@phatchuyenbdstheometro: TÒA NHÀ VĂN PHÒNG 283/107–109 CÁCH MẠNG THÁNG 8 Tọa lạc trên trục Cách Mạng Tháng 8, khu vực Hòa Hưng, tài sản có vị trí thuận lợi khi nằm trên hành lang phát triển đô thị và giao thông quan trọng của TP.HCM. Theo thông tin thị trường, khu đất có diện tích khoảng 127,5 m², dạng góc 2 mặt tiền, kích thước khoảng 11 × 13 m; hiện trạng công trình gồm hầm và 7 tầng, tổng diện tích sàn được giới thiệu khoảng 877m2 Vì vậy, giá trị của tài sản nằm trước hết ở vị trí trên trục CMT8 và khả năng tiếp cận hệ thống Metro của TPHCM Liền kề KDC HÀ ĐÔ và một khu đô thị Kỳ Hoà sắp được triển khai nên giá trị tăng theo thời gian. ☎️ Phát Metro | 08 3727 1838 #phatbdstheometro #bannhagangametrohcm

Phát - BDS theo Metro
Phát - BDS theo Metro
Open In TikTok:
Region: VN
Thursday 08 October 2026 03:07:43 GMT
73
1
0
0

Music

Download

Comments

There are no more comments for this video.
To see more videos from user @phatchuyenbdstheometro, please go to the Tikwm homepage.

Other Videos

An Artificial Neural Network (ANN) is a Deep Learning model inspired by the structure of the human brain. It learns patterns from data to make predictions, classify information, and solve complex problems. 🎯 What is an ANN? ANNs consist of interconnected neurons (nodes) organized into layers. During training, the network learns by adjusting its weights to minimize prediction errors. 🏗️ ANN Workflow 1️⃣ Collect the Data 📂 Gather data from databases, APIs, CSV files, or sensors. 2️⃣ Preprocess the Data 🧹 ✅ Handle missing values ✅ Encode categorical variables ✅ Normalize or standardize features ✅ Split data into training and testing sets 3️⃣ Build the Neural Network 🧠 Create: 📥 Input Layer 🔄 Hidden Layer(s) 📤 Output Layer 4️⃣ Train the Model 🤖 The ANN learns patterns by: ✅ Forward Propagation ✅ Loss Calculation ✅ Backpropagation ✅ Weight Updates using an Optimizer 5️⃣ Evaluate the Model 📊 Measure performance using: 📈 Accuracy 🎯 Precision 📢 Recall ⚖️ F1-Score 📉 Loss 6️⃣ Make Predictions 🔮 Use the trained model to predict outcomes for new, unseen data. 🧩 Core ANN Concepts 🟢 Neurons 🔗 Weights & Biases ⚡ Activation Functions (ReLU, Sigmoid, Tanh, Softmax) 📉 Loss Functions 🚀 Optimizers (SGD, Adam, RMSprop) 🔁 Epochs 📦 Batch Size 📊 Learning Rate 🛠 Tech Stack 🐍 Python 🧠 TensorFlow 🤗 Keras 🔥 PyTorch 🐼 Pandas 🔢 NumPy 📈 Matplotlib 🤖 Scikit-learn 🌍 Real-World Applications 🏥 Disease Prediction 💳 Credit Card Fraud Detection 📧 Spam Email Classification 🏡 House Price Prediction 😊 Customer Churn Prediction 🛒 Product Recommendation 🎤 Speech Recognition 📈 Financial Forecasting 💼 Beginner ANN Project Ideas 🏦 Loan Approval Prediction 🩺 Diabetes Prediction 📉 Customer Churn Prediction 📧 Email Spam Detection 💰 Salary Prediction ❤️ Heart Disease Prediction 💡 Learning Roadmap 🐍 Python Basics ⬇️ 📊 NumPy & Pandas ⬇️ 📈 Statistics & Linear Algebra ⬇️ 🤖 Machine Learning Fundamentals ⬇️ 🧠 Artificial Neural Networks ⬇️ 👁️ Convolutional Neural Networks (CNNs) ⬇️ 🔄 Recurrent Neural Networks (RNNs) & Transformers 🚀 Artificial Neural Networks are the foundation of modern Deep Learning. Once you understand how an ANN learns from data, you’ll be ready to explore advanced models like CNNs, RNNs, LSTMs, and Transformers that power today’s AI systems. #ArtificialNeuralNetwork #ANN #DeepLearning                 #creatorsearchinsights #aiandjobs
An Artificial Neural Network (ANN) is a Deep Learning model inspired by the structure of the human brain. It learns patterns from data to make predictions, classify information, and solve complex problems. 🎯 What is an ANN? ANNs consist of interconnected neurons (nodes) organized into layers. During training, the network learns by adjusting its weights to minimize prediction errors. 🏗️ ANN Workflow 1️⃣ Collect the Data 📂 Gather data from databases, APIs, CSV files, or sensors. 2️⃣ Preprocess the Data 🧹 ✅ Handle missing values ✅ Encode categorical variables ✅ Normalize or standardize features ✅ Split data into training and testing sets 3️⃣ Build the Neural Network 🧠 Create: 📥 Input Layer 🔄 Hidden Layer(s) 📤 Output Layer 4️⃣ Train the Model 🤖 The ANN learns patterns by: ✅ Forward Propagation ✅ Loss Calculation ✅ Backpropagation ✅ Weight Updates using an Optimizer 5️⃣ Evaluate the Model 📊 Measure performance using: 📈 Accuracy 🎯 Precision 📢 Recall ⚖️ F1-Score 📉 Loss 6️⃣ Make Predictions 🔮 Use the trained model to predict outcomes for new, unseen data. 🧩 Core ANN Concepts 🟢 Neurons 🔗 Weights & Biases ⚡ Activation Functions (ReLU, Sigmoid, Tanh, Softmax) 📉 Loss Functions 🚀 Optimizers (SGD, Adam, RMSprop) 🔁 Epochs 📦 Batch Size 📊 Learning Rate 🛠 Tech Stack 🐍 Python 🧠 TensorFlow 🤗 Keras 🔥 PyTorch 🐼 Pandas 🔢 NumPy 📈 Matplotlib 🤖 Scikit-learn 🌍 Real-World Applications 🏥 Disease Prediction 💳 Credit Card Fraud Detection 📧 Spam Email Classification 🏡 House Price Prediction 😊 Customer Churn Prediction 🛒 Product Recommendation 🎤 Speech Recognition 📈 Financial Forecasting 💼 Beginner ANN Project Ideas 🏦 Loan Approval Prediction 🩺 Diabetes Prediction 📉 Customer Churn Prediction 📧 Email Spam Detection 💰 Salary Prediction ❤️ Heart Disease Prediction 💡 Learning Roadmap 🐍 Python Basics ⬇️ 📊 NumPy & Pandas ⬇️ 📈 Statistics & Linear Algebra ⬇️ 🤖 Machine Learning Fundamentals ⬇️ 🧠 Artificial Neural Networks ⬇️ 👁️ Convolutional Neural Networks (CNNs) ⬇️ 🔄 Recurrent Neural Networks (RNNs) & Transformers 🚀 Artificial Neural Networks are the foundation of modern Deep Learning. Once you understand how an ANN learns from data, you’ll be ready to explore advanced models like CNNs, RNNs, LSTMs, and Transformers that power today’s AI systems. #ArtificialNeuralNetwork #ANN #DeepLearning #creatorsearchinsights #aiandjobs

About