Artificial Intelligence Training & Certification

Nerd Geek Lab Artificial Intelligence training with Deep Learning and TensorFlow is a career-oriented course for learning deep neural networks.
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CREATED BY: Jonas Schmedtmann | LAST UPDATED 3/2017

About Artificial Intelligence Training Course

In the national capital Delhi, Nerd Geek Lab provides robust training on Artificial Intelligence and Deep Learning technologies. You will be grounded in concepts like Deep Learning, Machine Learning and how it is better than legacy methodologies in AI. Therefore you will learn the industry required concepts in Artificial Intelligence.

What you will learn in this Deep Learning training?

  • What is Machine Learning?
  • How Machine Learning matters to the Artificial Intelligence field
  • How Deep Learning is different from other forms of Machine Learning
  • The way Deep Neural Networks(DNNs) is modeled on human brain
  • How GPUs differ from CPUs
  • Making use of GPUs to train DNNs

Who should take this Deep Learning Training Course

  • Those who have prior experience in ecommerce, SEO, analytics and Data Science
  • Software professionals can enhance their career through this course

What are the prerequisites for taking this Training Course?

No specific prerequisites for taking this course.

What are the AI job opportunities in Delhi?

Compared to Bangalore and Pune, Delhi is a bigger city and is governed like a state. But even though it is a big city the number of candidates who are familiar in Artificial Intelligence technologies are relatively low as when compared to the software professionals. Therefore it is a great opportunity to invest time and effort in learning breakout technologies like Artificial Intelligence.

What is the AI market trend in Delhi?

Thousands of jobs are present in India on Artificial Intelligence evenly distributed across various cities in India. Delhi has hundreds of jobs on Artificial Intelligence. In Delhi there are Artificial Intelligence startups like Instalocate, Daphnis Labs, Redcarpet.Cash, Fashionscoop, ADD, The Solar Labs, Smartbeings, MetaTensor, Waylo and many others. They and many other Artificial Intelligence companies are keen on hiring deserving candidates.

Why should you take this Deep Learning Training Course?

If you spend your valuable hours in learning our Artificial Intelligence course in Delhi then your career will be set. All throughout your life you will have the support of our operations team who will resolve your subject related queries within 24 hours. As and when the technology upgrades in Artificial Intelligence it will be reflected in the content we provide. So enrolling to this course is synonymous with subscribing to lifelong knowledge of Artificial Intelligence.

Deep Learning Course Content

Introduction to Deep Learning & Neural Networks

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.

Multi-layered Neural networks

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.

Training of neural networks

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.

Deep Learning Libraries

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.

Introduction to Keras API

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.

TFLearn API for TensorFLow

Implementing neural networks using TFLearn API, defining and composing models using TFLearn, deploying TensorBoard with TFLearn.

DNN: Deep Neural Networks

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.

Hyperledger Composer

What is hyperledger composer, benefits of hyperledger composer, conceptual components and structure, example business network car auction market, conceptual components and structures, the model, ACL, script file, metadata, the archive, open development toolset, modeling business networks, testing business networks, hyperledger composer playground, developing application using Hyperledger composer.

CNN: Convolutional Neural Networks

What is a Convolutional Neural Network, understanding the architecture of CNN, 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 and understanding Recurrent Neural Networks,feature maps, Kernel filter, pooling, deploying convolutinal neural network in TensorFlow

RNN: Recurrent Neural Networks

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.

GPU in Deep Learning

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.

Autoencoders & Restricted Boltzmann Machine (RBM)

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

  • IBM Watson
  • Google API.AI
  • Microsoft’s Luis
  • Amazon Lex
  • Generative
  • Open-Close Domain Bots
  • Sequence to Sequence model (LSTM).

Deep Learning Certification

This course is designed for clearing the Nerd Geek Lab 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.