My Blog

Author: saqibkhan

  • Node.js vs AngularJS

    Node.js and AngularJS both are developed to build web applications using JavaScript, both follow the syntax of JavaScript but they are quite different in their architecture and working.

    Following is a list of big differences between them.

    Difference between Node.js and AngularJS

    IndexNode.jsAngularJS
    1)Node.js is a cross-platform run-time environment and run-time system for applications written in JavaScript languages. it is like java runtime environment (JRE) for java, adobe flash player for ActionScript, common language runtime (CLR) for .net programs, or android runtime (art) for android apps.AnglarJS is an open source web application development framework developed by Google.
    2)You have to install Node.js on your computer system to use it further for creating web or chat applications.You have to add the AngularJS file just like any other JavaScript file to use it in applications. It doesn?t need to be installed separately before using it in applications.
    3)Node.js supports non-blocking input output I/O and follows an event driven architecture. It is used to create real-time applications such as instant messaging or chat apps.AngularJS is completely written in JavaScript. It is mainly used to create single-page client side applications.
    4)Node.js is a platform built on the top of Google’s V8 JavaScript engine.AngularJS is an open source framework, follows the syntax of JavaScript and developed by Google.
    5)Node.js is written in C, C++ and JavaScript languages.AngularJS is written completely in JavaScript but it is different from other web application frameworks like jQuery.
    6)Node.js has a lot number of frameworks such as Express.js, Sails.js, Partial.js etc.AngularJS itself is a web application framework of JavaScript.

  • Hyperparameter Tuning with Keras Tuner

    Hyperparameter Tuning with Keras Tuner

    • Use Keras Tuner to find the optimal hyperparameters.pythonCopy codefrom kerastuner import HyperModel, RandomSearch class MyHyperModel(HyperModel): def build(self, hp): model = Sequential() model.add(Dense(units=hp.Int('units', min_value=32, max_value=512, step=32), activation='relu')) model.add(Dense(10, activation='softmax')) model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) return model tuner = RandomSearch(MyHyperModel(), objective='val_accuracy', max_trials=10) tuner.search(X_train, y_train, epochs=10, validation_data=(X_val, y_val))
    • Keras Tuner automates the search for optimal architecture and hyperparameters.

  • Node.js vs AngularJS

    Node.js and AngularJS both are developed to build web applications using JavaScript, both follow the syntax of JavaScript but they are quite different in their architecture and working.

    Following is a list of big differences between them.

    Difference between Node.js and AngularJS

    IndexNode.jsAngularJS
    1)Node.js is a cross-platform run-time environment and run-time system for applications written in JavaScript languages. it is like java runtime environment (JRE) for java, adobe flash player for ActionScript, common language runtime (CLR) for .net programs, or android runtime (art) for android apps.AnglarJS is an open source web application development framework developed by Google.
    2)You have to install Node.js on your computer system to use it further for creating web or chat applications.You have to add the AngularJS file just like any other JavaScript file to use it in applications. It doesn?t need to be installed separately before using it in applications.
    3)Node.js supports non-blocking input output I/O and follows an event driven architecture. It is used to create real-time applications such as instant messaging or chat apps.AngularJS is completely written in JavaScript. It is mainly used to create single-page client side applications.
    4)Node.js is a platform built on the top of Google’s V8 JavaScript engine.AngularJS is an open source framework, follows the syntax of JavaScript and developed by Google.
    5)Node.js is written in C, C++ and JavaScript languages.AngularJS is written completely in JavaScript but it is different from other web application frameworks like jQuery.
    6)Node.js has a lot number of frameworks such as Express.js, Sails.js, Partial.js etc.AngularJS itself is a web application framework of JavaScript.

  • Using Mixed Precision Training

    • For large models or limited hardware, mixed precision training can improve performance by using 16-bit floats.pythonCopy codefrom tensorflow.keras.mixed_precision import experimental as mixed_precision policy = mixed_precision.Policy('mixed_float16') mixed_precision.set_policy(policy)
    • This helps leverage the speed and memory benefits of reduced precision without losing much accuracy.

  • Handling Class Imbalance

    • If you have imbalanced data (e.g., more samples of one class), adjust the class weights or use oversampling.pythonCopy codeclass_weights = {0: 1., 1: 50.} model.fit(X_train, y_train, class_weight=class_weights)
    • Class weights make the model pay more attention to underrepresented classes.

  • Monitor Overfitting with EarlyStopping

    • Use EarlyStopping to monitor metrics and stop training once overfitting is detected.pythonCopy codefrom tensorflow.keras.callbacks import EarlyStopping early_stop = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True) model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=50, callbacks=[early_stop])
    • This is an essential tool to stop training once the model starts overfitting, saving time and resources.

  • Custom Loss Functions

    • You can create custom loss functions to suit specific tasks.pythonCopy codeimport tensorflow.keras.backend as K def custom_loss(y_true, y_pred): return K.mean(K.square(y_pred - y_true) * K.exp(-K.abs(y_true))) model.compile(optimizer='adam', loss=custom_loss)
    • Custom loss functions are useful when default loss functions don’t meet the exact requirements of the task.

  • Multi-Output Models

    • Build models that predict more than one output by designing multiple outputs in the last layer.pythonCopy codefrom tensorflow.keras.models import Model from tensorflow.keras.layers import Dense, Input input_layer = Input(shape=(20,)) x = Dense(128, activation='relu')(input_layer) # Output 1 output1 = Dense(10, activation='softmax', name='output_1')(x) # Output 2 output2 = Dense(1, activation='linear', name='output_2')(x) model = Model(inputs=input_layer, outputs=[output1, output2]) model.compile(optimizer='adam', loss=['categorical_crossentropy', 'mse'], metrics=['accuracy', 'mae'])
    • This setup is useful for multi-task learning where you have a combination of classification and regression tasks.

  • Data Augmentation for Tabular Data

    • If you’re working with tabular data, augmenting it can improve performance.
      • SMOTE (Synthetic Minority Oversampling Technique) can generate new samples for imbalanced datasets.
      • Noise injection can also create variety in your training data.

  • Model Checkpoints with Conditions

    • Save the model based on the best metric, such as validation accuracy or validation loss, at different points in training.pythonCopy codefrom tensorflow.keras.callbacks import ModelCheckpoint checkpoint = ModelCheckpoint('best_model.h5', monitor='val_accuracy', save_best_only=True, mode='max') model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=50, callbacks=[checkpo