Hands-On Deep Learning with TensorFlow 2.0

Hands-On Deep Learning with TensorFlow 2.0
Hands-On Deep Learning with TensorFlow 2.0

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 4h 04m | 806 MB
eLearning | Skill level: All Levels


Your hands-on guide Deep Learning to get you up and running with TensorFlow 2.0, more effectively than other courses!

Are you eager to deep dive into the details of neural networks and would like to play with it? Do you want to learn Deep Learning Techniques to build projects with the latest Tensorflow 2.0. You may use Keras but it is a high-level implementation which itself uses Tensorflow in the backend and you can’t make changes up to that level in your model as of TensorflowKeras. A good data scientist must have the skill of how things are going on behind the scenes.
This course will help you to be a good Data Scientist by giving hands-on knowledge of Tensorflow 2.0. You will implement real deep learning algorithms and will be available with all the implementation. Using implementation you will learn core details of a neural network like forward-propagation i.e, how to initialize weights and backpropagation i.e, how to update weights with gradient descent algorithm, Cost functions like cross entropy and much more.
By the end of this course, you will be confident to implement your own neural network that is a very amazing thing you are adding to your toolbox.

Our approach is pretty simple and straightforward. In this course, you will be given some introductory part in every section and the advantages and application of that particular topic. After that, we will walk through the code in Python with crisp and clear explanation and easy to understand. Each section will be followed with the Quiz revolve around what we have learned so far, this will make you confident that how proficient you are till now.

What You Will Learn

  • Understand what TensorFlow is, how TensorFlow works, from basics to advanced level with case-study based approach.
  • Understand neural networks and how to implement them with TensorFlow via Churn Prediction Case Study.
  • Implement a convolution neural network in TensorFlow for pneumonia detection from the x-ray case study.
  • Implement a recurrent neural network for stock price prediction case study and improving accuracy with long short-term memory network.
  • Learn about TensorBoard for monitoring, transformer, eager execution and debugging code with TensorFlow.
  • Build Transfer learning in Tensorflow using TFlearn via object detection and opinion mining model.