Deep Learning Specialization

Deep Learning Specialization

Deep Learning Specialization Become a Machine Learning expert. Master the fundamentals of deep learning and break into AI. Recently updated with cutting-edge techniques! Taught in English 22 languages available Some content may not be translated Instructors: Andrew Ng +2 more Close Instructors Top Instructor Andrew Ng DeepLearning.AI 42 Courses • 7,127,453 learners Top Instructor Younes

Description

Deep learning is a subset of machine learning that relies on neural networks to understand and solve complex problems. Over the years, deep learning has gained significant traction in a variety of fields, thanks to its ability to perform tasks such as image and speech recognition, natural language processing, and autonomous driving.

To help individuals gain a comprehensive understanding of deep learning, the Deep Learning Specialization has emerged as a popular choice. Developed by leading experts in the field, this specialization is designed to provide a structured and in-depth learning experience that covers everything from the basics of neural networks to advanced techniques for building and optimizing deep learning models.

The Deep Learning Specialization typically consists of a series of courses that can be taken either individually or as a whole. Each course delves into different aspects of deep learning, such as convolutional neural networks,

Deep Learning Specialization

Become a Machine Learning expert. Master the fundamentals of deep learning and break into AI. Recently updated with cutting-edge techniques!

Taught in English

Some content may not be translated

Andrew Ng
Younes Bensouda Mourri
Kian Katanforoosh

Instructors: Andrew Ng

Top Instructor

839,959 already enrolled

Specialization – 5 course series

Get in-depth knowledge of a subject

4.9

(132,867 reviews)

Intermediate level

Recommended experience

3 months at 10 hours a week
Flexible schedule
Learn at your own pace
Earn degree credit

What you’ll learn

  • Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications

  • Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow

  • Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data

  • Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering

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