Neural network

I learned about the10 steps for building a Neural Network: 1. Install Tensorflow 2. Import/Load the dataset 3. Data Preprocessing (Missing values, Outliers, Scaling, Encoding) 4. Choose Features (X) and Label (Y) 5. Splitting the data (Into training and testing sets) 6. Standardizing the Data (Min-Max Scalar) 7. Build the Neural Network (Define Model type, Add Layers) 8. Compile the model (optimizer, loss function, Evaluation Metric) 9. Train the Model (epochs, batch size) 10. Evaluating the model

nural network

Done

Remember these 10 steps to start neural network (1) Install Tensorflow liberary (2) Load Dataset (3) Data Pre-porcessing (4) Select Features Target (5) Splitting the Dataset (6) Standardizing the Data (7) building the Nueral Network model (8) Compile the model (9) training the model (10) Evaluating the model

neural network

I learned in this video 1. Step: Install Tensorflow 2. Step: load the dataset 3. Step: Data Preprocessing 4. Step: Features and target choose X and y, 5. Steps: Split the dataset and divide the data into two sets for traning and testing, 6. Step: Standardizing the data this standardization process helps in speeding up the training process and improving performance. 7. Step: Building the Neural Network Model 8. Step: Compile the Model 9. Step: Traning the Model, and 10. Step: Evaluate the Model.

I learned in this lecture about 1. installing TensorFlow, 2. loading the datasets, 3. Data Pre-Processing, 4. Select Features and Target Choose X and Y, 5. Splitting the Dataset Divide the data into two sets for training and testing, 6. Standardizing the Data, 7. Building the Neural Network Model Define the Model, 8. Compile the Model Prepare the Model, 9. Training the Model, and 10. Evaluating the Model.t

AOA, I learned in this lecture how to create neural network in Python and steps of neural network areSTEP-1 Install TensorFlow: Tensorflow installed in Python environment. STEP-2 Load the Dataset: STEP-3 Data Pre-Processing Missing Value ( remove the missing values ) Outliers ( remove the outliers ) Scaling EncodingSTEP-4 Select Features and Target Choose X and ySTEP-5 Splitting the Dataset Divide the data into two sets for training and testing.STEP-6 Standardizing the Data This standardization process helps in speeding up the training process and improving performance.STEP-7 Building the Neural Network Model Define the Model Type:( use a sequential model in linear stack of layers ) And Define Layers: ( add one hidden layer with 10 neurons, and use the ‘ReLU’ activation function for non-linear processing. Then, add an output layer with 1 neuron, using the ‘Sigmoid’ activation function, suitable for binary classification)STEP-8 Compile the Model Prepare the model for training by sitting the optimizer (Adam), the loss function(binary_crossentropy), and the metric to evaluate(accuracy).STEP-9 Training The Model Train the model using the training data. We specify the number of epochs (iterations) and the batch size (number of samples per gradient update).STEP-10 Evaluating the Model Finally, assess the performance of the model on the test data to see how well it learned to predict the target variable.ALLAH PAK aap ko sahat o aafiyat wali lambi umar ata kray aor ap ko dono jahan ki bhalian naseeb farmaey aur aap ke walid-e-mohtram ko karwat karwat jannat ata farmaye,Ameen.