AI and Machine Learning
Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses
Ko'proq ko'rsatish📈 Telegram kanali AI and Machine Learning analitikasi
AI and Machine Learning (@machine_learning_courses) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 94 077 obunachidan iborat bo'lib, Taʼlim toifasida 1 547-o'rinni va Hindiston mintaqasida 3 005-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 94 077 obunachiga ega bo‘ldi.
26 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 965 ga, so‘nggi 24 soatda esa 37 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 6.79% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.34% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 6 384 marta ko‘riladi; birinchi sutkada odatda 2 203 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 9 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent learning, llm, linkedin, linux, udemy kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more!
Buy ads: https://telega.io/c/machine_learning_courses”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 27 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
Neural networks are computational models inspired by the human brain's structure and function. They consist of interconnected layers of nodes (or neurons) that process data and learn patterns. Here's a brief overview:1. Structure: Neural networks have three main types of layers: - Input layer: Receives the initial data. - Hidden layers: Intermediate layers that process the input data through weighted connections. - Output layer: Produces the final output or prediction. 2. Neurons and Connections: Each neuron receives input from several other neurons, processes this input through a weighted sum, and applies an activation function to determine the output. This output is then passed to the neurons in the next layer. 3. Training: Neural networks learn by adjusting the weights of the connections between neurons using a process called backpropagation, which involves: - Forward pass: Calculating the output based on current weights. - Loss calculation: Comparing the output to the actual result using a loss function. - Backward pass: Adjusting the weights to minimize the loss using optimization algorithms like gradient descent. 4. Activation Functions: Functions like ReLU, Sigmoid, or Tanh are used to introduce non-linearity into the network, enabling it to learn complex patterns. 5. Applications: Neural networks are used in various fields, including image and speech recognition, natural language processing, and game playing, among others. Overall, neural networks are powerful tools for modeling and solving complex problems by learning from data. ENJOY LEARNING 👍👍
🌀 Learn how to transcribe audio from video by integrating Node.js applications with the Google AI Speech-to-Text API.📗 Topics: Machine Transcription, Artificial Intelligence, Node.js 📤 Join Machine Learning and Artificial intelligence for more courses
🌀 Learn about the modeling techniques and experimental designs that allow you to establish causal inference, and how to use them.📗 Topics: Causal Inference, Machine Learning, Artificial Intelligence 📤 Join Machine Learning and Artificial intelligence for more courses
🌀 Learn how to streamline software development workflows using AI pair programming with GitHub Copilot X.📗 Topics: Pair Programming, GitHub Copilot, Artificial Intelligence 📤 Join Machine Learning and Artificial intelligence for more courses
🌀 Learn best practices for how to produce explainable AI and interpretable machine learning solutions.📗 Topics: Machine Learning, Artificial Intelligence 📤 Join Machine Learning and Artificial intelligence for more courses
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