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Deep Learning With TensorFlow

( Duration: 5 Days )

Deep Learning With TensorFlow training course is designed to introduces Deep Learning concepts and Tensorflow library. This course teaches various complex algorithms for deep learning with examples that use different deep neural networks. You will also learn how to train machine to craft new features to make sense of deeper layers of data. In this course, delegates will come across topics like logistic regression, convolutional neural networks, advanced multilayer perceptrons and how to implement them using real-world datasets.

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.

By attending Deep Learning With TensorFlow workshop, delegates will learn:

  • Introduction to Machine Learning
  • Deep Learning concepts
  • Tensorflow library
  • Writing Tensorflow applications (CNN, RNN)
  • Using TF tools
  • High level libraries : Keras

  • 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.
  • Developers, Data Analysts, Data Scientists



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

The Tensor: The Basic Unit of Tensorflow

  • Introducing Tensors
  • Tensorflow Execution Model

Single Layer Linear Perceptron Classifier With Tensorflow

  • Introducing Perceptrons
  • Linear Separability and Xor Problem
  • Activation Functions
  • Softmax output
  • Backpropagation, loss functions, and Gradient Descent

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

High level Tensorflow: tf.learn

  • Using high level tensorflow
  • Developing a model with tf.learn

Convolutional Neural Networks in Tensorflow

  • Introducing CNNs
  • CNNs in Tensorflow

Introducing Keras

  • What is Keras?
  • Using Keras with a Tensorflow Backend

Recurrent Neural Networks in Tensorflow

  • Introducing RNNs
  • RNNs in Tensorflow

Long Short Term Memory (LSTM) in Tensorflow

  • Introducing RNNs
  • RNNs in Tensorflow


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

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