Artificial Intelligence & ChatGPT Prompts
๐Unlock Your Coding Potential with ChatGPT ๐ Your Ultimate Guide to Ace Coding Interviews! ๐ป Coding tips, practice questions, and expert advice to land your dream tech job. For Promotions: @love_data
Ko'proq ko'rsatish๐ Telegram kanali Artificial Intelligence & ChatGPT Prompts analitikasi
Artificial Intelligence & ChatGPT Prompts (@curiousprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 42 105 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 235-o'rinni va Hindiston mintaqasida 9 556-o'rinni egallagan.
๐ Auditoriya koโrsatkichlari va dinamika
ะฝะตะฒัะดะพะผะพ sanasidan buyon loyiha tez oโsib, 42 105 obunachiga ega boโldi.
11 Iyun, 2026 dagi oxirgi maโlumotlarga koโra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 171 ga, soโnggi 24 soatda esa -2 ga oโzgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya oโrtacha 2.47% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 0.74% ini tashkil etuvchi reaksiyalarni toโplaydi.
- Post qamrovi: Har bir post oโrtacha 1 040 marta koโriladi; birinchi sutkada odatda 311 ta koโrish yigโiladi.
- Reaksiyalar va oโzaro taโsir: Auditoriya faol: har bir postga oโrtacha 3 ta reaksiya keladi.
- Tematik yoโnalishlar: Kontent learning, algorithm, detection, llm, pattern kabi asosiy mavzularga jamlangan.
๐ Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taโriflaydi:
โ๐Unlock Your Coding Potential with ChatGPT
๐ Your Ultimate Guide to Ace Coding Interviews!
๐ป Coding tips, practice questions, and expert advice to land your dream tech job.
For Promotions: @love_dataโ
Yuqori yangilanish chastotasi (oxirgi maโlumot 12 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boโlib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim taโsir nuqtasiga aylantirishini koโrsatadi.
-- iv. Statistics
| |
| |-- b. Programming
| | |-- i. Python
| | | |-- 1. Syntax and Basic Concepts
| | | |-- 2. Data Structures
| | | |-- 3. Control Structures
| | | |-- 4. Functions
| | | -- 5. Object-Oriented Programming
| | |
| | -- ii. R (optional, based on preference)
| |
| |-- c. Data Manipulation
| | |-- i. Numpy (Python)
| | |-- ii. Pandas (Python)
| | -- iii. Dplyr (R)
| |
| -- d. Data Visualization
| |-- i. Matplotlib (Python)
| |-- ii. Seaborn (Python)
| -- iii. ggplot2 (R)
|
|-- 2. Data Exploration and Preprocessing
| |-- a. Exploratory Data Analysis (EDA)
| |-- b. Feature Engineering
| |-- c. Data Cleaning
| |-- d. Handling Missing Data
| -- e. Data Scaling and Normalization
|
|-- 3. Machine Learning
| |-- a. Supervised Learning
| | |-- i. Regression
| | | |-- 1. Linear Regression
| | | -- 2. Polynomial Regression
| | |
| | -- ii. Classification
| | |-- 1. Logistic Regression
| | |-- 2. k-Nearest Neighbors
| | |-- 3. Support Vector Machines
| | |-- 4. Decision Trees
| | -- 5. Random Forest
| |
| |-- b. Unsupervised Learning
| | |-- i. Clustering
| | | |-- 1. K-means
| | | |-- 2. DBSCAN
| | | -- 3. Hierarchical Clustering
| | |
| | -- ii. Dimensionality Reduction
| | |-- 1. Principal Component Analysis (PCA)
| | |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE)
| | -- 3. Linear Discriminant Analysis (LDA)
| |
| |-- c. Reinforcement Learning
| |-- d. Model Evaluation and Validation
| | |-- i. Cross-validation
| | |-- ii. Hyperparameter Tuning
| | -- iii. Model Selection
| |
| -- e. ML Libraries and Frameworks
| |-- i. Scikit-learn (Python)
| |-- ii. TensorFlow (Python)
| |-- iii. Keras (Python)
| -- iv. PyTorch (Python)
|
|-- 4. Deep Learning
| |-- a. Neural Networks
| | |-- i. Perceptron
| | -- ii. Multi-Layer Perceptron
| |
| |-- b. Convolutional Neural Networks (CNNs)
| | |-- i. Image Classification
| | |-- ii. Object Detection
| | -- iii. Image Segmentation
| |
| |-- c. Recurrent Neural Networks (RNNs)
| | |-- i. Sequence-to-Sequence Models
| | |-- ii. Text Classification
| | -- iii. Sentiment Analysis
| |
| |-- d. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
| | |-- i. Time Series Forecasting
| | -- ii. Language Modeling
| |
| -- e. Generative Adversarial Networks (GANs)
| |-- i. Image Synthesis
| |-- ii. Style Transfer
| -- iii. Data Augmentation
|
|-- 5. Big Data Technologies
| |-- a. Hadoop
| | |-- i. HDFS
| | -- ii. MapReduce
| |
| |-- b. Spark
| | |-- i. RDDs
| | |-- ii. DataFrames
| | -- iii. MLlib
| |
| -- c. NoSQL Databases
| |-- i. MongoDB
| |-- ii. Cassandra
| |-- iii. HBase
| -- iv. Couchbase
|
|-- 6. Data Visualization and Reporting
| |-- a. Dashboarding Tools
| | |-- i. Tableau
| | |-- ii. Power BI
| | |-- iii. Dash (Python)
| | -- iv. Shiny (R)
| |
| |-- b. Storytelling with Data
| -- c. Effective Communication
|
|-- 7. Domain Knowledge and Soft Skills
| |-- a. Industry-specific Knowledge
| |-- b. Problem-solving
| |-- c. Communication Skills
| |-- d. Time Management
| -- e. Teamwork
|
-- 8. Staying Updated and Continuous Learning
|-- a. Online Courses
|-- b. Books and Research Papers
|-- c. Blogs and Podcasts
|-- d. Conferences and Workshops
`-- e. Networking and Community EngagementArtificial neural networks (ANN) give machines the ability to process data similar to the human brain and make decisions or take actions based on the data. While thereโs still more to develop before machines have similar imaginations and reasoning power as humans, ANNs help machines complete and learn from the tasks they perform.โถ๏ธContent: โฟโฟโฟโฟโฟโฟโฟโฟโฟโฟโฟโฟ 001 What is Deep Learning 002 Plan of Attack 003 The Neuron 004 The Activation Function 005 How do Neural Networks work 006 How do Neural Networks learn 007 Gradient Descent 008 Stochastic Gradient Descent 009 Back propagation โฟโฟโฟโฟโฟโฟโฟโฟโฟโฟโฟโฟ ๐๐ Deep Learning Complete Course
Hi [Name],
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Best regards,
[Your Full Name]
[Your Email Address]
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