms to demonstrate results.
At this point, we've got data that we think is useful. How does the actual machine learning thing work? With supervised learning, you have features and labels. The features are the descriptive attributes, and the label is what you're attempting to predict or forecast. Another common example with regression might be to try to predict the dollar value of an insurance policy premium for someone. The company may collect your age, past driving infractions, public criminal record, and your credit score for example. The company will use past customers, taking this data, and feeding in the amount of the "ideal premium" that they think should have been given to that customer, or they will use the one they actually used if they thought it was a profitable amount.
Thus, for training the machine learning classifier, the features are customer attributes, the label is the premium associated with those attributes.
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So now that you know the basics of what Pytorch is, let's apply it using a basic neural network example. The very first thing we have to consider is our data.
Text-based tutorials and sample code: https://pythonprogramming.net/....data-deep-learning-n
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Welcome to part 2 of the web scraping with Beautiful Soup 4 tutorial mini-series. In this tutorial, we're going to talk about navigating source code to get just the slice of data we want.
Tutorial code: https://pythonprogramming.net/....navigating-pages-scr
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The next loop is the For loop. The idea of the for loop is to "iterate" through something. For each thing in that something, it will do a block of code. Most often, you will a for loop's structure very much like
for eachThing in thisThing:
do this stuff
in this block
So, again, why would someone use each type of loop?
Typically, you will see the while loop being used for finite tasks that have predetermined length, and the for loop being used for tasks that have uncertain and variable time-frames.
That said, the for loop can be used for the exact same tasks as the while loop.
For this reason, I prefer the for loop myself, but again, it comes down to personal preference.
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This python 3 tutorial covers the for loop in python 3.3.3
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Text-based tutorial and sample code: https://pythonprogramming.net/....autoencoders-tutoria
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Here, we create a convolutional neural network using the same neural network that we used in the machine learning tutorial series: https://pythonprogramming.net/....tflearn-machine-lear
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Adding logic to the Starcraft 2 artificial intelligence to defend our main base.
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Asyncio basics: https://pythonprogramming.net/....asyncio-basics-inter
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Welcome to part 8 of the Deep Learning with Python, Keras, and Tensorflow series. In this tutorial, we're going to work on using a recurrent neural network to predict against a time-series dataset, which is going to be cryptocurrency prices.
Text tutorials and sample code: https://pythonprogramming.net/....cryptocurrency-recur
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In the previous tutorial, we covered how to use channels to send and receive values with goroutines. That said, it was just a basic example. In reality, we're likely to have questions of synchronization and iterating through known, or unknown, numbers of channel returns.
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Welcome to the next part of our Deep Learning with Python, TensorFlow, and Keras tutorial series. In this tutorial, we're going to continue building our cryptocurrency-price-predicting Recurrent Neural Network.
Text tutorials and sample code: https://pythonprogramming.net/....balancing-rnn-data-d
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A self-driving car in GTA 5.
For more information on this project, and how it all began from simple lane detection to deep learning, follow the full tutorial series here: https://pythonprogramming.net/....game-frames-open-cv-
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Welcome to the next tutorial covering deep learning with Python, Tensorflow, and Keras. We've been working on a cryptocurrency price movement prediction recurrent neural network, focusing mainly on the pre-processing that we've got to do. In this tutorial, we're going to be finishing up by building our model and training it.
Text tutorials and sample code: https://pythonprogramming.net/....crypto-rnn-model-dee
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Welcome to a tutorial on sockets with Python 3. We have a lot to cover, so let's just jump right in. The socket library is a part of the standard library, so you already have it.
Source code and text-based tutorial: https://pythonprogramming.net/....sockets-tutorial-pyt
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DreamFusion is Google research (https://arxiv.org/pdf/2209.14988.pdf) into generating 3D objects from text prompts, and is based off 2D diffusion models.
Stable DreamFusion github: https://github.com/ashawkey/st....able-dreamfusion?s=0
NeRF (Neural Radiance Fields): https://www.matthewtancik.com/nerf
Meta AI make-a-video: https://makeavideo.studio/
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