DINS InfoTech | Artificial Intelligence Training Institute in Pimpri Chinchwad Pune

Enquire about this course

Artificial Intelligence is the simulation of the human process by machines (computer systems). These processes include the learning, reasoning, and self-correction.

Artificial Intelligence Syllabus

  • Introduction to Artificial Intelligence and Deep Learning
  • Applications of AI in various industries
  • Introduction to the installation of Anaconda
  • Creating of Environment with stable Python version
  • Introduction to TensorFlow, Keras, OpenCV, Caffe, Theano
  • Installation of required libraries

  • Introduction to Data Optimization
  • Calculus and Derivatives Primer
  • Finding Maxima and Minima using Derivatives in Data Optimization
  • Data Optimization in Minimizing errors in Linear Regression
  • Gradient Descent Optimization
  • Linear Algebra Primer
  • Probability Primer

  • Understand the history of Neural Networks
  • Learn about Perceptron algorithm
  • Understand about Backpropagation Algorithm to update weight
  • Drawbacks of Perceptron Algorithm
  • Introduction to Artificial Neural Networks or Multilayer Perceptron
  • Manual calculation of updating weights of final layer and hidden layers in MLP
  • Understanding of various Activation Functions
  • R code and Python code to understand about practical model building using MNIST dataset

  • Understand about challenges in Gradient
  • Introduction to various Error, Cost, Loss functions
  • ME, MAD, MSE, RMSE, MPE, MAPE, Entropy, Cross Entropy
  • Vanishing / Exploding Gradient
  • Learning Rate (Eta), Decay Parameter, Iteration, Epoch
  • Variants of Gradient Descent
    • Batch Gradient Descent (BGD)
    • Stochastic Gradient Descent (SGD)
    • Mini-batch Stochastic Gradient Descent (Mini-batch SGD)
  • Techniques to overcome challenges of Mini-batch SGD
    • Momentum
    • Nesterov Momentum
    • Adagrad (Adaptive Gradient Learning)
    • Adadelta (Adaptive Delta)
    • RMSProp (Root Mean Squared Propagated)
    • Adam (Adaptive Moment Estimation)

  • Binary classification problem using MLP on IMDB dataset
  • Multi-class classification problem using MLP on Reuters dataset
  • Regression problem using MLP on Boston Housing dataset
  • Types of Machine Learning outcomes – Self-supervised, Reinforcement Learning, etc.
  • Handling imbalanced datasets and avoiding overfitting and underfitting
  • Simple hold-out validation
    • K-Fold validation
    • Iterated K-fold validation with shuffling
    • Adding weight regularization
    • Drop Out and Drop Connect
    • Early Stopping
    • Adding Noise – Data Noise, Label Noise, Gradient Noise
    • Batch Normalization
    • Data Augmentation
    • Weight initialization techniques
    • Xavier, Glorot, Caffe, He

  • Understanding about Computer Vision related applications
  • Various challenges in handling Images and Videos
  • Images to Pixel using Gray Scale and Color images
  • Color Spaces – RGB, YUV, HSV
  • Image Transformations – Affine, Projective, Image Warping
  • Image Operations – Point, Local, Global
  • Image Translation, Rotation, Scaling
  • Image Filtering – Linear Filtering, Non-Linear Filtering, Sharpening Filters
  • Smoothing / Blurring Filters – Mean / Average Filters, Gaussian Filters
  • Embossing, Erosion, Dilation
  • Convolution vs Cross-correlation
  • Boundary Effects, Padding – Zero, Wrap, Clamp, Mirror
  • Template Matching and Orientation of image
  • Edge Detection Filters – Sobel, Laplacian, LoG (Laplacian of Gaussian)
  • Bilateral Filters
  • Canny Edge Detector, Non-maximum Suppression, Hysteresis Thresholding
  • Image Sampling – Sub-sampling, Down-sampling
  • Aliasing, Nyquist rate, Image pyramid
  • Image Up-sampling, Interpolation – Linear, Bilinear, Cubic
  • Detecting Face and eyes in the Video
  • Identifying the interest points, key points
  • Identifying corner points using Harris and Shi-Tomasi Corner Detector
  • Interest point detector algorithms
    • Scale-invariant feature transform (SIFT)
    • Speeded-up robust features (SURF)
    • Features from accelerated segment test (FAST)
    • Binary robust independent elementary features (BRIEF)
    • Oriented FAST and Rotated Brief (ORB)
  • Reducing the size of images using Seam Carving
  • Contour Analysis, Shape Matching and Image segmentation
  • Object Tracking, Object Recognition

  • Understand about various Image related applications
  • Understanding about Convolution Layer and Max-Pooling
  • Practical application when we have small data
  • Building the Convolution Network
  • Pre-processing the data and Performing Data Augmentation
  • Using pre-trained ConvNet models rather than building from scratch
  • Feature Extraction with and without D
  • How to Visualize the outputs of the various Hidden Layers
  • How to Visualize the activation layer outputs and heatmaps

  • Understand about textual data
  • Pre-processing data using words and characters
  • Perform word embeddings by incorporating the embedding layer
  • How to use pre-trained word embeddings
  • Introduction to RNNs – Recurrent layers
  • Understanding LSTM and GRU networks and associated layers
  • Hands-on use case using RNN, LSTM, and GRU
  • Recurrent dropout, Stacking recurrent layers, Bidirectional recurrent layers
  • Solving forecasting problem using RNN
  • Processing sequential data using ConvNets rather than RNN (1D CNN)
  • Building models by combining CNN and RNN

  • Text generation using LSTM and generative recurrent networks
  • Understanding about DeepDream algorithm
  • Image generation using variational autoencoders
  • GANs theory and practical models
  • The Generator, the Discriminator, the Adversarial network
  • Deep Convolution Generative Adversarial networks
  • Producing audio using GAN
  • Unsupervised learning using Autoencoders

  • Q-learning
  • Exploration and Exploitation
  • Experience Replay
  • Model Ensembling

More Details for Artificial Intelligence Course

Artificial Intelligence is the simulation of the human process by machines (computer systems). These processes include the learning, reasoning, and self-correction. We need Artificial Intelligence (AI) because the work that we need to do is increasing day-to-day. So it’s a good idea to automate the routine work. This saves the manpower of the organization and also increases the productivity. Additionally, through this Artificial Intelligence, the company can also get the skilled the persons for the development of the company. Moreover the companies today think that they want to mechanize all the regular and routine work. And they think they can automate those regular works through the simple program because, with the development of data science, automation becomes more common.

There is nothing much prerequisites to pursue the course. It’s good to have a basic knowledge of Data science algorithms and basic knowledge of programming languages like python for the purpose of automation. But it’s not mandatory.

  • Software developers
  • Database Administrators
  • Team leaders
  • System Admin

DINS Infotech offers Artificial Intelligence course, on Regular and Weekend basis.
Online or Classroom training available.
For more details contact on +91-992-375-5189

  • Small batch size
  • Expert faculty
  • Job Assistance for modular course
  • Job Guaranteed for Career courses
  • Practical oriented training
Enquire about this course

Courses

IF YOU ARE LOOKING FOR A REWARDED CAREER?

Contact Us

Quick Links

Courses

DINS InfoTech,1st Floor,Dhage Plaza, Near Dange Chowk, Aundh - Ravet BRTS Rd, in front of Gavane hospital, Laxman Nagar, Thergaon, Pune, Maharashtra 411033
  • 1st Floor, Dhage Plaza,
  • Opp. Gavene Hospital,
  • Aundh-Ravet Road,
  • Laxman Nagar, Near Dange Chowk,
  • Thergaon, Pune-411033

Copyright © 2021 All rights reserved