Deep Learning - Prakalpana Technologies

Deep Learning Course

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Curriculum of Deep Learning & AI

1.Introduction to Neural Network

what is neural network…?
How neural networks works?
Gradient descent
Stochastic Gradient descent
Perceptron
Multilayer Perceptron
Backpropagation

2.Building Deep Learning Environment

Overview of deep learning
DL environment setup locally
- Installing Tensor flow
- Installing Keras
Setting up a DL environment in the cloud
- AWS
- GCP
Run Tensor flow program on AWS cloud platform

3.Tenserfow Basics

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

4.Activation Functions

What are activation functions?
Sigmoid function
Hyperbolic Tangent function
ReLu -Rectified Linear units
Softmax function

5.Training Neural Network for MNIST dataset

Exploring the MNIST dataset
Defining the hyper parameters
Model definition
Building the training loop
Overfitting and Under fitting
Building Inference

6.Word Representation Using word2vec

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 tensor board

7.Clasifying Images with Convolutional Neural Networks(CNN)

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

8.Popular CNN Model Architectures

Introduction to ImageNet
LeNet architecture
AlexNet architecture
VGGNet architecture
ResNet architecture

9.Introduction to Recurrent Neural Networks(RNN)

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

10.Sequence-to-Sequence Models for Building Chatbot

11.HandWritten Digits and letters Classification Using CNN

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

Reviews

I've been here for SpringBoot & Microservices course. Tutors are professional with in-depth knowledge, using simple examples and making it easy to understand. Course work is scheduled in such a way it includes much of assignments. I got zero knowledge on programming when i started, But now I'm able to code. I would recommend it to anyone.

I have been Learning Docker & Kubernetes course. My trainer taught me very in-depth hands on using simple examples and making it easy to understand. It help me to grab job in very good MNC.

Thank u so much for your very valuable training and Prakalpana support team also helped me lot answering all of my question the instructor also very excellent.