Anybody can take this Training Course regardless of their prior skills.
Artificial Intelligence today is taking over each and every industry domain. Machine Learning and especially Deep Learning are the most important aspects of Artificial Intelligence that are being deployed everywhere from search engines to online movie recommendations. Taking the Intellipaat Deep Learning training can help professionals to build a solid career in a rising technology domain and get the best jobs in top organizations.
The domain of machine learning and its implications to the artificial intelligence sector, the advantages of machine learning over other conventional methodologies, introduction to Deep Learning within machine learning, how it differs from all others methods of machine learning, training the system with training data, supervised and unsupervised learning, classification and regression supervised learning, clustering and association unsupervised learning, the algorithms used in these types of learning. Introduction to AI, Introduction to Neural Networks, Supervised Learning with Neural Networks, Concept of Machine Learning, Basics of statistics, probability distributions, hypothesis testing, Hidden Markov Model.
Introduction to Multi Layer Network, Concept of Deep neural networks, Regularization. Multi-layer perceptron, capacity and overfitting, neural network hyperparameters, logic gates, thevariousactivationfunctions in neural networks like Sigmoid, ReLu and Softmax, hyperbolic functions. Backpropagation, convergence, forward propagation, overfitting, hyperparameters.
The various techniques used in training of artificial neural networks, gradient descent rule, perceptron learning rule, tuning learning rate, stochastic process, optimization techniques, regularization techniques, regression techniques Lasso L1, Ridge L2, vanishing gradients, transfer learning, unsupervised pre-training, Xavier initialization, vanishing gradients.
How Deep Learning Works, Activation Functions, Illustrate Perceptron, Training a Perceptron, Important Parameters of Perceptron,Multi-layer Perceptron What is Tensorflow, Introduction to TensorFlow open source software library for designing, building and training Deep Learning models, Python Library behind TensorFlow, Tensor Processing Unit (TPU) programmable AI accelerator by Google,Tensorflow code-basics, Graph Visualization, Constants, Placeholders, Variables, Step by Step – Use-Case Implementation, Keras.
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, neural network training process customization.
Implementing neural networks using TFLearn API, defining and composing models using TFLearn, deploying TensorBoard with TFLearn.
Mapping the human mind with Deep Neural Networks, the various building blocks of Artificial Neural Networks, the architecture of DNN, its building blocks, the concept of reinforcement learning in DNN, the various parameters, layers, activation functions and optimization algorithms in DNN.
Introduction to CNNs, CNNs Application, Architecture of a CNN, Convolution and Pooling layers in a CNN, Understanding and Visualizing a CNN, Transfer Learning and Fine-tuning Convolutional Neural Networks,feature maps, Kernel filter, pooling, deploying convolutinal neural network in TensorFlow
Intro to RNN Model, Application use cases of RNN, Modelling sequences, Training RNNs with Backpropagation, Long Short-Term memory (LSTM), Recursive Neural Tensor Network Theory, Recurrent Neural Network Model, basic RNN cell, unfolded RNN, training of RNN, dynamic RNN, time-series predictions.
Introduction to GPUs and how they differ from CPUs, the importance of GPUs in training Deep Learning Networks, the forward pass and backward pass training technique, the GPU constituent with simpler core and concurrent hardware.
Introduction to RBM and autoencoders, deploying it for deep neural networks, collaborative filtering using RBM, features of autoencoders, applications of autoencoders.
Automated conversation bots using one of the descriptive techniques
Project 1 : Image recognition with TensorFlow
Industry : Internet Search
Problem Statement : Building a robust deep learning model to recognize the right object on the internet depending on the user search for the image.
Description : In this project you will learn how to build Convolutional Neural Network using Google TensorFlow. You will do visualization of images using training, providing input images, losses and distributions of activations and gradients. You will learn to break each image into manageable tiles and input it to the Convolutional Neural Network for the desired result.
Project 2 : Building an AI-based chatbot
Industry : Ecommerce
Description : This project involves building the chatbots using Artificial Intelligence and Google TensorFlow.
Problem Statement : Understanding the customer needs and offering the right services through Artificial Intelligence chatbot. You will learn how to create the right artificial neural network with the right amount of layers to ensure the customer queries are comprehensible to the Artificial Intelligence chatbot. This will help to understand natural language processing, understanding beyond keywords, data parsing and providing the right solutions.
Project 3 : Ecommerce product recommendation
Industry : Ecommerce
Problem Statement : Recommending the right projects to customers by artificial intelligence
Description : This project involves working with recommender systems to provide the right product recommendation to customers with TensorFlow. You will learn how to use Artificial Intelligence to check for user past buying habits, find out what are the products that go hand-in-hand, and recommend the best products for a particular product.
In Intellipaat self-paced training program you will receive recorded sessions, course material, Quiz,related software’s and assignments.The courses are designed such that you will get real world exposure and focused on clearing relevant certification exam. After completion of training you can take quiz which enable you to check your knowledge and enables you to clear relevant certification at higher marks/grade also you will be able to work on the technology independently.
This course is designed for clearing the Intellipaat AI Deep Learning Certification Exam. The entire training course content is designed by industry professionals to get the best jobs in the top MNCs. As part of this training you will be working on real time projects and assignments that have immense implications in the real world industry scenario thus helping you fast track your career effortlessly.
At the end of this training program there will be quizzes that perfectly reflect the type of questions asked in the respective certification exams and helps you score better marks in certification exam.
Intellipaat Course Completion Certification will be awarded on the completion of Project work (on expert review) and upon scoring of at least 60% 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.
A senior software architect at NextGen Healthcare who has previously worked with IBM Corporation. Suresh has worked on Big Data, Data Science, advanced analytics, Internet of Things, Azure along with AI domains like Machine Learning and Deep Learning. He has successfully implemented high impact projects in major corporations around the world.
A renowned Data Scientist who has worked with Google and currently working at ASCAP. Samanth has a proven ability to develop Data Science strategies that have a high impact on the revenues of organizations. He comes with strong Data Science expertise and has created decisive Data Science strategies for Fortune 500 Corporations.
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