Artificial Intelligence Training in Trichy

The main goal of this course is to familiarize you with all aspects of AI so that you can start your career as an artificial intelligence engineer. A few of the many topics/modules that you will learn in the program are:

• Basics of Deep Learning techniques
• Understanding artificial neural networks
• Training a neural network using the training data
• Convolutional neural networks and its applications
• TensorFlow and Tensor processing units
• Supervised and unsupervised learning methods
• Machine Learning using Python
• Applications of Deep Learning in image recognition, NLP, etc.
• Real-world projects in recommender systems, etc.

• Professionals working in the domains of analytics, Data Science, e-commerce, search engine, etc.
• Software professionals and new graduates seeking a career change.

Anyone can take this online course and be a successful machine learning engineer or AI engineer regardless of their previous knowledge.

Today, Artificial Intelligence has conquered almost every industry. Within a year or two, nearly 80% of emerging technologies will be based on AI. Machine Learning, especially Deep Learning, which is the most important aspect of Artificial intelligence, is used from AI-powered recommender systems (Chatbots) and Search engines for online movie recommendations. Therefore, to remain relevant and gain expertise in this emerging technology, enroll in Intellipaat’s AI Course.

This will help you build a solid AI career and get the best artificial intelligence engineer positions in leading organizations.

• Multi-layer network introduction, regularization, deep neural networks
• Multi-layer perceptron
• Overfitting and capacity
• Neural network hyperparameters, logic gates
• Different activation functions used in neural networks, including relu, softmax, sigmoid and hyperbolic functions
• Back propagation, forward propagation, convergence, hyperparameters, and overfitting.

• Various methods that are used to train artificial neural networks
• Perceptron learning rule, gradient descent rule, tuning the learning rate, regularization techniques, optimization techniques
• Stochastic process, vanishing gradients, transfer learning, regression techniques,
• Lasso l1 and ridge l2, unsupervised pre-training, xavier initialization.

• Understanding how deep learning works
• Activation functions, illustrating perceptron, perceptron training
• multi-layer perceptron, key parameters of perceptron;
• Tensorflow introduction and its open-source software library that is used to design, create and train
• Deep learning models followed by google’s tensor processing unit (tpu) programmable ai
• Python libraries in tensorflow, code basics, variables, constants, placeholders
• Graph visualization, use-case implementation, keras, and more.

• Keras high-level neural network for working on top of tensorflow
• Defining complex multi-output models
• Composing models using keras
• Sequential and functional composition, batch normalization
• Deploying keras with tensorboard, and neural network training process customization.

• Using tflearn api to implement neural networks
• Defining and composing models, and deploying tensorboard

• Mapping the human mind with deep neural networks (dnns)
• Several building blocks of artificial neural networks (anns)
• The architecture of dnn and its building blocks
• Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions.

• What is a convolutional neural network?
• Understanding the architecture and use-cases of cnn
• What is a pooling layer?’ how to visualize using cnn
• How to fine-tune a convolutional neural network
• What is transfer learning?
• Understanding recurrent neural networks, kernel filter, feature maps, and pooling, and deploying convolutional neural networks in tensorflow.

• Introduction to the rnn model
• Use cases of rnn, modeling sequences
• Rnns with back propagation
• Long short-term memory (lstm)
• Recursive neural tensor network theory, the basic rnn cell, unfolded rnn, dynamic rnn
• Time-series predictions.

• Gpu’s introduction, ‘how are they different from cpus?,’ the significance of gpus
• Deep learning networks, forward pass and backward pass training techniques
• Gpu constituent with simpler core and concurrent hardware.

• Introduction rbm and autoencoders
• Deploying rbm for deep neural networks, using rbm for collaborative filtering
• Autoencoders features and applications of autoencoders.

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

• Automated conversation bots leveraging any of the following descriptive techniques: Ibm watson, Microsoft’s luis, Open–closed domain bots
• Generative model, and the sequence to sequence model (lstm).