HerbertD primeMover2011

## fast_ai_mooc_important_points.md

      
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                bhavikngala
                / fast_ai_mooc_important_points.md
            
            
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              January 6, 2023 23:04
            
              
                This gist contains a list of important points from fast.ai "practical deep learning for coders" and "cutting edge deep learning for coders" MOOC
              
          
    This gist contains a list of points I found very useful while watching the fast.ai "Practical deep learning for coders" and "Cutting edge deep learning for coders" MOOC by Jeremy Howard and team. This list may not be complete as I watched the video at 1.5x speed on marathon but I did write down as many things I found to be very useful to get a model working. A fair warning the points are in no particular order, you may find the topics are all jumbled up.
Before beginning, I want to thank Jeremy Howard, Rachel Thomas, and the entire fast.ai team in making this awesome practically oriented MOOC.


Progressive image resolution training: Train the network on lower res first and then increase the resolution to get better performance. This can be thought of as transfer learning from the same dataset but at a different resolution. There is one paper by NVIDIA as well that used such an approach to train GANs.


Cyclical learning rates: Gradually increasing the learning rate initially helps to avoid getting stuc


## installing_dlib_on_macos_for_python.md

      
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                ageitgey
                / installing_dlib_on_macos_for_python.md
            
            
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              June 3, 2024 21:07
            
          
    How to install dlib v19.9 or newer (w/ python bindings) from github on macOS and Ubuntu

Pre-reqs:

Have Python 3 installed. On macOS, this could be installed from homebrew or even via standard
Python 3.6 downloaded installer from https://www.python.org/download. On Linux, just use your
package manager.
On macOS:

Install XCode from the Mac App Store (or install the XCode command line utils).
Have homebrew installed


On Linux:


## pg-pong.py
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

## torch-install-gpu-ec2.sh
# install torch on ec2 g2 instance

# installing torch
curl -s https://raw.githubusercontent.com/torch/ezinstall/master/install-deps | bash  # prereqs
git clone https://github.com/torch/distro.git ~/torch --recursive
cd ~/torch; ./install.sh
source ~/.bashrc

# confirm torch is installed
th
	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward
	# install torch on ec2 g2 instance

	# installing torch
	curl -s https://raw.githubusercontent.com/torch/ezinstall/master/install-deps \| bash # prereqs
	git clone https://github.com/torch/distro.git ~/torch --recursive
	cd ~/torch; ./install.sh
	source ~/.bashrc

	# confirm torch is installed
	th