Artificial Intelligence Training in Hyderabad

3.1 Various methods that are used to train artificial neural networks

3.2 Perceptron learning rule, gradient descent rule, tuning the learning rate, regularization techniques, optimization techniques

3.3 Stochastic process, vanishing gradients, transfer learning, regression techniques,

3.4 Lasso l1 and ridge l2, unsupervised pre-training, Xavier initialization.

4.1 Understanding how deep learning works

4.2 Activation functions, illustrating perceptron, perceptron training

4.3 multi-layer perceptron, key parameters of perceptron;

4.4 Tensorflow introduction and its open-source software library that is used to design, create and train

4.5 Deep learning models followed by google’s tensor processing unit (tpu) programmable ai

4.6 Python libraries in tensorflow, code basics, variables, constants, placeholders

4.7 Graph visualization, use-case implementation, keras, and more.

5.1 Keras high-level neural network for working on top of tensorflow

5.2 Defining complex multi-output models

5.3 Composing models using keras

5.3 Sequential and functional composition, batch normalization

5.4 Deploying keras with tensorboard, and neural network training process customization.

6.1 Using tflearn api to implement neural networks

6.2 Defining and composing models, and deploying tensorboard

7.1 Mapping the human mind with deep neural networks (dnns)

7.2 Several building blocks of artificial neural networks (anns)

7.3 The architecture of dnn and its building blocks

7.4 Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions.

8.1 What is a convolutional neural network?

8.2 Understanding the architecture and use-cases of cnn

8.3‘What is a pooling layer?’ how to visualize using cnn

8.4 How to fine-tune a convolutional neural network

8.5 What is transfer learning?

8.6 Understanding recurrent neural networks, kernel filter, feature maps, and pooling, and deploying convolutional neural networks in tensorflow.

9.1 Introduction to the rnn model

9.2 Use cases of rnn, modeling sequences

9.3 Rnns with back propagation

9.4 Long short-term memory (lstm)

9.5 Recursive neural tensor network theory, the basic rnn cell, unfolded rnn,  dynamic rnn

9.6 Time-series predictions.

10.1 Gpu’s introduction, ‘how are they different from cpus?,’ the significance of gpus

10.2 Deep learning networks, forward pass and backward pass training techniques

10.3 Gpu constituent with simpler core and concurrent hardware.

11.1 Introduction  rbm and autoencoders

11.2 Deploying rbm for deep neural networks, using rbm for collaborative filtering

11.3 Autoencoders features and applications of autoencoders.

12.1 Image processing

12.2 Natural language processing (nlp) – Speech recognition, and video analytics.

13.1 Automated conversation bots leveraging any of the following descriptive techniques:  Ibm watson, Microsoft’s luis, Open–closed domain bots,

13.2 Generative model, and the sequence to sequence model (lstm).

In this module, all the discussed modules are summarized again.

In this module, at the end of the course, you will be working on two real time projects for gaining practical experience, conduct mock interviews to evaluate your learned skills, Provides free course materials, frequently ask interview questions and answers, advanced AI tutorials, community question and answers, and also helps in preparing the resumes, etc.