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Intro to Deep Learning with TensorFlow

Course Code

BD78

Duration

3 Days

Basic knowledge of Python language and Jupyter notebooks is assumed.
Basic knowledge of Linux environment would be beneficial.
Some Machine Learning familiarity would be nice, but not necessary.
The abundance of data and affordable cloud scale has led to an explosion of interest in Deep Learning. Google has released an excellent library called Tensorflow to open-source, allowing state-of-the-art machine learning done at scale, complete with GPU-based acceleration.

This course introduces Deep Learning concepts and Tensorflow library to students.
This course is designed for Developers, Data analysts, data scientists

In this course, participants will learn:

  • Introduction to Machine Learning
  • Deep Learning concepts
  • Tensorflow library
  • Writing Tensorflow applications (CNN, RNN)
  • Using TF tools
  • High level libraries : Keras
Introduction to Machine Learning
Understanding Machine Learning
Supervised versus Unsupervised Learning
Regression
Classification
Clustering

Introducing Tensorflow
Tensorflow intro
Tensorflow Features
Tensorflow Versions
GPU and TPU scalability
Lab: Setting up and Running Tensorflow

The Tensor: The Basic Unit of Tensorflow
Introducing Tensors
Tensorflow Execution Model
Lab: Learning about Tensors

Single Layer Linear Perceptron Classifier With Tensorflow
Introducing Perceptrons
Linear Separability and Xor Problem
Activation Functions
Softmax output
Backpropagation, loss functions, and Gradient Descent
Lab: Single-Layer Perceptron in Tensorflow

Hidden Layers: Intro to Deep Learning
Hidden Layers as a solution to XOR problem
Distributed Training with Tensorflow
Vanishing Gradient Problem and ReLU
Loss Functions
Lab: Feedforward Neural Network Classifier in Tensorflow

High level Tensorflow: tf.learn
Using high level tensorflow
Developing a model with tf.learn
Lab: Developing a tf.learn model

Convolutional Neural Networks in Tensorflow
Introducing CNNs
CNNs in Tensorflow
Lab: CNN apps

Introducing Keras
What is Keras?
Using Keras with a Tensorflow Backend
Lab: Example with a Keras

Recurrent Neural Networks in Tensorflow
Introducing RNNs
RNNs in Tensorflow
Lab: RNN

Long Short Term Memory (LSTM) in Tensorflow
Introducing RNNs
RNNs in Tensorflow
Lab: RNN

Conclusion
Summarize features and advantages of Tensorflow
Summarize Deep Learning and How Tensorflow can help
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