https://github.com/camara94/cognitive-deep-learning-with-tensorflow
The majority of data in the world is unlabeled and unstructured, for instance images, sound, and text data. Shallow neural networks cannot easily capture relevant structures within this type of data, but deep networks are capable of discovering the hidden structures. In this course, you will use the TensorFlow library to apply deep learning on different types of data to solve real world problems.
https://github.com/camara94/cognitive-deep-learning-with-tensorflow
convolutional-neural-networks deeplearning-ai python recurrent-neural-networks tensorflow
Last synced: 4 months ago
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The majority of data in the world is unlabeled and unstructured, for instance images, sound, and text data. Shallow neural networks cannot easily capture relevant structures within this type of data, but deep networks are capable of discovering the hidden structures. In this course, you will use the TensorFlow library to apply deep learning on different types of data to solve real world problems.
- Host: GitHub
- URL: https://github.com/camara94/cognitive-deep-learning-with-tensorflow
- Owner: camara94
- License: mit
- Created: 2022-02-09T01:13:26.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-02-10T22:17:31.000Z (over 3 years ago)
- Last Synced: 2025-04-09T15:12:30.552Z (6 months ago)
- Topics: convolutional-neural-networks, deeplearning-ai, python, recurrent-neural-networks, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 28.1 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Cognitive-Deep-Learning-with-TensorFlow
The majority of data in the world is unlabeled and unstructured, for instance images, sound, and text data. Shallow neural networks cannot easily capture relevant structures within this type of data, but deep networks are capable of discovering the hidden structures. In this course, you will use the TensorFlow library to apply deep learning on different types of data to solve real world problems.
## Prerequisites and Recommended skills
### Prerequisites
* Python programming
### Recommended skills prior to taking this course
* Neural Network
## Learning Objectives
In this course you will learn about:
* Using TensorFlow for Deep Learning
* Breaking down images into their principal components and automatically generating a caption for it;
* Recommending movies, products, anything, based on what a certain person likes
* Processing incomplete sentences and predicting what was going to be written afterwards;
## Syllabus
### Module 1 - Intro to TensorFlow
* Intro to TensorFlow
* Intro to Deep Learning
* Deep Neural Networks
### Module 2 - Convolutional Neural Networks (CNNs)
* Intro to CNNs
* CNNs for Classification
* CNN Architecture
### Module 3 - Recurrent Neural Networks (RNNs)
* The Sequence Problem
* The RNN Model
* The LSTM Model
* Applying RNs to Language Modelling
### Module 4 - Restricted Boltzmann Machines (RBMs)
* Intro to RBMs
* RBMs
### Module 5 - Autoencoders
* Intro to Autoencoders
* Autoencoders