Deep Learning Concepts

Course Details

Deep Learning

Deep Learning may be a subfield of machine learning involved with algorithms galvanized by the structure and performance of the brain known as artificial neural networks.

The leaders and specialists within the field have concepts of what deep learning is and these specific and nuanced views shed a great deal of sunshine on what deep learning is all regarding. during this post, you’ll discover precisely what deep learning is by hearing from a spread of consultants and leaders within the field.

Deep learning is a side of computer science (AI) that’s involved with emulating the training approach that individuals use to realize bound kinds of information. At its simplest, deep learning are often thought of as some way to automatise prophetical analytics.

Computer programs that use deep learning undergo abundant a similar method. every rule within the hierarchy applies a nonlinear transformation on its input and uses what it learns to make a applied math model as output. Iterations continue till the output has reached an appropriate level of accuracy. the amount of process layers through that information should pass is what galvanized the label deep.

Deep Learning:

Advanced Machine Learning:

Neural Networks Intro

  1. Artificial Neural Networks(ANN)
  2. Deep Neural Networks
  3. Convolutional Neural Networks(CNN)
  4. Recurrent Neural Networks(RNN)
  5. Stock Price Prediction using Neural Networks: Demo
  6. Neural Net Concepts:
  7. Neurons as Nodes: Perceptrons
  8. Dense & Sparse Neural Networks

Neuron Based approach: Benefits

  1. Perceptrons
  2. Learning Weights
  3. Gradient Descent & Back Propagation
  4. Activation Function & Feedforward Neural Networks

Installing Prerequisite Softwares:

  1. Tensorflow
  2. Theano
  3. Keras

3 Layer Neural Network for Customer Churn Modeling

Online Learning(Reinforcement Learning)

Generative Adversarial Networks (GANs)

PyTorch

Image Processing Introduction

  1. OpenCV for Image Processing in Python
  2. Edge Detection
  3. Eye & Nose Detection
  4. Face Detection using Haar cascades
  5. Optical Character Recognition using Neural Networks
  6. Text Detection: Sliding Window
  7. Character Segmentation
  8. Character Classification

Synthetic Character Generation: Shearing & Scaling, Rotation

Revisiting Perceptrons

  1. Coding a Text Classifier in Neural Networks

Advanced Neural Nets:

Long Short Term Memory(LSTM) in RNN

Time Series Data(ARMA, ARIMA)

Unsupervised Learning using Hidden Markov Model(Tensorflow and Theano)

Tensorflow Deep Dive

Speech Recognition

Advanced Text Mining

Building & Deploying a Intelligent Chatbot

  1. Data Preprocessing
  2. Seq2Seq
  3. Deploying the Chat Application

Computer Vision as AI

Image Recoginition and Classification

Deep Neural Networks Architecture revisited

Deep Convolutional Neural Network for Image Recognition:

  1. Convolutions
  2. Pooling, Flattening
  3. LeNet, Fully Connectected Feed Forward Network
  4. Face Recognition using Convolutional Neural Network
  5. Importing Pretrained Models
  6. Running Convolutional Neural Networks on GPU for Image

Unsupervised Learning in Deep Neural Networks Revisited

  1. Current Advancements:
    1. Self Organizing Maps(SOM)
    2. Auto Encoders
    3. Boltzman Machines

VGG, SDD, ResNet

Future Direction: Self Driving Cars, IIoT with AI, Drone based Parcel Delivery, etc

Conclusion

Final Project:

Facial Expression Detection

Course Outline:

  1. Advanced Python Programming Language is covered in Depth
  2. Machine Learning & Data Visualization are covered in Python using language and also using Very advanced ML packages like Tensorflow, Theano and Keras
  3. Data Visualization is covered with Python
  4. All Machine Learning algorithms are covered in Depth, several algorithms are covered with real world solutions. Neural Networks and Deep Neural Nets are covered with real world exampls
  5. Knowledge about data, features & distribution, model building, accuracy are clearly covered
  6. Basics to Advanced statistics are covered
  7. Very advanced practical applications like Recommendation Engine, Threat Detection on Python

 

Benefits of our Courses:

  1. Exposure to advanced Data science concepts through experienced and senior Solution Architect.
  2. Presentations with Live Examples and real project scenarios, Business use cases.
  3. Optionally the students can work in our application(Real time project) based on their interest.
  4. Internships will be offered to high performing students.
  5. Profile Development& Placement assistance.

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