strikingloo

11 ms·

61.
by strikingloo 7y ago
Thanks! I hadn't thought of doing Markov Chains since I was focusing on RNN/LSTMs, but I think I'll try that for the next article, see how that goes.
62.
by strikingloo 7y ago
Hey guys, I'm the writer. As you can see from the post, I'm still very much learning. What I want the most from this site is for more experienced people to help me out with some of my questions. Here they come: - Can you use Batch
63.
LSTM: How to Train Neural Networks to Write Like Lovecraft(datastuff.tech)
10 points by strikingloo 7y ago|4 comments
64.
Probability Distributions Every Data Scientist Should Know(datastuff.tech)
2 points by strikingloo 7y ago|0 comments
65.
Convolutional Neural Networks: an Introduction (TensorFlow Eager API)(datastuff.tech)
3 points by strikingloo 7y ago|0 comments
66.
K-Means Clustering with Dask (Image Filters for Cat Pictures)(datastuff.tech)
1 points by strikingloo 7y ago|0 comments
67.
My First 1000 Medium Followers: A Tech Writing Journey(datastuff.tech)
2 points by strikingloo 7y ago|0 comments
68.
by strikingloo 7y ago
I agree! Personally, I'd advice anyone who starts reading these books to also practice everything they read on them, and maybe keep all of those projects on GitHub for potential employers to see them.
69.
by strikingloo 7y ago
Bishop's looks like an awesome book, and it's on my reading list after many colleagues recommended it to me. I wouldn't have added it to the list though, because I haven't read it yet.
70.
Machine Learning Books That Helped Me Level Up(datastuff.tech)
307 points by strikingloo 7y ago|50 comments
71.
Autoencoders: Deep Learning with TensorFlow's Eager API(datastuff.tech)
3 points by strikingloo 7y ago|0 comments
72.
Intro to XGBoost: Predicting Life Expectancy with Supervised Learning(towardsdatascience.com)
3 points by strikingloo 7y ago|0 comments
73.
Intro to XGBoost: Predicting Life Expectancy on an HDI Dataset(datastuff.tech)
3 points by strikingloo 7y ago|0 comments
74.
Exploratory Data Analysis by Example: Pandas Framework on a Kaggle Dataset(datastuff.tech)
2 points by strikingloo 7y ago|0 comments
75.
by strikingloo 8y ago
Here you go! http://www.datastuff.tech/data-analysis/magic-the-gathering-...
76.
by strikingloo 8y ago
So after the previous article, some people wanted to see what the results were. Here are some of the insights I could get. Let me know what you think!
77.
Results of Applying K-Means Clustering to Magic: The Gathering (Part 2)(datastuff.tech)
2 points by strikingloo 8y ago|1 comments
78.
by strikingloo 8y ago
https://github.com/StrikingLoo/MtGRecommender/blob/master/mt... Here you go!
79.
by strikingloo 8y ago
Awesome! I'll work on that side of the article then, I'll probably have it ready by tomorrow!
80.
by strikingloo 8y ago
It's visible on the notebook, but I will add them to the article if more people insist. I'm still kinda new to writing, and wasn't sure if it would make the article better or worse. As a summary, I can tel you one of the clus
81.
K-Means Clustering: Unsupervised Learning Applied on Magic:The Gathering(datastuff.tech)
57 points by strikingloo 8y ago|18 comments
82.
How to Start Using the Terminal to Be More Productive(datastuff.tech)
2 points by strikingloo 8y ago|0 comments
83.
Vim Tutorial: How to Use Multiple Vim Registers and Windows(dataden.tech)
2 points by strikingloo 8y ago|0 comments
84.
by strikingloo 8y ago
Awesome, so what are some things that are not on the help page you think would make this more useful?
85.
by strikingloo 8y ago
Hi everyone! Please tell me what you think of this tutorial, and if you have any interesting tips or scripts I could've added to it. I'm still learning, so all feedback is extremely valuable.
86.
How to Start Using Curl and Why: Hands-On Introduction(dataden.tech)
5 points by strikingloo 8y ago|3 comments
87.
Shell 101: How to Manage Files and Strings from the Terminal(dataden.tech)
2 points by strikingloo 8y ago|0 comments
88.
by strikingloo 8y ago
Thanks! I'll definitely check that out.
89.
by strikingloo 8y ago
Thank you very much! Nitpicky readers are the kind of readers I look forward to, since they're the ones that help me learn the most. That's why I thought this site would be a good place to post. That said, I learned those two thi
90.
SIMD Intro:Comparing SIMD Assembly with C on a Trivially Concurrent Algorithm(dataden.tech)
2 points by strikingloo 8y ago|4 comments

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