IBM Deep Learning with TensorFlow - IBM: ML0120EN

IBM Accelerating Deep Learning with GPUs - IBM: ML0122ENv3

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

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

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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