https://github.com/jesussantana/ibm-deep-learning-with-tensorflow

In this TensorFlow course, you will be able to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline.
https://github.com/jesussantana/ibm-deep-learning-with-tensorflow

deep-learning machine-learning neural-network neural-networks python tensorflow tensorflow-models

Last synced: 5 months ago
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In this TensorFlow course, you will be able to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline.

• Host: GitHub
• URL: https://github.com/jesussantana/ibm-deep-learning-with-tensorflow
• Owner: jesussantana
• License: mit
• Created: 2021-08-12T07:08:21.000Z (almost 5 years ago)
• Default Branch: main
• Last Pushed: 2022-05-13T11:39:30.000Z (about 4 years ago)
• Last Synced: 2025-02-13T18:49:32.806Z (over 1 year ago)
• Topics: deep-learning, machine-learning, neural-network, neural-networks, python, tensorflow, tensorflow-models
• Language: Jupyter Notebook
• Homepage:
• Size: 2.2 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# [IBM Deep Learning with TensorFlow - IBM: ML0120EN](https://cognitiveclass.ai/courses/course-v1:BigDataUniversity+ML0120EN+v2)

# [IBM Accelerating Deep Learning with GPUs - IBM: ML0122ENv3](https://cognitiveclass.ai/courses/tensorflow_gpu)

[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/)  

[![Made withJupyter](https://img.shields.io/badge/Made%20with-Jupyter-orange?style=for-the-badge&logo=Jupyter)](https://jupyter.org/try)  

## In this TensorFlow course, you will be able to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. 

  

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## [IBM Deep Learning with TensorFlow Credential](https://www.credly.com/badges/7574f37f-7d67-4acf-bbfb-cd2cbf76015a/public_url) 

## COURSE SYLLABUS: 

### Module 1 - Introduction to TensorFlow

- HelloWorld with TensorFlow

- Linear Regression

- Nonlinear Regression

- Logistic Regression

- Activation Functions 

### Module 2 - Convolutional Neural Networks (CNN)

- CNN History

- Understanding CNNs

- CNN Application

### Module 3 - Recurrent Neural Networks (RNN)

- Intro to RNN Model

- Long Short-Term memory (LSTM)

- Recursive Neural Tensor Network Theory

- Recurrent Neural Network Model

### Module 4 - Unsupervised Learning

- Applications of Unsupervised Learning

- Restricted Boltzmann Machine

- Collaborative Filtering with RBM 

### Module 5 - Autoencoders

- Introduction to Autoencoders and Applications

- Autoencoders

- Deep Belief Network

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## Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kind of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which consitutes the vast majority of data in the world.

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## TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

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## [IBM Accelerating Deep Learning with GPUs](https://cognitiveclass.ai/courses/tensorflow_gpu)  

## Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning. 

## COURSE SYLLABUS: 

### Module 1 - Quick review of Deep Learning

- Intro to Deep Learning

- Deep Learning Pipeline 

### Module 2 - Hardware Accelerated Deep Learning

- How to accelerate a deep learning model?

- Running TensorFlow operations on CPUs vs. GPUs

- Convolutional Neural Networks on GPU

### Module 3 - Deep Learning in the Cloud

- Deep Learning in the Cloud

- How does one use a GPU

### Module 4 - Distributed Deep Learning

- Distributed Deep Learning

- Object Detection with IBM PowerAI Vision

- Image Classification with IBM PowerAI Vision  

### Module 5 – Deed Learning Project

- Introduction to Character Modelling

- Recurrent Neural Network on GPU

- Benchmark performance of training your model on GPU versus CPU 

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