קורס אונליין – תעודה מקצועית מוסמכת ב-Deep Learning Specialization של DeepLearning.AI

Neural Networks and Deep Learning

Course 1 • 24 hours • 4.9 (121,879 ratings)

What you'll learn:

• Foundational concepts of neural networks and deep learning.
• Build, train, and apply fully connected deep neural networks.
• Implement efficient (vectorized) neural networks.
• Identify key parameters in a neural network’s architecture.
• Apply deep learning to your own applications.

Skills you'll gain:

• Tensorflow
• Deep Learning
• Hyperparameter tuning
• Mathematical Optimization

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

Course 2 • 23 hours • 4.9 (63,126 ratings)

What you'll learn:

• Understand processes that drive performance in deep learning.
• Use standard neural network techniques such as initialization, L2 and dropout regularization.
• Implement various optimization algorithms like mini-batch gradient descent, Momentum, RMSprop, and Adam.
• Implement a neural network in TensorFlow.

Skills you'll gain:

• Gated Recurrent Unit (GRU)
• Recurrent Neural Network
• Natural Language Processing
• Long Short Term Memory (LSTM)
• Attention Models

Structuring Machine Learning Projects

Course 3 • 6 hours • 4.8 (49,853 ratings)

What you'll learn:

• Build a successful machine learning project.
• Diagnose errors in a machine learning system.
• Understand complex ML settings and apply end-to-end learning.

Skills you'll gain:

• Artificial Neural Network
• Backpropagation
• Python Programming
• Neural Network Architecture

Convolutional Neural Networks

Course 4 • 35 hours • 4.9 (42,276 ratings)

What you'll learn:

• Build convolutional neural networks and apply them to visual detection tasks.
• Use neural style transfer to generate art.

Skills you'll gain:

• Decision-Making
• Machine Learning
• Inductive Transfer
• Multi-Task Learning

Sequence Models

Course 5 • 37 hours • 4.8 (30,314 ratings)

What you'll learn:

• Build and train Recurrent Neural Networks (RNNs) and their variants.
• Apply RNNs to natural language processing tasks.

Skills you'll gain:

• Facial Recognition System
• Tensorflow
• Convolutional Neural Network
• Object Detection and Segmentation