Deep Learning + Project Work

Deep Learning + Project Work

Rs.25,000.00

Course Fee Including 18% GST

Please login to purchase the course.

Timing: Starting from 29th June 2020.

Note: Please WhatsApp +91 9910043510 before making the payment

SKU: cid_88519 Category:

Target Audience

  • B.Tech/MCA/BCA/M.Tech Students
  • Working Professionals from Corporate

Test & Evaluation

1. During the program, the participants will have to take all the assignments given to them for better learning.
2. At the end of the program, a final assessment will be conducted.

Certification

1. All successful participants will be provided with a certificate of completion.

2. Students who do not complete the course / leave it midway will not be awarded any certificate.

Tentative Date & Schedule

New batch starting from 29th June 2020 for the live online tutorial session and doubt clearing sessions

No experience is required, But fundamental knowledge of C/C++ would be helpful.

Topics to be covered

1. Introduction To Artificial Neural Network day's (1-5)

  • What is Artificial Neural Network (ANN)?
  • How Neural Network Works?
  • Perceptron
  • Multilayer Perceptron
  • Feed Forward
  • Gradient Descent and Stochastic Gradient Descent
  • Back propagation

2. Introduction To Deep Learning day's (6,7)

  • What is Deep Learning?
  • Deep Learning Packages
  • Deep Learning Applications
  • Building Deep Learning Environment
    • Installing Tensorflow Locally
    • Understanding Google Colab

3. Tensorflow Basics day's (8-10)

  • What is Tensorflow?
  • Tensorflow 1.x V/S Tensorflow 2.x
  • Placeholder ,Variables, Constants
  • Operations using tensorflow
  • Difference between tensorflow and numpy operations
  • Computational Graph
  • Visualizing Graph using Tensorboard

4. Activation Functions day's (11,12)

  • What are Activation Functions ?
  • Sigmoid Function,
  • Hyperbolic Tangent Function (tanh)
  • ReLU –Rectified Linear Unit
  • Softmax Function
  • Vanishing Gradient Problem

5. Building Artificial Neural Network day's (13-15)

  • Understanding MNIST Dataset
  • Initializing weights and biases
  • Defining loss/cost Function
  • Train the Neural Network
  • Minimizing the loss by adjusting weights and biases

6. Modern Deep Learning Optimizers and Regularization day's (16-20)

  • SGD with Momentum
  • RMSprop
  • AdaGrad
  • Adam
  • Dropout Layers and Regularization
  • Batch Normalization

7. Building Deep Neural Network Using Keras day's (21-23)

  • What is Keras?
  • Keras Fundamental For Deep Learning
  • Keras Sequential Model and Functional API
  • Solve a Linear Regression and Classification Problem with Example
  • Saving and Loading a Keras Model

8. Convolutional Neural Networks (CNNs) day's (24-30)

  • Introduction to CNN
  • CNN Architecture
  • Convolutional Operations
  • Pooling , Stride and Padding Operations
  • Data Augmentation
  • Building ,Training and Evaluating First CNN Model
  • Model Performance Optimization
  • Auto encoders for CNN
  • Transfer Learning

9. Word Embedding day's (31-33)

  • Word Embedding
  • Keras Embedding Layers
  • Visualize Word Embedding
  • Google Word2Vec Embedding
  • GloVe Embedding

10. Recurrent Neural Networks (RNNs) day's (34-40)

  • Introduction to RNN
  • RNN Architecture
  • Types of RNN
  • Implementing basic RNN in tensorflow
  • Need for LSTM and GRU
  • Deep RNN
  • Text Classification Using LSTM
  • Sequence to Sequence Modeling
  • Prediction for Time Series problem

11. Projects day's (41-50)

  • Sentiment analysis using RNN
  • Neural Machine Translation (NMT)
  • MNIST Handwritten digits classification using CNN
  • Cat vs Dog Image classification
  • Building Chatbot using NLP and Seq2Seq Modeling
  • Objects Detection from Yolov3
  • Hand Detection
  • Real-Time Hand Gesture Recognition
  • Auto-complete search query

Sample Project:

(Text Extraction from Image)

(Autocomplete search query in Tensorflow)

(Hand Detection)

(Neural Machine Translation (NMT) using Tensorflow)

(ChatBot using Tensorflow)

(Cat Dog Classification)

(MNIST Digit Prediction)

Note:
  1. Upto six weeks (or till submission of the final quiz) access to the course
  2. To get access to the certificate - you need to take the online exam at the end of the course
Open chat
Powered by