Udemy Coupon: Self-Supervised Learning [aprendizaje profundo en TensorFlow] With 100% discount for a limited time - Tech Beastz

Self-supervised learning Supervised Machine Learning | Unsupervised Machine Learning | Contradictory teaching | SimCLR

“If intelligence is a cake, then so is quantity Self-supervised learningThe icing on the cake is the supervised education and the cherry on the cake is the reinforcement education.“

Yan Andre Lekun

Chief AI Scientist at Meta

Some “essentials” before starting

• You should be familiar with the Deep Learning Architecture with a stack of Convoluted, Recurrent, Density, Pooling, Average and Normalization Layers using the TensorFlow Library in Python 3+.

• You need to know how to develop, train and test a multi-layer deep learning model using the TensorFlow library in Python 3+.

• You must know that this a “100% Money Back Guarantee” Course according to Udemy rules.

Course Instructor

• My name is Mohammed H. Rafi, Ph.D. I feel honored and humbled to have served as your coach.

• I am a machine learning engineer, researcher, and instructor at Johns Hopkins University, College of Engineering, and Georgia State University, Department of Computer Science. I am also the founder of MHR Group LLC in Georgia.

Topics and materials

• This course teaches you “Self-Supervised Learning” (SSL), also called “Representation Learning”.

• SSL is a relatively new and discussed topic in machine learning for dealing with repositories with limited labeled data.

• There are two common SSL techniques, paradoxical and generative. The focus of this course is only on supervised and non-supervised conflicting models.

• There are many examples and experiments in this course to fully understand the concept of SSL.

• Our focus domain is the image domain, but you can apply what you learn to other domains, including temporal records and natural language processing (NLP).

• In each lecture, you can access the corresponding Python .ipynb notebook. Notebooks are best run with GPU accelerator. See the lecture below for more details.

• If the video is too fast or too slow, you can always change their speed. You can also turn on video captions.

• The video of this course is best watched with captions using 1080p quality.

• Lectures are designed to work best with GPU accelerators on Google Colab.

• The TensorFlow version used in these lectures is ‘2.8.2.’ You can use% tensorflow_version 2.x in the first cell of your Python notebook.

Machine learning libraries in Python are evolving with TensorFlow. Because of this, you should keep yourself updated with changes and change your code.

Course overview

Four sections and Ten lectures:

• Section 01: Introduction.

• Section 02: Supervised Model.

• Section 03: Labeling Work.

• Section 04: Self-Supervised Education.

  • Lecture 06: Self-supervised teaching.

  • Lecture 07: Supervised Contradictory Reasons, Experiment 1.

  • Lecture 08: Supervised Contradictory Reasons, Experiment 2.

  • Lecture 09: CMCLR, an Unexpected Contradictory Cause Model.

  • Lecture 10: CMCLR experiment.

Self-supervised learning Supervised Machine Learning | Unsupervised Machine Learning | Contradictory teaching | SimCLR

This course is free. You will find the coupon below.

Note that these types of coupons last very short.

If the coupon has already expired, you can purchase the course as usual.

These types of coupons last very few hours, and even minutes after publication.

Due to the Udemy update there are now only 1,000 coupons available, we are not responsible if the coupon has already expired.

Use the button below to get the course with your coupon:

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