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
GPU and TPU scalability
Lab: Setting up and Running Tensorflow
The Tensor: The Basic Unit of Tensorflow
Tensorflow Execution Model
Lab: Learning about Tensors
Single Layer Linear Perceptron Classifier With Tensorflow
Linear Separability and Xor Problem
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
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
CNNs in Tensorflow
Lab: CNN apps
What is Keras?
Using Keras with a Tensorflow Backend
Lab: Example with a Keras
Recurrent Neural Networks in Tensorflow
RNNs in Tensorflow
Long Short Term Memory (LSTM) in Tensorflow
RNNs in Tensorflow
Summarize features and advantages of Tensorflow
Summarize Deep Learning and How Tensorflow can help