oba2311

## LangTalksMeetUp2.md

      
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    LLM Agents: Advancements in Multimodal Capabilities and Real-World Applications

Lecture 1: Building LLM Apps with Multimodal Capabilities

Presenter: Gili Nachum

Key Argument

LLM applications can be significantly enhanced through the integration of multimodal capabilities, leading to improved accuracy and versatility.

  
## RL assignment

# imports:
import gym
import random
import numpy as np
import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
from statistics import median, mean
from collections import Counter

## External Tutorial: Let’s make a bar chart - D3
External Tutorial: Let’s make a bar chart

index.html#
<!DOCTYPE html>
<meta charset="utf-8">
<style>

.chart rect {
  fill: steelblue;
}

## Code Review - Recap of Tutorial - D3 - Udacity
Code Review - Recap of Tutorial
Tutorial First Code: China’s circle

// clear the main area
d3.select('.main').html("");
// create an SVG empty canvas
var svg = d3.select('.main').append('svg');
// define size of canvas as (600,300)
svg.attr('width', 600).attr('height',300);
// set y scale: linear. Input: 15,90. Output: 0,250. Remember Y axis is top down.

## isr.topo.json

      
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## Urban 9.1

# count=100,
# q='*',
search = api.search(q='#jewish',geocode='31.0461,34.8516,200km')  #**supply whatever query you want here**
print(len(search))


# count=100,
# q='*',

## RNNs - Group B
"""

Adopted and modified from:
https://gist.githubusercontent.com/karpathy/d4dee566867f8291f086/raw/119a6930b670bced5800b6b03ec4b8cb6b8ff4ec/min-char-rnn.py

Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

## RNNs - Group A
"""

Adopted and modified from:
https://gist.githubusercontent.com/karpathy/d4dee566867f8291f086/raw/119a6930b670bced5800b6b03ec4b8cb6b8ff4ec/min-char-rnn.py

Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

## Vanilla RNNs - Karpathy
"""

Adopted and modified from:
https://gist.githubusercontent.com/karpathy/d4dee566867f8291f086/raw/119a6930b670bced5800b6b03ec4b8cb6b8ff4ec/min-char-rnn.py

Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

## yaml of Travis CI 13.2
## Q 1:
https://travis-ci.org/oba2311/cs162-continuous-integration

## Q 2:

# user type:
sudo: required

# prog lang:
language: python

	# imports:
	import gym
	import random
	import numpy as np
	import tflearn
	from tflearn.layers.core import input_data, dropout, fully_connected
	from tflearn.layers.estimator import regression
	from statistics import median, mean
	from collections import Counter
	External Tutorial: Let’s make a bar chart

	index.html#
	<!DOCTYPE html>
	<meta charset="utf-8">
	<style>

	.chart rect {
	fill: steelblue;
	}
	Code Review - Recap of Tutorial
	Tutorial First Code: China’s circle

	// clear the main area
	d3.select('.main').html("");
	// create an SVG empty canvas
	var svg = d3.select('.main').append('svg');
	// define size of canvas as (600,300)
	svg.attr('width', 600).attr('height',300);
	// set y scale: linear. Input: 15,90. Output: 0,250. Remember Y axis is top down.

	# count=100,
	# q='*',
	search = api.search(q='#jewish',geocode='31.0461,34.8516,200km') #supply whatever query you want here
	print(len(search))



	# count=100,
	# q='*',
	"""

	Adopted and modified from:
	https://gist.githubusercontent.com/karpathy/d4dee566867f8291f086/raw/119a6930b670bced5800b6b03ec4b8cb6b8ff4ec/min-char-rnn.py

	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np
	## Q 1:
	https://travis-ci.org/oba2311/cs162-continuous-integration

	## Q 2:

	# user type:
	sudo: required

	# prog lang:
	language: python