π€π§ Wan 2.1: Alibabaβs Open-Source Revolution in Video Generation
ποΈ 21 Oct 2025
π AI News & Trends
The landscape of artificial intelligence has been evolving rapidly, especially in the domain of video generation. Since OpenAI unveiled Sora in 2024, the world has witnessed an explosive surge in research and innovation within generative AI. However, most of these cutting-edge tools remained closed-source limiting transparency and accessibility. Recognizing this gap, Alibaba Group introduced Wan, ...
#Alibaba #Wan2.1 #VideoGeneration #GenerativeAI #OpenSource #ArtificialIntelligence
ποΈ 21 Oct 2025
π AI News & Trends
The landscape of artificial intelligence has been evolving rapidly, especially in the domain of video generation. Since OpenAI unveiled Sora in 2024, the world has witnessed an explosive surge in research and innovation within generative AI. However, most of these cutting-edge tools remained closed-source limiting transparency and accessibility. Recognizing this gap, Alibaba Group introduced Wan, ...
#Alibaba #Wan2.1 #VideoGeneration #GenerativeAI #OpenSource #ArtificialIntelligence
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π€π§ DeepSeek-OCR: Redefining Document Understanding Through Optical Context Compression
ποΈ 21 Oct 2025
π AI News & Trends
In the age of large language models (LLMs) and vision-language models (VLMs), handling long and complex textual data efficiently remains a massive challenge. Traditional models struggle with processing extended contexts because the computational cost increases quadratically with sequence length. To overcome this, researchers from DeepSeek-AI have introduced a groundbreaking approach β DeepSeek-OCR, a model that ...
ποΈ 21 Oct 2025
π AI News & Trends
In the age of large language models (LLMs) and vision-language models (VLMs), handling long and complex textual data efficiently remains a massive challenge. Traditional models struggle with processing extended contexts because the computational cost increases quadratically with sequence length. To overcome this, researchers from DeepSeek-AI have introduced a groundbreaking approach β DeepSeek-OCR, a model that ...
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π€π§ The Art of Scaling Reinforcement Learning Compute for LLMs: Top Insights from Meta, UT Austin and Harvard University
ποΈ 21 Oct 2025
π AI News & Trends
As Large Language Models (LLMs) continue to redefine artificial intelligence, a new research breakthrough has emerged from Meta, The University of Texas at Austin, University College London, UC Berkeley, Harvard University and Periodic Labs. Their paper, titled βThe Art of Scaling Reinforcement Learning Compute for LLMs,β introduces a transformative framework for understanding how reinforcement learning ...
#ReinforcementLearning #LLMs #AIResearch #Meta #UTAustin #HarvardUniversity
ποΈ 21 Oct 2025
π AI News & Trends
As Large Language Models (LLMs) continue to redefine artificial intelligence, a new research breakthrough has emerged from Meta, The University of Texas at Austin, University College London, UC Berkeley, Harvard University and Periodic Labs. Their paper, titled βThe Art of Scaling Reinforcement Learning Compute for LLMs,β introduces a transformative framework for understanding how reinforcement learning ...
#ReinforcementLearning #LLMs #AIResearch #Meta #UTAustin #HarvardUniversity
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π€π§ Master Machine Learning with Stanfordβs CS229 Cheatsheets: The Ultimate Learning Resource
ποΈ 21 Oct 2025
π AI News & Trends
Machine learning is one of the most transformative fields in technology today. From powering recommendation systems to enabling self-driving cars, machine learning is at the core of modern artificial intelligence. However, mastering its vast concepts, equations and algorithms can be overwhelming especially for beginners and busy professionals. Thatβs where the Stanford CS229 Machine Learning Cheatsheets ...
ποΈ 21 Oct 2025
π AI News & Trends
Machine learning is one of the most transformative fields in technology today. From powering recommendation systems to enabling self-driving cars, machine learning is at the core of modern artificial intelligence. However, mastering its vast concepts, equations and algorithms can be overwhelming especially for beginners and busy professionals. Thatβs where the Stanford CS229 Machine Learning Cheatsheets ...
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OpenAI has released Atlas, a new AI-powered browser that can remember context and operates in Agent Mode.
What is known:
Atlas is fully integrated with ChatGPT and uses ChatGPT Search under the hood.
In Agent Mode, the browser can navigate websites, click, search, and perform actions on its own.
You can open an unlimited number of tabs with agents β each lives its own life and solves separate tasks.
Atlas is already available to Free, Plus, Pro, Go, and Business users worldwide.
Enterprise and Education users can access the beta if their admin enables it. Versions for Windows, iOS, and Android are also in development.
You can download it at chatgpt.com/atlas
We hope Windows users will soon be able to experience this new browser in action.π
π @codeprogrammer
What is known:
Atlas is fully integrated with ChatGPT and uses ChatGPT Search under the hood.
In Agent Mode, the browser can navigate websites, click, search, and perform actions on its own.
You can open an unlimited number of tabs with agents β each lives its own life and solves separate tasks.
Atlas is already available to Free, Plus, Pro, Go, and Business users worldwide.
Enterprise and Education users can access the beta if their admin enables it. Versions for Windows, iOS, and Android are also in development.
You can download it at chatgpt.com/atlas
We hope Windows users will soon be able to experience this new browser in action.
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π€π§ Mastering Large Language Models: Top #1 Complete Guide to Maxime Labonneβs LLM Course
ποΈ 22 Oct 2025
π AI News & Trends
In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) have become the foundation of modern AI innovation powering tools like ChatGPT, Claude, Gemini and countless enterprise AI applications. However, building, fine-tuning and deploying these models require deep technical understanding and hands-on expertise. To bridge this knowledge gap, Maxime Labonne, a leading AI ...
#LLM #ArtificialIntelligence #MachineLearning #DeepLearning #AIEngineering #LargeLanguageModels
ποΈ 22 Oct 2025
π AI News & Trends
In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) have become the foundation of modern AI innovation powering tools like ChatGPT, Claude, Gemini and countless enterprise AI applications. However, building, fine-tuning and deploying these models require deep technical understanding and hands-on expertise. To bridge this knowledge gap, Maxime Labonne, a leading AI ...
#LLM #ArtificialIntelligence #MachineLearning #DeepLearning #AIEngineering #LargeLanguageModels
β€2π1
π€π§ The Ultimate #1 Collection of AI Books In Awesome-AI-Books Repository
ποΈ 22 Oct 2025
π AI News & Trends
Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the 21st century. From powering self-driving cars to enabling advanced conversational AI like ChatGPT, AI is redefining how humans interact with machines. However, mastering AI requires a strong foundation in theory, mathematics, programming and hands-on experimentation. For enthusiasts, students and professionals seeking ...
#ArtificialIntelligence #AIBooks #MachineLearning #DeepLearning #AIResources #TechBooks
ποΈ 22 Oct 2025
π AI News & Trends
Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the 21st century. From powering self-driving cars to enabling advanced conversational AI like ChatGPT, AI is redefining how humans interact with machines. However, mastering AI requires a strong foundation in theory, mathematics, programming and hands-on experimentation. For enthusiasts, students and professionals seeking ...
#ArtificialIntelligence #AIBooks #MachineLearning #DeepLearning #AIResources #TechBooks
β€2π₯1
π€π§ LandingAI ADE Python SDK: Streamlining AI-Powered Document Understanding
ποΈ 22 Oct 2025
π AI News & Trends
In the age of AI automation, extracting structured data from documents has become a key part of many business workflows. From invoices and contracts to identity documents and research papers, organizations are relying on AI models to interpret and process information accurately. LandingAIβs ADE Python SDK β an official API client for the LandingAI ADE ...
#AIPowered #DocumentUnderstanding #LandingAI #ADEPythonSDK #AIAutomation #DataExtraction
ποΈ 22 Oct 2025
π AI News & Trends
In the age of AI automation, extracting structured data from documents has become a key part of many business workflows. From invoices and contracts to identity documents and research papers, organizations are relying on AI models to interpret and process information accurately. LandingAIβs ADE Python SDK β an official API client for the LandingAI ADE ...
#AIPowered #DocumentUnderstanding #LandingAI #ADEPythonSDK #AIAutomation #DataExtraction
β€3
A collection of basic techniques for working with tensors in PyTorch β for those who are starting to get acquainted with the framework and want to quickly master its fundamentals.
What's inside:
A good starting material to understand the mechanics of tensors before moving on to models and training.βΆοΈ What tensors are and why they are neededβΆοΈ Tensor initialization: zeros, ones, random, similar sizeβΆοΈ Type conversion and switching between NumPy and PyTorchβΆοΈ Arithmetic, logical operations, tensor comparisonβΆοΈ Matrix multiplication and batch computationsβΆοΈ Broadcasting, view(), reshape(), changing dimensionsβΆοΈ Indexing and slicing: how to access parts of a tensorβΆοΈ Notebook with code examples
tags: #useful
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π€π§ Master Machine Learning: Explore the Ultimate βMachine-Learning-Tutorialsβ Repository
ποΈ 23 Oct 2025
π AI News & Trends
In todayβs data-driven world, Machine Learning (ML) has become the cornerstone of modern technology from intelligent chatbots to predictive analytics and recommendation systems. However, mastering ML isnβt just about coding, it requires a structured understanding of algorithms, statistics, optimization techniques and real-world problem-solving. Thatβs where Ujjwal Karnβs Machine-Learning-Tutorials GitHub repository stands out. This open-source, topic-wise ...
#MachineLearning #MLTutorials #ArtificialIntelligence #DataScience #OpenSource #AIEducation
ποΈ 23 Oct 2025
π AI News & Trends
In todayβs data-driven world, Machine Learning (ML) has become the cornerstone of modern technology from intelligent chatbots to predictive analytics and recommendation systems. However, mastering ML isnβt just about coding, it requires a structured understanding of algorithms, statistics, optimization techniques and real-world problem-solving. Thatβs where Ujjwal Karnβs Machine-Learning-Tutorials GitHub repository stands out. This open-source, topic-wise ...
#MachineLearning #MLTutorials #ArtificialIntelligence #DataScience #OpenSource #AIEducation
β€4π1
β¨ Topic: Flask Tutorials β¨
π Explore Flask, a popular Python web framework, through these tutorials. Learn key aspects of Flask development. With this knowledge, you'll be able to create robust and scalable web applications using Flask.
π·οΈ #26_resources
π Explore Flask, a popular Python web framework, through these tutorials. Learn key aspects of Flask development. With this knowledge, you'll be able to create robust and scalable web applications using Flask.
π·οΈ #26_resources
β€5
π€π§ LangChain: The Ultimate Framework for Building Reliable AI Agents and LLM Applications
ποΈ 24 Oct 2025
π AI News & Trends
As artificial intelligence continues to transform industries, developers are racing to build smarter, more adaptive applications powered by Large Language Models (LLMs). Yet, one major challenge remains how to make these models interact intelligently with real-world data and external systems in a scalable, reliable way. Enter LangChain, an open-source framework designed to make LLM-powered application ...
#LangChain #AI #LLM #ArtificialIntelligence #OpenSource #AIAgents
ποΈ 24 Oct 2025
π AI News & Trends
As artificial intelligence continues to transform industries, developers are racing to build smarter, more adaptive applications powered by Large Language Models (LLMs). Yet, one major challenge remains how to make these models interact intelligently with real-world data and external systems in a scalable, reliable way. Enter LangChain, an open-source framework designed to make LLM-powered application ...
#LangChain #AI #LLM #ArtificialIntelligence #OpenSource #AIAgents
β€3π2
In Python, you can unpack sequences using *, to work with a variable number of elements. The * can be placed anywhere and it will collect all the extra elements into a separate variable.
π @DataScience4
a, b, c = 10, 2, 3 # Standard unpacking
a, *b = 10, 2, 3 # b = [2, 3]
a, *b, c = 10, 2, 3, 4 # b = [2, 3]
*a, b, c = 10, 2, 3, 4 # a = [10, 2]
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Forwarded from Data Science Jupyter Notebooks
π₯ Trending Repository: awesome-system-design-resources
π Description: Learn System Design concepts and prepare for interviews using free resources.
π Repository URL: https://github.com/ashishps1/awesome-system-design-resources
π Website: https://blog.algomaster.io
π Readme: https://github.com/ashishps1/awesome-system-design-resources#readme
π Statistics:
π Stars: 26.9K stars
π Watchers: 361
π΄ Forks: 6.3K forks
π» Programming Languages: Java - Python
π·οΈ Related Topics:
==================================
π§ By: https://www.tgoop.com/DataScienceM
π Description: Learn System Design concepts and prepare for interviews using free resources.
π Repository URL: https://github.com/ashishps1/awesome-system-design-resources
π Website: https://blog.algomaster.io
π Readme: https://github.com/ashishps1/awesome-system-design-resources#readme
π Statistics:
π Stars: 26.9K stars
π Watchers: 361
π΄ Forks: 6.3K forks
π» Programming Languages: Java - Python
π·οΈ Related Topics:
#computer_science #distributed_systems #awesome #backend #scalability #interview #interview_questions #system_design #hld #high_level_design
==================================
π§ By: https://www.tgoop.com/DataScienceM
β€2π2
Forwarded from Data Science Jupyter Notebooks
π₯ Trending Repository: best-of-ml-python
π Description: π A ranked list of awesome machine learning Python libraries. Updated weekly.
π Repository URL: https://github.com/lukasmasuch/best-of-ml-python
π Website: https://ml-python.best-of.org
π Readme: https://github.com/lukasmasuch/best-of-ml-python#readme
π Statistics:
π Stars: 22.3K stars
π Watchers: 444
π΄ Forks: 3K forks
π» Programming Languages: Not available
π·οΈ Related Topics:
==================================
π§ By: https://www.tgoop.com/DataScienceM
π Description: π A ranked list of awesome machine learning Python libraries. Updated weekly.
π Repository URL: https://github.com/lukasmasuch/best-of-ml-python
π Website: https://ml-python.best-of.org
π Readme: https://github.com/lukasmasuch/best-of-ml-python#readme
π Statistics:
π Stars: 22.3K stars
π Watchers: 444
π΄ Forks: 3K forks
π» Programming Languages: Not available
π·οΈ Related Topics:
#python #nlp #data_science #machine_learning #deep_learning #tensorflow #scikit_learn #keras #ml #data_visualization #pytorch #transformer #data_analysis #gpt #automl #jax #data_visualizations #gpt_3 #chatgpt
==================================
π§ By: https://www.tgoop.com/DataScienceM
β€6
In Python, enhanced
#python #forloops #enumerate #bestpractices
βοΈ @DataScience4
for loops with enumerate() provide both the index and value of items in an iterable, making it ideal for tasks needing positional awareness without manual counters. This is more Pythonic and efficient than using range(len()) for list traversals.fruits = ['apple', 'banana', 'cherry']
for index, fruit in enumerate(fruits):
print(f"{index}: {fruit}")
# Output:
# 0: apple
# 1: banana
# 2: cherry
# With start offset:
for index, fruit in enumerate(fruits, start=1):
print(f"{index}: {fruit}")
# 1: apple
# 2: banana
# 3: cherry
#python #forloops #enumerate #bestpractices
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In Python, lists are versatile mutable sequences with built-in methods for adding, removing, searching, sorting, and moreβcovering all common scenarios like dynamic data manipulation, queues, or stacks. Below is a complete breakdown of all list methods, each with syntax, an example, and output, plus key built-in functions for comprehensive use.
π Adding Elements
β¦ append(x): Adds a single element to the end.
β¦ extend(iterable): Adds all elements from an iterable to the end.
β¦ insert(i, x): Inserts x at index i (shifts elements right).
π Removing Elements
β¦ remove(x): Removes the first occurrence of x (raises ValueError if not found).
β¦ pop(i=-1): Removes and returns the element at index i (default: last).
β¦ clear(): Removes all elements.
π Searching and Counting
β¦ count(x): Returns the number of occurrences of x.
β¦ index(x[, start[, end]]): Returns the lowest index of x in the slice (raises ValueError if not found).
π Ordering and Copying
β¦ sort(key=None, reverse=False): Sorts the list in place (ascending by default; stable sort).
β¦ reverse(): Reverses the elements in place.
β¦ copy(): Returns a shallow copy of the list.
π Built-in Functions for Lists (Common Cases)
β¦ len(lst): Returns the number of elements.
β¦ min(lst): Returns the smallest element (raises ValueError if empty).
β¦ max(lst): Returns the largest element.
β¦ sum(lst[, start=0]): Sums the elements (start adds an offset).
β¦ sorted(lst, key=None, reverse=False): Returns a new sorted list (non-destructive).
These cover all standard operations (O(1) for append/pop from end, O(n) for most others). Use slicing
#python #lists #datastructures #methods #examples #programming
β @DataScience4
π Adding Elements
β¦ append(x): Adds a single element to the end.
lst = [1, 2]
lst.append(3)
print(lst) # Output: [1, 2, 3]
β¦ extend(iterable): Adds all elements from an iterable to the end.
lst = [1, 2]
lst.extend([3, 4])
print(lst) # Output: [1, 2, 3, 4]
β¦ insert(i, x): Inserts x at index i (shifts elements right).
lst = [1, 3]
lst.insert(1, 2)
print(lst) # Output: [1, 2, 3]
π Removing Elements
β¦ remove(x): Removes the first occurrence of x (raises ValueError if not found).
lst = [1, 2, 2]
lst.remove(2)
print(lst) # Output: [1, 2]
β¦ pop(i=-1): Removes and returns the element at index i (default: last).
lst = [1, 2, 3]
item = lst.pop(1)
print(item, lst) # Output: 2 [1, 3]
β¦ clear(): Removes all elements.
lst = [1, 2, 3]
lst.clear()
print(lst) # Output: []
π Searching and Counting
β¦ count(x): Returns the number of occurrences of x.
lst = [1, 2, 2, 3]
print(lst.count(2)) # Output: 2
β¦ index(x[, start[, end]]): Returns the lowest index of x in the slice (raises ValueError if not found).
lst = [1, 2, 3, 2]
print(lst.index(2)) # Output: 1
π Ordering and Copying
β¦ sort(key=None, reverse=False): Sorts the list in place (ascending by default; stable sort).
lst = [3, 1, 2]
lst.sort()
print(lst) # Output: [1, 2, 3]
β¦ reverse(): Reverses the elements in place.
lst = [1, 2, 3]
lst.reverse()
print(lst) # Output: [3, 2, 1]
β¦ copy(): Returns a shallow copy of the list.
lst = [1, 2]
new_lst = lst.copy()
print(new_lst) # Output: [1, 2]
π Built-in Functions for Lists (Common Cases)
β¦ len(lst): Returns the number of elements.
lst = [1, 2, 3]
print(len(lst)) # Output: 3
β¦ min(lst): Returns the smallest element (raises ValueError if empty).
lst = [3, 1, 2]
print(min(lst)) # Output: 1
β¦ max(lst): Returns the largest element.
lst = [3, 1, 2]
print(max(lst)) # Output: 3
β¦ sum(lst[, start=0]): Sums the elements (start adds an offset).
lst = [1, 2, 3]
print(sum(lst)) # Output: 6
β¦ sorted(lst, key=None, reverse=False): Returns a new sorted list (non-destructive).
lst = [3, 1, 2]
print(sorted(lst)) # Output: [1, 2, 3]
These cover all standard operations (O(1) for append/pop from end, O(n) for most others). Use slicing
lst[start:end:step] for advanced extraction, like lst[1:3] outputs ``.#python #lists #datastructures #methods #examples #programming
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In Python, handling CSV files is straightforward using the built-in
#python #csv #pandas #datahandling #fileio #interviewtips
π @DataScience4
csv module for reading and writing tabular data, or pandas for advanced analysisβessential for data processing tasks like importing/exporting datasets in interviews.# Reading CSV with csv module (basic)
import csv
with open('data.csv', 'r') as file:
reader = csv.reader(file)
data = list(reader) # data = [['Name', 'Age'], ['Alice', '30'], ['Bob', '25']]
# Writing CSV with csv module
import csv
with open('output.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerow(['Name', 'Age']) # Header
writer.writerows([['Alice', 30], ['Bob', 25]]) # Data rows
# Advanced: Reading with pandas (handles headers, missing values)
import pandas as pd
df = pd.read_csv('data.csv') # df = DataFrame with columns 'Name', 'Age'
print(df.head()) # Output: First 5 rows preview
# Writing with pandas
df.to_csv('output.csv', index=False) # Saves without row indices
#python #csv #pandas #datahandling #fileio #interviewtips
π @DataScience4
β€2π2
The course gathers up-to-date information on #Python programming and creating advanced AI assistants based on it.
β’ Content: The course includes 9 lectures, supplemented with video materials, detailed presentations, and code examples. Learning to develop AI agents is accessible even for coding beginners.
β’ Topics: The lectures cover topics such as #RAG (Retrieval-Augmented Generation), embeddings, #agents, and the #MCP protocol.
The perfect weekend plan is to dive deep into #AI!
https://github.com/orgs/azure-ai-foundry/discussions/166
https://www.tgoop.com/CodeProgrammer
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