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Deep Learning roadmap. 𝗣𝗵𝗮𝘀𝗲 𝟭: 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 (𝗪𝗲𝗲𝗸 𝟭-𝟮) ● Understand perceptrons, sigmoid, cost functions, gradient descent, and derivatives ● Implement binary logistic regression using NumPy 𝗣𝗵𝗮𝘀𝗲 𝟮: 𝗦𝗵𝗮𝗹𝗹𝗼𝘄 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗪𝗲𝗲𝗸 𝟯-𝟰) ● Build a neural net with one hidden layer ● Compare activation functions ● Train your model to classify simple images 𝗣𝗵𝗮𝘀𝗲 𝟯: 𝗗𝗲𝗲𝗽 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗪𝗲𝗲𝗸 𝟱-𝟲) ● Forward and backward propagation through multiple layers ● Parameter initialization and tuning 𝗣𝗵𝗮𝘀𝗲 𝟰: 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 & 𝗥𝗲𝗴𝘂𝗹𝗮𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗪𝗲𝗲𝗸 𝟳-𝟴) ● Learn mini-batch gradient descent, RMSProp, and Adam ● Apply L2 and Dropout regularization to avoid overfitting 𝗣𝗵𝗮𝘀𝗲 𝟱: 𝗧𝗲𝗻𝘀𝗼𝗿𝗙𝗹𝗼𝘄 & 𝗥𝗲𝗮𝗹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 (𝗪𝗲𝗲𝗸 𝟵-𝟭𝟬) ● Build models using TensorFlow and Keras ● Normalize data, tune hyperparameters, and visualize metrics 𝗣𝗵𝗮𝘀𝗲 𝟲: 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 & 𝗖𝗮𝗿𝗲𝗲𝗿 𝗣𝗿𝗲𝗽 (𝗪𝗲𝗲𝗸 𝟭𝟭-𝟭𝟮) ● Work on image recognition, text classification, and real datasets ● Prepare for interviews with hands-on projects and GitHub repo

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Python Roadmap | |-- Fundamentals | |-- Basics of Programming | | |-- Introduction to Python | | |-- Setting Up Development Environment (IDE: PyCharm, VSCode, etc.) | | | |-- Syntax and Structure | | |-- Basic Syntax | | |-- Variables and Data Types | | |-- Operators and Expressions | |-- Control Structures | |-- Conditional Statements | | |-- If-Else Statements | | |-- Elif Statements | | | |-- Loops | | |-- For Loop | | |-- While Loop | | | |-- Exception Handling | | |-- Try-Except Block | | |-- Finally Block | | |-- Raise and Custom Exceptions | |-- Functions and Modules | |-- Defining Functions | | |-- Function Syntax | | |-- Parameters and Arguments | | |-- Return Statement | | | |-- Lambda Functions | | |-- Syntax and Usage | | | |-- Modules and Packages | | |-- Importing Modules | | |-- Creating and Using Packages | |-- Object-Oriented Programming (OOP) | |-- Basics of OOP | | |-- Classes and Objects | | |-- Methods and Constructors | | | |-- Inheritance | | |-- Single and Multiple Inheritance | | |-- Method Overriding | | | |-- Polymorphism | | |-- Method Overloading (using default arguments) | | |-- Operator Overloading | | | |-- Encapsulation | | |-- Access Modifiers (Public, Private, Protected) | | |-- Getters and Setters | | | |-- Abstraction | | |-- Abstract Base Classes | | |-- Interfaces (using ABC module) | |-- Advanced Python | |-- File Handling | | |-- Reading and Writing Files | | |-- Working with CSV and JSON Files | | | |-- Iterators and Generators | | |-- Creating Iterators | | |-- Using Generators and Yield Statement | | | |-- Decorators | | |-- Function Decorators | | |-- Class Decorators | |-- Data Structures | |-- Lists | | |-- List Comprehensions | | |-- Common List Methods | | | |-- Tuples | | |-- Immutable Sequences | | | |-- Dictionaries | | |-- Dictionary Comprehensions | | |-- Common Dictionary Methods | | | |-- Sets | | |-- Set Operations | | |-- Set Comprehensions | |-- Libraries and Frameworks | |-- Data Science | | |-- NumPy | | |-- Pandas | | |-- Matplotlib | | |-- Seaborn | | |-- SciPy | | | |-- Web Development | | |-- Flask | | |-- Django | | | |-- Automation | | |-- Selenium | | |-- BeautifulSoup | | |-- Scrapy | |-- Testing in Python | |-- Unit Testing | | |-- Unittest | | |-- PyTest | | | |-- Mocking | | |-- unittest.mock | | |-- Using Mocks and Patches | |-- Deployment and DevOps | |-- Containers and Microservices | | |-- Docker (Dockerfile, Image Creation, Container Management) | | |-- Kubernetes (Pods, Services, Deployments, Managing Python Applications on Kubernetes) | |-- Best Practices and Advanced Topics | |-- Code Style | | |-- PEP 8 Guidelines | | |-- Code Linters (Pylint, Flake8) | | | |-- Performance Optimization | | |-- Profiling and Benchmarking | | |-- Using Cython and Numba | | | |-- Concurrency and Parallelism | | |-- Threading | | |-- Multiprocessing | | |-- Asyncio | |-- Building and Distributing Packages | |-- Creating Packages | | |-- setuptools | | |-- Creating environment setup | | | |-- Publishing Packages | | |-- PyPI | | |-- Versioning and Documentation Best Resource to learn Python Python Interview Questions with Answers Freecodecamp Python ML Course with FREE Certificate Python for Data Analysis Python course for beginners by Microsoft Scientific Computing with Python Python course by Google Python Free Resources Please give us credits while sharing: -> https://t.me/free4unow_backup ENJOY LEARNING 👍👍

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🔰 Deep Python Roadmap for Beginners 🐍 Setup & Installation 🖥⚙️ • Install Python, choose an IDE (VS Code, PyCharm) • Set up virtual environments for project isolation 🌎 Basic Syntax & Data Types 📝🔢 • Learn variables, numbers, strings, booleans • Understand comments, basic input/output, and simple expressions ✍️ Control Flow & Loops 🔄🔀 • Master conditionals (if, elif, else) • Practice loops (for, while) and use control statements like break and continue 👮 Functions & Scope ⚙️🎯 • Define functions with def and learn about parameters and return values • Explore lambda functions, recursion, and variable scope 📜 Data Structures 📊📚 • Work with lists, tuples, sets, and dictionaries • Learn list comprehensions and built-in methods for data manipulation ⚙️ Object-Oriented Programming (OOP) 🏗👩‍💻 • Understand classes, objects, and methods • Dive into inheritance, polymorphism, and encapsulation 🔍 React "❤️" for Part 2