Deep Learning by TensorFlow 2.0 Basic to Advance with Python

Become Deep Learning professional by learning from Deep Learning professional

Deep Learning by TensorFlow 2.0 Basic to Advance with Python
Deep Learning by TensorFlow 2.0 Basic to Advance with Python

Deep Learning by TensorFlow 2.0 Basic to Advance with Python udemy course

Become Deep Learning professional by learning from Deep Learning professional

What you'll learn:

  • Gain a Strong Understanding of TensorFlow – Google’s Cutting-Edge Deep Learning Framework
  • Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow
  • Set Yourself Apart with Hands-on Deep and Machine Learning Experience
  • Grasp the Mathematics Behind Deep Learning Algorithms
  • Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules
  • Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization
  • Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding

Requirements:

  • Some basic Python programming skills
  • You’ll need to install Anaconda. We will show you how to do it in one of the first lectures of the course.
  • All software and data used in the course are free.

Description:

As a practitioner of Deep Learning, I am trying to bring many relevant topics under one umbrella in the following topics. Deep Learning has been most talked about for the last few years and the knowledge has been spread across multiple places.

1. The content (80% hands-on and 20% theory) will prepare you to work independently on Deep Learning projects

2. Foundation of Deep Learning TensorFlow 2.x

3. Use TensorFlow 2.x for Regression (2 models)

4. Use TensorFlow 2.x for Classifications (2 models)

5. Use Convolutional Neural Net (CNN) for Image Classifications (5 models)

6. CNN with Image Data Generator

7. Use Recurrent Neural Networks (RNN) for Sequence data (3 models)

8. Transfer learning

9. Generative Adversarial Networks (GANs)

10. Hyperparameters Tuning

11. How to avoid Overfitting

12. Best practices for Deep Learning and Award-winning Architectures

Who this course is for:

Course Details:

  • 17.5 hours on-demand video
  • 5 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of completion

Deep Learning by TensorFlow 2.0 Basic to Advance with Python udemy free download

Become Deep Learning professional by learning from Deep Learning professional

Demo Link: https://www.udemy.com/course/deep-learning-by-tensorflow-tfkeras-keras-using-python/