Emerging Trends in AI and Machine Learning

Duration: 5 min

This module delves into the latest advancements and emerging trends in AI and Machine Learning, including cutting-edge algorithms, innovative feature engineering techniques, and advanced model selection strategies. Understanding these trends is crucial for staying ahead in the rapidly evolving field of AI.

Transfer Learning

Transfer learning is a technique where a model developed for one task is reused as the starting point for a model on a second task. It is a popular approach in deep learning where pre-trained models are used as the starting point on computer vision and natural language processing tasks given the vast compute and time resources required to develop neural network models on these problems.

import tensorflow as tf
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense

# Load the VGG16 model pre-trained on ImageNet data
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# Freeze the layers of the base model
for layer in base_model.layers:
    layer.trainable = False

# Add custom layers on top of the base model
x = base_model.output
x = tf.keras.layers.GlobalAveragePooling2D()(x)
predictions = Dense(10, activation='softmax')(x)

# Define the new model
model = Model(inputs=base_model.input, outputs=predictions)

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

Try it in Google Colab:

Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
vgg16 (Functional)          (None, 7, 7, 512)          14714688 
_________________________________________________________________
global_average_pooling2d (G (None, 512)               0         
_________________________________________________________________
dense (Dense)               (None, 10)                5130      
=================================================================
Total params: 14,719,818
Trainable params: 5,130
Non-trainable params: 14,714,688
_________________________________________________________________

AutoML

AutoML refers to techniques for automatically discovering well-performing models for predictive tasks with minimal human intervention. It encompasses a range of methods from automated feature engineering to automated hyperparameter tuning and neural architecture search.

import tensorflow as tf
from tensorflow import keras
from keras_tuner import RandomSearch

# Define a model-building function
def build_model(hp):
    model = keras.Sequential()
    model.add(keras.layers.Dense(units=hp.Int('units',
                                              min_value=32,
                                              max_value=512,
                                              step=32),
                                 activation='relu', input_shape=(784,)))
    model.add(keras.layers.Dense(10, activation='softmax'))
    model.compile(optimizer=keras.optimizers.Adam(hp.Choice('learning_rate',
                                                            values=[1e-2, 1e-3, 1e-4])),
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])
    return model

# Instantiate the tuner
tuner = RandomSearch(
    build_model,
    objective='val_accuracy',
    max_trials=5,
    executions_per_trial=3,
    directory='my_dir',
    project_name='helloworld'
)

# Perform the search
tuner.search(x_train, y_train, epochs=5, validation_data=(x_test, y_test))

💡 Tip: When using AutoML, ensure that your dataset is well-prepared and preprocessed to avoid misleading results. Also, be mindful of the computational resources required, as AutoML can be resource-intensive.