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Deep Learning Course Syllabus

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This carefully crafted Deep Learning Course Syllabus is designed to provide you with a comprehensive understanding of deep learning concepts, methodologies, and applications. From neural networks to convolutional neural network model architecture and recurrent neural networks, you’ll explore cutting-edge techniques in machine learning that power advancements in various industries. Whether you’re a fresher or an experienced practitioner, our Deep Learning Course Syllabus offers hands-on exercises and projects to develop your skills and unleash the full potential of deep learning algorithms. So, enroll in our Deep Learning Training Institute in Chennai.

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Breakdown of Deep Learning Course Fee and Batches

Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
July 2024
Week days
(Mon-Fri)
Online/Offline

2 Hours Real Time Interactive Technical Training 

1 Hour Aptitude 

1 Hour Communication & Soft Skills

(Suitable for Fresh Jobseekers / Non IT to IT transition)

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July 2024
Week ends
(Sat-Sun)
Online/Offline

4 Hours Real Time Interactive Technical Training

(Suitable for working IT Professionals)

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Syllabus for The Deep Learning Course

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Introduction to Neural Network

1

  • what is neural network..?
  • How neural networks works?
  • Gradient descent
  • Stochastic Gradient descent
  • Perceptron
  • Multilayer Perceptron
  • BackPropagation
Building Deep learning Environment

2

  • Overview of deep learning
  • DL environment setup locally
    • Installing Tensorflow
    • Installing Keras
  • Setting up a DL environment in the cloud
    • AWS
    • GCP
  • Run Tensorflow program on AWS cloud plateform
Tenserfow Basics

3

  • Placeholders in Tensorflow
    • Defining placeholders
    • Feeding placeholders with data
  • Variables,
  • Constant
  • Computation graph
  • Visualize graph with Tensor Board
Activation Functions

4

  • What are activation functions?
  • Sigmoid function
  • Hyperbolic Tangent function
  • ReLu -Rectified Linear units
  • Softmax function
Training Neural Network for MNIST dataset

5

  • Exploring the MNIST dataset
  • Defining the hyperparameters
  • Model definition
  • Building the training loop
  • Overfitting and Underfitting
  • Building Inference
Word Representation Using word2vec

6

  • Learning word vectors
    • Loading all dependencies
    • Preparing the text corpus
    • defining our word2vec model
    • Training the model
    • Analyzing the model
  • Visualizing the embedding space by plotting the model on tensorboard
Clasifying Images with Convolutional Neural Networks(CNN)

7

  • Introduction to CNN
  • Train a simple convolutional neural net
  • Pooling layer in CNN
  • Building ,training and evaluating our first CNN
  • Model performance optimization
Popular CNN Model Architectures

8

  • Introduction to Imagenet
  • LeNet architecture
  • AlexNet architecture
  • VGGNet architecture
  • ResNet architecture
Introduction to Recurrent Neural Networks(RNN)

9

  • What are Recurrent Neural Networks (RNNs)?
  • Understanding a Recurrent Neuron in Detail
  • Long Short-Term Memory(LSTM)
  • Back propagation Through Time(BPTT)
  • Implementation of RNN in Keras
HandWritten Digits and letters Classification Using CNN

10

  • Code Implementation
    • Importing all of the dependencies
    • Defining the hyperparameters
    • Building a simple deep neural network
    • Convolution in keras
    • Pooling
    • Dropout technique
    • Data augmentation

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