Module 04-Deep Learning Libraries
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 it's an 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.
The entire content of this best artificial intelligence course in Bangalore is developed by leading AI professionals to help you find the best artificial intelligence engineering job at the top MNCs. During the certification training, you will work on real-world projects that will help evaluate your skills and learning in real-time business scenarios, thus helping you accelerate your career effortlessly.
Upon the completion of this artificial intelligence online course, there will be quizzes that reflect the type of questions asked in the certification examination and will help you score better.
Intellipaat Course Completion Certification will be awarded on the completion of the project work (after the expert review) and upon scoring at least 60 percent marks in the quiz. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.
Module 05-Keras API
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.
Module 06-TFLearn API for TensorFlow
6.1 Using tflearn API to implement neural networks
6.2 Defining and composing models, and deploying tensorboard
Module 07-Dnns (deep neural networks)
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.
Module 08-Cnns (convolutional neural networks)
8.1 What is a convolutional neural network?
8.2 Understanding Is the Architecture and the Use of the Cases CNN-
8.3 'is the What are 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.
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