Deep Learning Course Syllabus

Deep Learning Course SyllabusWhen it comes to artificial intelligence and machine learning, Deep Learning is by far the most in-demand method and set of skills. At SLA Jobs, we offer thorough training on Deep Learning Training in Chennai, including an extensive Deep Learning Course Syllabus that includes hands-on experience on real-time projects, so anyone with a background in engineering or IT can benefit from it. 

With input from experts in the field, we developed a comprehensive Deep Learning Course curriculum that is tailored to meet the needs of students from all backgrounds.

To ensure that our students are well-prepared for the workforce, we must maintain constant attention to industry developments and adapt our Deep Learning Course Syllabus accordingly. Data science prototypes, machine learning tools and methods, deep learning algorithms, datasets, data representation, deep learning tests and experiments, and other data science-related topics are all covered in our Deep Learning training curriculum. 

Here is a comprehensive look at what you can expect to learn in a Deep Learning course syllabus.

Introduction to Deep learning:

  • Origin of Deep Learning
  • Machine Learning limitations
  • Introduction about Deep Learning
  • Deep Learning advantages and Machine Learning limitations
  • Real-life use cases
  • Brush up Machine Learning concepts

Understanding the Neural network with TensorFlow:

  • Structure and working of Deep Learning
  • Detailed explanation about Perceptron
  • Different Activation functions
  • Introduction to TensorFlow
  • What is the computational graph?
  • Basic TensorFlow coding and graph visualization
  • Brief introduction about Variables, Constants, and Place Holders
  • Creating a simple TensorFlow model

Deep dive into Neural Network with TensorFlow:

  • Different layers in the Neural network
  • Understanding Neural Networks in detail
  • Introduction to Multi-layer Perceptron
  • What are Forward propagation and Backpropagation?
  • Build a Multi-layer perceptron model using TensorFlow
  • Familiarise in using Tensor Board

Master Deep Networks:

  • What is a Deep Neural Network?
  • How does Deep Neural Network help to increase accuracy?
  • Understanding the working of Deep Neural Networks
  • What are Weight and Bias, and how it is getting updated?
  • How gradient descent is useful to update parameters?
  • Types of Deep Networks

Convolutional Neural Network (CNN)

  • Introduction to CNN
  • Advantage of CNN over other Neural Networks
  • Applications of CNN
  • Architecture of CNN
  • Different layers and its use to build a CNN model
  • Real-time use cases of CNN

Recurrent Neural Network (RNN):

  • Introduction to RNN
  • How RNN is different from other Neural Network models
  • Structure and working of RNN
  • Exploding and Vanishing Gradient descend problem
  • Long Short-Term Memory (LSTM)
  • How LSTM overcome the problem of Vanishing Gradient descent?
  • Real-time use-cases of LSTM

Restricted Boltzmann Machine (RBM) and Autoencoders:

  • What is Restricted Boltzmann Machine (RBM)
  • Applications of RBM
  • How to do Collaborative Filtering with RBM?
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders and how it is different from PCA

Keras API:

  • Introduction to Keras
  • How to build a Models in Keras using TensorFlow backend
  • Sequential and Functional Composition
  • Explaining Predefined Neural Network Layers
  • What is Batch Normalization?
  • How to save and load a model
  • Using TensorBoard with Keras

TFLearn API:

  • Introduction to TFLearn
  • How to build a Models in TFLearn using TensorFlow backend
  • Sequential and Functional Composition
  • Explaining Predefined Neural Network Layers
  • What is Batch Normalization?
  • How to save and load a model
  • Using TensorBoard with TFLearn

The goal of our Deep Learning Course syllabus at SLA is to give students the tools they need to succeed in the competitive job market and among the industry’s most prestigious institutions. Students will emerge with an in-depth understanding of Convolutional network concepts in addition to the fundamental techniques and principles of Neural Networks after completing our Deep Learning Course in Chennai.

No more thinking. Just make the right choice by enrolling in SLA for the bright course of your career.