Deep Learning with Python

Live Online (VILT) & Classroom Corporate Training Course

Deep learning is a machine learning technique that clarifies computers to do what comes naturally to humans. In deep learning, a computer model studies how to perform classification jobs directly from images, text, or sound.
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Overview

Deep learning is the most powerful Machine learning method in various areas such as Robotics, Natural Language Processing, Image Recognition and Artificial Intelligence. The Deep Learning using Python course is designed for anyone with at least a year of coding experience and knowledge in mathematics.

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Objectives

At the end of Deep Learning with Python training course, participants will learn

  • Fundamentals of Deep Learning techniques
  • Artificial Neural networks and their architecture
  • Building and training the deep neural networks from scratch.
  • Convolutional Neural Networks (CNN)
  • Different optimization techniques to tune the learning of any neural network
  • Pointers to next frontiers in CNN and Deep Learning
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Prerequisites

  1. Basic python programming skills
  2. Basic mathematics skills
  3. Basic knowledge of Machine learning fundamentals
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Course Outline

  • Introduction to DL problems
  • DL terminologies
  • DL project workflow
  • DL real life examples

  • Working with Jupyter notebooks
  • Markdown and Code blocks
  • Keyboard shortcuts

  • Python syntax
  • Basic data types
  • Basic data structures

  • Numpy Arrays
  • Plotting using Matplotlib
  • Pandas Dataframes
  • Introduction to Keras

  • What is a Neuron
  • What are Activation Functions
  • How does a neural network learn?
  • Gradient Descent
  • Stochastic Gradient Descent
  • Back Propagation
  • Artificial Neural Networks in Keras
  • Linear model (No Hidden Layers)
  • Neural network with a single hidden layer

  • Image representation
  • ConvNets or CNN
  • Convolution Layer
  • Padding
  • How do we learn these kernels?
  • Can we force a particular kernel to learn to recognize a specific feature?
  • Non-Linear Activations
  • Downsampling or Pooling
  • Full Connection
  • Loading MNIST data
  • Implementation of CNN in Keras

  • Introduction to Auto Encoders
  • Why learn identity function?
  • Properties of learned function
  • Real world applications of Autoencoders
  • MNIST Dimensionality Reduction
  • A Simple Autoencoder
  • Functional API
  • Deep Autoencoder
  • Convolutional Autoencoder
  • Image Denoising
  • Data Specific Encoding and Decoding

  • Introduction to Recurrent Neural Networks
  • Sequence Learning
  • Regular Neural Network
  • Simple RNN
  • Problems with RNN
  • Long Short Term Memory (LSTM)
  • Stacked (Deep) LSTM Model
  • Deep Stacked LSTM with Stateful Cells
  • Gated Recurrent Unit
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