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Human Activity Recognition using TensorFlow (CNN + LSTM) | 2 Methods

🖥 By: https://t.me/Python53

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📺 12 comprehensive playlists to master
⬅️ machine learning, deep learning, and GenAI!


👨🏻‍💻 Each playlist is designed to be simple and understandable for beginners, and then gradually dive deeper into the topics.


😉 Machine Learning Basics (39 videos)

😉 Python for ML (9 videos)

😉 Optimization for ML (5 videos)

😉 Machine Learning with Practical Exercises (37 videos)

😉 Building Decision Trees from Scratch (13 videos)

😉 Building Neural Networks from Scratch (35 videos)

😉 Graph Neural Networks (6 videos)

😉 Computer Vision from Scratch (19 videos)

😉 Building LLM from Scratch (43 videos)

😉 Reasoning in LLMs from Scratch (22 videos)

😉 Building DeepSeek from Scratch (29 videos)

😉 Machine Learning in Production Environment (6 videos)



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🐍 How to run a FastAPI application in a Docker container

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🖥 What’s New in Python 3.14 and Why You Should Upgrade

Python 3.14 was released on October 7, 2025. This is a new stable release that includes changes in the language itself as well as improvements in implementation, standard library, debugging, and multithreading interaction.

Below is an overview of key innovations, their meaning, applicability, and possible pitfalls.

🟠Main Innovations (Release highlights)

- Deferred (lazy) evaluation of annotations — now annotations are not evaluated immediately, reducing overhead. 
- Support for multiple interpreters within a single process via a new module. 
- New syntax for template strings (t-strings), giving more control over static and interpolated parts. 
- More informative error messages (e.g., hints for typos in keywords). 
- Support for Zstandard compression format in the standard library. 
- Improved debugging and profiling capabilities, including attaching to a live process without stopping it. 
- Enhancements in asyncio — commands for visualizing and diagnosing tasks, wait stacks, and dependencies. 
- Reduced garbage collector (gc) pauses via incremental collection. 
- Syntax highlighting and module autocompletion in interactive mode (REPL) by default. 

🟠 Details and Examples

Deferred evaluation of annotations

Previously, annotations (for types, documentation, hints) could trigger computations immediately when defining a function or class. Now they are stored as "lazy" structures and evaluated on demand. This reduces overhead on code loading, especially if annotations are complex or contain many forward references.

There is a module annotationlib that allows programmatic inspection of annotations and choosing their retrieval format — strings, objects, or deferred references.

When this is especially helpful:
- large frameworks, code generation, ORM, libraries with many annotations; 
- speeding up imports at application startup; 
- reducing overhead when working with types.

What to check when migrating:
- code that uses __annotations__ directly may require adaptation; 
- ensure third-party libraries working with annotations support the new format.

Multiple interpreters (subinterpreters)

Now Python can run multiple independent interpreters inside a single process (module concurrent.interpreters). 

Advantages:
- isolation between interpreters (separate memory, separate GIL); 
- parallelism on multi-core systems; 
- less overhead than using multiprocessing. 

Limitations:
- not all C extensions support multi-interpretation; 
- communication between interpreters requires explicit channels (queues, messages). 

This provides a real opportunity for CPU task parallelization without launching separate processes.

Template string literals (t-strings)

A new syntactic feature — prefix t before a string, similar to f'...'. 
The result is a Template object that stores text and insertions separately.

variety = 'Stilton'
template = t'Try some {variety} cheese!'

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☄️ Top 12 YouTube Channels to Learn Python

💐 Python will include 57% of data scientist job ads in 2024 . It is still the number one programming language for data scientists.

✅ Now, if you are looking for the best resources to improve your Python skills, after searching and reviewing various resources, I have prepared a list of 12 top channels that provide first-class Python training, which can turn beginners into professional Python programmers. convert


🎬 Python Programmer channel
┤ 📈 211 videos / 465K SUB
┘ 🔴 Link: Python Programmer


🎬 Luke Barousse channel
┤ 📈 157 videos / 429K SUB
┘ 🔴 Link: Luke Barousse


🎬 codebasics channel
┤ 📈 837 videos / 990K SUB
┘ 🔴 link: codebasics


🎬 StatQuest channel with Josh Starmer
┤ 📈 271 videos / 1.14M SUB
┘ 🔴 Link: StatQuest with Josh Starmer


🎬 Sundas Khalid channel
┤ 📈 143 videos / 203K SUB
┘ 🔴 Link: Sundas Khalid


🎬 Shashank Kalanithi channel
┤ 📈 152 videos / 148K SUB
┘ 🔴 Link: Shashank Kalanithi


🎬 Programming with Mosh channel
┤ 📈 203 videos / 3.85M SUB
┘ 🔴 Link: Programming with Mosh


🎬 Corey Schafer channel
┤ 📈 233 videos / 129K SUB
┘ 🔴 Link: Corey Schafer


🎬 sentdex channel
┤ 📈 1254 videos / 1.3M SUB
┘ 🔴 link: sentdex


🎬 Patrick Loeber channel
┤ 📈 206 videos / 264K SUB
┘ 🔴 Link: Patrick Loeber


🎬 Socratica channel
┤ 📈 659 videos / 876K SUB
┘ 🔴 Link: Socratica


🎬 Tech With Tim channel
┤ 📈 983 videos / 1.48M SUB
┘ 🔴 Link: Tech With Tim

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🇸🇾 Course on Design Patterns in Python

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Free MIT course for beginners on the basics of computer science. These classic lectures cover concepts such as programming language design, abstraction, and recursion.

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There is a repository on GitHub free-programming-books that contains over 4000 free books, 2000 courses, and other useful programming resources

For convenient searching, you can use this tool

This project is a vivid example of the power of the open source community, which evolved from a clone of a StackOverflow list into one of the most popular on GitHub ✌️

🔸 English-language version of the resources

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Python Data Visualization: Create impactful visuals, animations and dashboards [EN]

What you will learn :

The Sneak Peek program provides early access to Pearson video courses, allowing subscribers to study materials as they are being developed. Content can be updated throughout the entire creation cycle, including editing and post-production.

⚡ tags: #python #datascience

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Python Data Visualization: Create impactful visuals, animations and dashboards [EN]

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🐍 10 Free Courses to Learn Python

👩🏻‍💻 These top-notch resources can take your #Python skills several levels higher. The best part is that they are all completely free!


1⃣ Comprehensive Python Course for Beginners

📃A complete video course that teaches Python from basic to advanced with clear and organized explanations.


2⃣ Intensive Python Training

📃A 4-hour intensive course, fast, focused, and to the point.


3⃣ Comprehensive Python Course

📃Training with lots of real examples and exercises.


4⃣ Introduction to Python

📃Learn the fundamentals with a focus on logic, clean coding, and solving real problems.


5⃣ Automate Daily Tasks with Python

📃Learn how to automate your daily project tasks with Python.


6⃣ Learn Python with Interactive Practice

📃Interactive lessons with real data and practical exercises.


7⃣ Scientific Computing with Python

📃Project-based, for those who want to work with data and scientific analysis.


8⃣ Step-by-Step Python Training

📃Step-by-step and short training for beginners with interactive exercises.


9⃣ Google's Python Class

📃A course by Google engineers with real exercises and professional tips.


1⃣ Introduction to Programming with Python

📃University-level content for conceptual learning and problem-solving with exercises and projects.

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