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Luna Gomes
@GameReviewsLuna_twitter
I thought perhaps due to if I do right click, it says "save as .webp" but how do I check whether it is a webp file?
Philip Durbin
@pdurbin
The webp files always fail? That's the pattern? I see you asked at https://gitter.im/FreeCodeCamp/python and it might be more on topic there.
Luna Gomes
@GameReviewsLuna_twitter
well yea but it is quite old code so I am kind of upgrading it and perhaps I might have a fix
Zied Aouini
@aouinizied
https://github.com/aouinizied/nfstream
a flexible network data analysis framework.
Feedbacks are welcome!

networkanalytics #machinelearning #datasets #dataprocessing

Philip Durbin
@pdurbin
You might be interested in this. It feels related to me, written by a friend of mine: https://dl.acm.org/doi/10.1145/3332186.3332224
Alice Jiang
@becausealice2
@aouinizied product promotion is in violation of FreeCodeCamp's Code of Conduct rule on spam. Please remove your link.
WELP! I am finally returned to the land of the overeducated and have bits and pieces of time enough to get back into the thick of things. I've been jonesing for some visualizations, not sure where I want to start just yet, though.....
Philip Durbin
@pdurbin
@becausealice2 you should come listen to the keynote by Fernando Perez, the person who started what we now call Jupyter Notebooks: "(Note that there is no registration fee for these keynote talks)" https://projects.iq.harvard.edu/datafest2020
Zied Aouini
@aouinizied
@becausealice2 Do you consider open source project as product?
and the aim is to help Network Data Scientist to have a comon framework, so the word "product" hurts me a little bit. But it's OK to remove it if you want.
Alice Jiang
@becausealice2
@pdurbin I am having surgery Tuesday morning or I absolutely would!
@aouinizied I do not intend at all to hurt but all self promotion falls under the same umbrella, I'm afraid. We're a bit lenient when the presentation of the "product" (blog, OSS, video, etc.) is at least done in such a way to request some form of feedback (as opposed to "use the thing!")
We are trying to encourage active discussions and learning, others have abused FCC's visibility to drop links and leave and spoiled it for everyone else
Philip Durbin
@pdurbin
Good luck!
Alice Jiang
@becausealice2
it's a quick minor dental thing... local anesthetic only, so I should be just fine after a day or two :)
Philip Durbin
@pdurbin
phew
Alice Jiang
@becausealice2
@aouinizied You might try posting about it in the forum describing what it is, how it works and would be used, and accept feedback. I can't guarantee it will get indexed for search engines but if you can open discussion there I know the Data Science and Machine Learning subtopics are in need of some traffic
I've been slacking on starting discussions :(
Philip Durbin
@pdurbin
Are there older discussions you started that we should check out?
Alice Jiang
@becausealice2
The only one I've started is on starting a DS learning pathway by studying ethics if anyone here is interested in that, I stand by the idea though I'm not sure of the quality of the courses I recommend or availability of others anymore
I've been meaning to post more but the holiday season was ... well... the holiday season lol. I should hopefully be more active on the forum after surgery
Philip Durbin
@pdurbin
Yeah. Post holiday season I started my first blog ever, if you can believe it. I've been blogging once a day in 2020. :) Oh, here's something new. We are starting to collect data that we hope will help indicate the health (or non-health) of open source projects. I recently joined the team: https://projects.iq.harvard.edu/osshealthindex/team
So if anyone wants to help look at the data, please let me know! :)
Philip Durbin
@pdurbin
probably as good as any
Zijing Zhang
@zzj0402_gitlab
# Create an instance of the model
print(len(X_train.keys()))
print(X_train.keys())
print(X_train.values)
model = keras.Sequential([
  layers.Dense(64, activation='relu', input_shape=[len(X_train.keys())]),
  layers.Dense(64, activation='relu'),
  layers.Dense(1)
])
model.summary()
5
Index(['gender_code', 'agenum', 'apachescore_code', 'unittype_code',
       'admissionweight_code'],
      dtype='object')
[[  1.  59.  71.   0.  61.]
 [  2.  82.  63.   4. 243.]
 [  2.  60.  63.   5. 385.]
 ...
 [  2.  72. 105.   5. 458.]
 [  2.  75. 151.   1. 765.]
 [  2.  66.  30.   5. 502.]]
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 64)                384       
_________________________________________________________________
dense_1 (Dense)              (None, 64)                4160      
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 65        
=================================================================
Total params: 4,609
Trainable params: 4,609
Non-trainable params: 0
_________________________________________________________________
model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['accuracy'])
history = model.fit(X_train.values, y_train.values, epochs=1, 
                    validation_data=(X_test.values, y_test.values))
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
Train on 8000 samples, validate on 2000 samples
8000/8000 [==============================] - 1s 71us/sample - loss: nan - acc: 0.4954 - val_loss: nan - val_acc: 0.5150
Suggestion?
Why it's getting nan on loss?
Shaun Hamilton
@Sky020
@zzj0402_gitlab Have you tried replacing 'binary_crossentropy' with tf.keras.losses.BinaryCrossentropy()
Zijing Zhang
@zzj0402_gitlab
@Sky020 thanks for the suggestion! Turns out
Dataset has Nan values
Alice Jiang
@becausealice2
@/all
HEY YOU GUYSSSSSS! I'm supposed to be helping to breathe life into the Data Science and Machine Learning and I've finally got enough down time to start creating topics. Is there any specific type of topic you would want to see there?
Marky Jackson
@markyjackson-taulia
Use cases
Alice Jiang
@becausealice2
Asking specifically for things you can't or aren't comfortable posting yourself. I want as much input from not-me as I can get :)
Use cases for what?
for DS and ML in general? or for specific types of algorithms?
Marky Jackson
@markyjackson-taulia
The spectrum of cases is vast so I apologize for responding knowing that could go anywhere
Here is one I am working on currently: https://arxiv.org/abs/1912.07354
In the past for course work, I did some interesting things with data from https://www.cdc.gov/nchs/icd/index.htm
For for fun I have also played with baseball stats..
Those are a few things that would perk my interest
Alice Jiang
@becausealice2
So you'd be interested in seeing people post about their projects?
Marky Jackson
@markyjackson-taulia
Yes
Alice Jiang
@becausealice2
I agree. I have no examples I can post as of right now, but I encourage you and everyone else here to lead the charge!
Marky Jackson
@markyjackson-taulia
:thumbsup:
Maybe even have a community project. Loads of fun datasets to learn from
Alice Jiang
@becausealice2
We've done some community things via gitter, it would be interesting to try it on the forum
Eric Leung
@erictleung
@becausealice2 I'll try to make some time to contribute! I have plenty I'd be interested to discuss
Alice Jiang
@becausealice2
YAY! Thank you thank you!
Riadh
@KWFE
@becausealice2 hi
Alice Jiang
@becausealice2
Hello :)