Online Course – Certified Professional Specialization in Deep Learning Specialization by DeepLearning.AI

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Online Course - Certified Professional Specialization in Deep Learning Specialization by DeepLearning.AI

Professional Certificate

Intermediate level

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Building and training deep neural networks and implementing polarized neural networks.
  • Identify architectural parameters and apply deep learning to your applications.
  • Using best practices for training and developing test teams.
  • Analyzing biases/variances in building deep learning applications.
  • Implementing optimization algorithms and neural network execution in TensorFlow.
  • Implementing error reduction strategies in machine learning systems.
  • Building a convolutional neural network and its application in visual identification and recognition tasks.
  • Building and training recurrent neural networks and their variations (GRUs, LSTMs).
  • Working with NLP and word illustration.
  • Using HuggingFace’s coding and Transformers.

What you will learn in the course

Courses for which the course is suitable

  • Artificial Intelligence Software Developer
  • Machine Learning Engineer
  • Data Analyst
  • Speech recognition systems developer
  • Chatbot developer
  • Machine translation systems developer
  • Natural Language Processing Application Developer
  • Music synthesis system developer
  • Neural Network Engineer
  • Deep Learning Expert
  • Researcher in the field of artificial intelligence
  • Industry AI solutions developer

Specialization in Deep Learning

The Deep Learning specialization is a foundational program that will help you understand the capabilities, challenges, and implications of deep learning, and prepare you to participate in the development of advanced AI technologies.

In this internship, you will build and train neural network architectures such as:

  • Convolutional neural networks
  • Repeating networks
  • LSTMs
  • Transformers

You will learn how to improve them with strategies like Dropout, BatchNorm, and others. Get ready to master theoretical understandings and their industry applications with Python and TensorFlow, and solve real-world cases like:

  • Speech recognition
  • Music synthesis
  • Chatbots
  • Machine translation
  • Natural language processing

AI is changing the face of many industries. The Deep Learning specialization provides you with a pathway to take the next step in the world of AI by acquiring the knowledge and skills that will advance your career. Along the way, you’ll also receive career tips from deep learning experts from industry and academia.

Applied Learning Project

At the end of the course you will be able to:

  • Build and train deep neural networks and implement polarized neural networks.
  • Identify architectural parameters and apply deep learning to your applications.
  • Use best practices for training and developing test teams.
  • Analyze biases/variances in building deep learning applications.
  • Implement optimization algorithms and execute a neural network in TensorFlow.
  • Implement error reduction strategies in machine learning systems.
  • Build a convolutional neural network and apply it to visual identification and recognition tasks.
  • Build and train recurrent neural networks and their variations (GRUs, LSTMs).
  • Working with NLP and word illustration.
  • To be used in HuggingFace’s coding and Transformers.

Details of the courses that make up the specialization

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

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