Develop AI and Deep Learning solutions using Python libraries. Weights updating methods - Windrow-Hoff Learning Rule & Rosenblatt’s Perceptron. 5.4 Deploying keras with tensorboard, and neural network training process customization. Our Artificial Intelligence online training involves the simultaneous participation of both learners and instructors in an online environment. We provide Neural Network webinars to watch before attending regular classes. Be able to implement Deep Learning solutions and Image Processing applications using Convolution Neural Networks, Be able to run practical applications of building AI driven games using Reinforcement Learning and Q-Learning, Be able to effectively use various Python libraries such as Keras, TensorFlow, OpenCV, etc., which are used in solving AI and Deep Learning problems, Learn about the applications of Graphical Processing Units (GPUs) & Tensor Processing Units (TPUs) in using Deep Learning Algorithms, Artificial Intelligence Course in Hyderabad, Advanced Program in Digital Marketing Course  |, Life Sciences and HealthCare Analytics Program  |, Certification Program in Financial Analytics  |, Certification Program in Marketing Analytics  |, Certification Program in Supply Chain Analytics  |, Certification Program in Cyber Security Analytics  |, Exclusive Python & R Programme For Beginners, Project Management Professional (PMP) Training, Manufacturing and Automotive Analytics Program, Data Science for Financial Analytics & Auditors, Life Sciences and HealthCare Analytics Program, Lean Six Sigma Green Belt Training in Malaysia, 1. This Artificial Intelligence Course has been conceived and structured to groom consummate AI professionals. Gated Recurrent Unit, a variant of LSTM solves this problem in RNN. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc. Our Learning Management System is title AISPRY. The coursware is comprehensive, and has a variety of material like videos, PPTs, and PDFs that are neatly organized. I recently completed the course and experienced good quality teaching offered by intellipaat. The entire certification training course was up to the mark. 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. COURSE DESCRIPTION. They will investigate the various AI structures and techniques used for problem-solving, inference, perception, knowledge representation, and learning. If 360DigiTMG feels that you need additional help then you might be assigned more than one mentor. At Intellipaat, you can enroll in either the instructor-led online training or self-paced training. Great teaching team, All trainers and support team were very helpful and easily reachable. All classroom sessions are video recorded and lodged in our Learning Management System AISPRY. You will also learn about colors and intensity, affine transformation, projective transformation, embossing, erosion & dilation, vignette, histogram equalization, HAAR cascade for object detection, SIFT, SURF, FAST, BRIEF and seam carving. Also, we record the proceedings of all AI classes and equip you with them to further enhance your learning process. Artificial intelligence Training in Hyderabad, Artificial intelligence Course in Hyderabad, 80 Hours of Intensive Classroom & Online Sessions, Receive Certificate from Technology Leader - IBM, Receive Certificate from Top University - UTM, Malaysia, Those aspiring to be Data scientists, or Deep learning and AI experts, Analytics managers and professionals, Business analysts and developers, Graduates looking for a career in Machine learning, Deep learning or AI, Professionals looking for mid-career shift to AI, All About 360DigiTMG & Innodatatics Inc., USA, Introduction to Artificial intelligence and Deep learning, Course Outline, Road Map and Takeaways from the Course, Cross-Industry Standard Process for Data Mining, Artificial Intelligence and Deep Learning Applications, Introduction to Deep Learning libraries – Torch, Theono, Caffe, Tensorflow, Keras, OpenCV and PyTorch, Deep dive into Tensorflow, Keras, OpenCV and PyTorch, Introduction to Anaconda, R for Windows, R studio and Spyder, Environment Setup and Installation Methods of Multiple Packages, Machine Learning and its types - Supervised Learning, Unsupervised Learning, Reinforcement Learning, Semi-supervised Learning, Active Learning, Transfer Learning, Structured Prediction, Understand Business Problem – Business Objective & Business Constraints, Data Collection - Surveys and Design of Experiments, Data Types namely Continuous, Discrete, Categorical, Count, Qualitative, Quantitative and its identification and application, Further classification of data in terms of Nominal, Ordinal, Interval & Ratio types, Cross-Sectional versus Time Series versus Panel / Longitudinal Data, Batch Processing versus Real-Time Processing, Structured versus Unstructured vs Semi-Structured Data, Data Cleaning / Preparation - Outlier Analysis, Missing Values Imputation Techniques, Transformations, Normalization / Standardization, Discretization, Sampling Techniques for Handling Balanced versus Imbalanced Datasets, Measures of Central Tendency & Dispersion, Population Parameters and Sample Statistics, Various Graphical Techniques to Understand Data, Feature Engineering - Feature Extraction & Feature Selection, Error Functions - Mean Error, Mean Absolute Deviation, Mean Squared Error, Mean Percentage Error, Root Mean Squared Error, Mean Absolute Percentage Error, Cross Table, Confusion Matrix, Binary Cross Entropy & Categorical Cross-Entropy, High-Level Strategy in Handling Machine Learning Projects, Foundations - Slope, Derivatives & Tangent, Derivatives in Optimization: Maxima & Minima - First Derivative Test, Second Derivative Test, Partial Derivatives, Cross Partial Derivatives, Saddle Point, Determinants, Minor and Cofactor, Gradient Descent Method / Optimization - Minima, Maxima & Learning Rate, Compositionality in Data – Images, Speech & text, Components of ANN - Neuron, Weights, Activation function, Integration function, Bias and Output, Activation functions – Identity Function, Step Function, Ramp Function, Sigmoid Function, Tanh Function, ReLU, ELU, Leaky ReLU & Maxout, Network Topology – Key characteristics and Number of layers, Error Surface – Learning Rate & Random Weight Initialization, Local Minima issues in Gradient Descent Learning, Practical Implementation of MLP/ANN in Python – MNIST, IMDB, Reuters & Boston Housing, Segregation of data set: Train, Test & Validation, Data Representation in Graphs using Matplotlib, Deep Learning Challenges – Gradient Primer, Activation Function, Error Function, Vanishing Gradient, Error Surface challenges, Learning Rate challenges, Decay Parameter, Gradient Descent Algorithmic Approaches, Momentum, Nestrov Momentum, Adam, Adagrad, Adadelta & RMSprop, Deep Learning Practical Issues – Avoid Overfitting, DropOut, DropConnect, Noise, Data Augmentation, Parameter Choices, Weights Initialization, ImageNet Challenge – Winning Architectures, Difficult Vision Problems & Hierarchical Approach, Practical Issues – Weight decay, Drop Connect, Data Manipulation Techniques & Batch Normalization, Image Processing Challenges – Interclass Variation, ViewPoint Variation, Illumination, Background Clutter, Occlusion & Number of Large Categories, Introduction to Image – Image Transformation, Image Processing Operations & Simple Point Operations, Noise Reduction – Moving Average & 2D Moving Average, Image Filtering – Linear & Gaussian Filtering, Boundary Effects – Zero, Wrap, Clamp & Mirror, Edge Detection – Image filtering, Origin of Edges, Edges in images as Functions, Sobel Edge Detector, Noise – Reduction using Salt & Pepper Noise using Gaussian Filter, Image Sampling & Interpolation – Image Sub Sampling, Image Aliasing , Nyquist Limit, Wagon Wheel Effect, Down Sampling with Gaussian Filter, Image Pyramid, Image Up Sampling, Image Interpolation – Nearest Neighbour Interpolation, Linear Interpolation, Bilinear Interpolation & Cubic Interpolation, Language Models – Next Word Prediction, Spell Checkers, Mobile Auto Correction, Speech Recognition & Machine Translation, Types of RNN – One to One, One to Many, Many to One, Many to Many, Combining CNN and RNN for Image Captioning, Architecture of CNN and RNN for Image Captioning, Importance of Cell State, Input Gate, Output Gate, Forget Gate, Sigmoid and Tanh, Mathematical Calculations to Process Data in LSTM, Update Gate, Reset Gate, Current Memory Content, Comparison with other Encoders (MP3 and JPEG), Introduction to Restricted Boltzmann Machines - Energy Function, Schematic implementation, Implementation in TensorFlow, Network Architecture - Generator, Discriminator, Loss Function - Discriminator Loss & Generator Loss, Deep Reinforcement Learning vs Atari Games, Experience Replay, or the Value of Experience, Q-Learning and Deep Q-Network as a Q-Function, Integrating and implementing Neural Networks Chatbot, Areas of expertise: Data analytics, Digital Transformation, Industrial Revolution 4.0, Over 14+ years of professional experience, Trained over 2,500 professionals from eight countries, Corporate clients include Hewlett Packard Enterprise, Computer Science Corporation, Akamai, IBS Software, Litmus7, Personiv, Ebreeze, Alshaya, Synchrony Financials, Deloitte, Professional certifications - PMP, PMI-ACP, PMI-RMP from Project Management Institute, Lean Six Sigma Master Black Belt, Tableau Certified Associate, Certified Scrum Practitioner, AgilePM (DSDM Atern), Alumnus of Indian Institute of Technology, Hyderabad and Indian School of Business, Areas of expertise: Data sciences, Machine Learning, Business Intelligence and Data Visualization, Trained over 1,500 professionals across 12 countries, Worked as a Data Scientist for 14+ years across several industry domains, Professional certifications: Lean Six Sigma Green and Black Belt, Information Technology Infrastructure Library, Experienced in Big Data Hadoop, Spark, NoSQL, NewSQL, MongoDB, R, RStudio, Python, Tableau, Cognos, Corporate clients include DuPont, All-Scripts, Girnarsoft (College-dekho, Car-dekho) and many more, Areas of expertise: Data Sciences, Machine Learning, Business Intelligence and Data Visualization, Over 20+ years of industry experience in Data Science and Business Intelligence, Trained professionals from Fortune 500 companies and students at prestigious colleges, Experienced in Cognos, Tableau, Big Data, NoSQL, NewSQL, Corporate clients include Time Inc., Hewlett Packard Enterprise, Dell, Metric Fox (Champions Group), TCS and many more, Use Deep Learning Algorithms to construct AI systems, Program and run all variants of Neural Network Machine Learning Algorithms, Employ Convolution Neural Networks to implement Deep Learning solutions and Image Processing applications, Use Recurrent Neural Networks to analyze sequence data and perform Text Analytics and Natural Language Processing (NLP), Build AI-driven games using Reinforcement Learning and Q - Learning, Learn to employ Python libraries such as Keras, TensorFlow, OpenCV to solve AI and Deep Learning problems, Perceptron Algorithm, Back Propagation Neural Network Algorithm, Artificial Neural Network (ANN), Multilayer Perceptron (MLP), Autoencoder, Restricted Boltzmann Machine (RBM), The blended learning approach includes on-campus training and Interactive online training, 24x7 learning support - anytime, anywhere learning to suit busy schedules, Guaranteed International University Certificate for all of our programs, Job Placement Assistance through our dedicated placement cell and job drives, Guaranteed Live Project Internship on all of our programs along with a certificate from Innodatatics Inc., USA, 360DigiTMG - Data Analytics, Data Science.

.

5e Adventuring Gear, Bird Repellent Ireland, Apple Watch 24 Hour Analog Face, Hurricane Donna Wind Speed, Dod Compressor Review, Sims 4 Stuff Packs Ranked 2020, Isacord Thread Chart Pantone,