Ideas to tweak your neural network:
1. More hidden layers instead of more neurons
2. Batch size could make a difference
3. Adjust the learning rate
4. Play with different optimizers
5. Use a learning rate scheduler
6. Try different dropout rates
These are my top.
Yours?
It's common for people to believe that they're late to their field, that the tallest trees have already been felled. Personally, I think humanity's best works are still ahead, in every single field, and this is likely to remain true for thousands of years
Some of the best developers I've worked with are always asking and wondering. "How does this code work? Why does it work this way?" If you're an #AspiringDeveloper, one of the best ways to become a great dev is to be curious.
#webdev#100DaysOfCode#JuniorDevelopers#CodeNewbies
Woohoo! #Python 3.8 was just released. And on the same day that PyPI hit 200,000 packages :-)
This one really is the best Python ever. Download it now.
https://t.co/rayw3zrIQE
Some highlights:
* Multiprocessing shared memory
* The walrus operator :=
* The = format specifier for f-strings
* Much faster variable access for: globals, builtins, named tuples, class variables, and C function calls
* Beefed-up math and statistics modules
The Python 3.8 final release is scheduled for 21 October 2019.
That almost coincides with another important milestone.
The #Python Package Index will hit 200,000 packages somewhere around October 10th.
#Python 3.8 quick demo: A walrus operator one-liner.
def binomial_coefficients(n):
'Coefficients of (x + 1) ** n'
return [(c := c * (n - k + 1) // k if k else 1) for k in range(n+1)]
>>> binomial_coefficients(10)
[1, 10, 45, 120, 210, 252, 210, 120, 45, 10, 1]
One of the (many) reasons AI research is progressing faster than ever is that there're now more people doing AI research than at any other point in history.
This is a wonderful trend, and lets keep welcoming more people into AI!
Are you a deep learning researcher? Wondering if all this TensorFlow 2.0 stuff you heard about is relevant to you?
This thread is a crash course on everything you need to know to use TensorFlow 2.0 + Keras for deep learning research. Read on!
Here are some things you won't have to write in 2.0:
❌ session.run
❌ tf.control_dependencies
❌ tf.global_variables_initializer
❌ tf.cond, tf.while_loop
You're welcome! Tune in to #TFDevSummit to see tf.function pick up the slack → https://t.co/L1MWwrN0IH
The determinant of a matrix is how much the matrix expands or contracts space. More precisely: it's the volume of the parallelepiped spanned by the columns of the matrix. (Ditto the rows.) This, btw, is why det 0 => non-invertible: one or more spatial dimensions has collapsed