Deep Learning + Project Work

Deep Learning + Project Work


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.


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)

  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