Nov05

New York,Los Angeles,2778
Chicago,Houston,940
San Francisco,Seattle,1094
Miami,Atlanta,663
Washington DC,Boston,394
Dallas,Austin,195
Phoenix,Denver,586
Detroit,Minneapolis,539
Charlotte,Raleigh,172
Philadelphia,Pittsburgh,305

Nov05

3 2 0
6 5 1
7 4 0
8 1 1

23

454971987 406874902 1

503069073

Nov05

https://www.youtube.com/watch?v=_DaTsI42Wvo&t=653s
@furkanunsal5814, 10 months ago (edited)

I solved the problem much differently and pure mathematically before watching the video. it is very hard to explain a solution in text form but the main idea was to see the portals as binary numbers. I split the problem into two. evaluating how many times the player has entered a portal (to teleport not passover) and then calculating the total distance traveled. The second problem's solution was easy I just had to calculate the difference in position between the portal and the teleportation target to calculate the "cost" of the teleportation. the total length of the line plus all the costs were equal to the total distance traveled. now for the solution to the first part, look at the first example with 4 portals (red, orange, yellow, green). in the solution of this part ignore all the distances. if you look at the first red portal it teleports the player right behind itself and is thus equivalent to a binary nu

Nov05

# **The Sound of Silence**
music tabs for 21-key Kalimba, encoded by nov05

,,,,,,,
6ld,3,1,3,6ld,3,1,3
,,6,6,1d,1d,3d,3d,v,2d,,,,,,,,v,,5,5,5,7,7,2d,2d,v,1d,,,,,,,
6ld,3,1,3,6ld,3,1,3,v,5ld,2,7ld,2,5ld,2,7ld,2,v,5ld,2,7ld,2,5ld,2,7ld,2,v,6ld,3,1,3,6ld,3,1,3
,1d,1d,1d,3d,3d,5d,5d,v,6d,,6d,5d,,,,,v,,,1d,1d,3d,3d,5d,5d,v,6d,,6d,5d,,,,
6ld,3,1,3,6ld,3,1,3,v,4,1d,,,1,5,3,5,v,1,5,3,5,1,5,3,5,v,4,1d,,,1,5,3,5
,,1d,1d,6d,6d,,,v,,,6d,7d,1dd,1dd,,,v,7d,6d,,5d,,,,,v,,,6d,5d,3d,,,

Nov05

👉 with all 7 features, a simple vanilla neural network with hidden dimension [64] as regressor, trained 200 epochs.

(kaggle--feedback-prize-ell) PS D:\github\kaggle--feedback-prize-ell> & D:/Users/guido/miniconda3/envs/kaggle--feedback-prize-ell/python.exe d:/github/kaggle--feedback-prize-ell/main.py
MSFTDeBertaV3Config object:
         model name: deberta-v3-base
         traning device: cuda:0
         inference device: cuda:0
model type: nn
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.

Nov05

the content below is from the original author.

---

Feedback Prize - English Language Learning

My first(-ish) Kaggle competition

I'm new to Kaggle and wanted to take this challenge as an opportunity to:

• learn how to compete in Kaggle: How to set things up, manage the constraint of number of submissions etc.. You might not realize it if you have been competing for a while, but there is a learning curve.

Nov05

Udacity Deep Reinforcement Learning - p1 env setup

👉 for the course projcts, Unity MLAgents - Banana Collector, etc.
👉 go to the Banana and VisualBanana notebooks
👉 go to the course repo
👉 check course curriculum

---

Window 11, VSCode, Minicoda, Powershell

Nov05

👉 check the colab notebook
👉 go to the cell

⚠️ issue

mono_gdb_render_native_backtraces not supported on this platform, unable to find gdb or lldb  

• installed mlagents release 1. used trainer_config.yaml in the old format.

Nov05

• go to the cell in the notebook
• pip install the latest mlagents version. using banana.ymal in the new format.

⚠️ error

mlagents_envs.exception.UnityEnvironmentException: Environment shut down with return code -6 (SIGABRT).

Nov05

👉 Unity ML-Agents Banana Collectors Project Submission

1. For this toy game, two Deep Q-network methods are tried out. Since the observations (states) are simple (not in pixels), convolutional layers are not in use. And the evaluation results confirm that linear layers are sufficient for solving the problem.
   • Double DQN, with 3 linear layers (hidden dims: 256*64, later tried with 64*64)
   • Dueling DQN, with 2 linear layers + 2 split linear layers (hidden dims: 64*64)

▪️ The Dueling DQN architecture is displayed as below.