Transcription
Deep Learning with Keras andTensorFlow
Table of Contents:Program OverviewCertification Details and CriteriaProgram FeaturesCourse CurriculumDelivery ModeCourse End ProjectsPrerequisitesTools CoveredTarget AudienceCustomer ReviewsKey Learning OutcomesAbout UsProgram Overview:Deep learning is one of the newest technological advances in the fields of artificialintelligence and machine learning. This Deep Learning with Keras and TensorFlow courseis designed to help you master deep learning techniques and enables you to build deeplearning models using the Keras and TensorFlow frameworks. These frameworks are usedin deep neural networks and machine learning research, which in turn contributes to thedevelopment and implementation of artificial neural networks.Program Features:34 hours of blended learningOne industry-based course-end projectInteractive learning with Jupyter notebooks integrated labsDedicated mentoring session from faculty of industry expertsDelivery Mode:Blended - Online self-paced learning and live virtual classroomPrerequisites:It is recommended that you first complete the following courses in order to improve yourability to understand the deep learning course’s concepts:Programming FundamentalsStatistics EssentialsConcepts about Machine Learning
Target Audience:Software and IT professionals interested in analyticsData scientistsBusiness/ data analysts who want to understand deep learning techniquesStatisticians with an interest in deep learningKey Learning Outcomes:When you complete this deep learning course, you will be able to accomplish the following:Understand the concepts of Keras and TensorFlow, its main functions, operations, and theexecution pipelineImplement deep learning algorithms, understand neural networks, and traverse the layers ofdata abstractionMaster and comprehend advanced topics such as convolutional neural networks, recurrentneural networks, training deep networks, and high-level interfacesBuild deep learning models using Keras and TensorFlow frameworks and interpret the resultsUnderstand the language and fundamental concepts of artificial neural networks, applicationof autoencoders, and Pytorch and its elementsTroubleshoot and improve deep learning modelsBuild your own deep learning projectDifferentiate between machine learning, deep learning, and artificial intelligenceCertification Details and Criteria:At least 85 percent attendance of one live virtual classroomA score of at least 75 percent in course-end assessmentSuccessful evaluation in the course-end projectCourse Curriculum:Lesson 01 - Course IntroductionIntroduction
Lesson 02 - AI and Deep learning introductionWhat is AI and Deep LearningBrief History of AIRecap: SL, UL and RLDeep Learning: Successes Last DecadeDemo and Discussion: Self-Driving Car Object DetectionApplications of Deep LearningChallenges of Deep LearningDemo and Discussion: Sentiment Analysis Using LSTMFull Cycle of a Deep Learning ProjectKey TakeawaysKnowledge CheckLesson 03 - Artificial Neural NetworkBiological Neuron Vs PerceptronShallow Neural NetworkTraining a PerceptronDemo Code #1: Perceptron (Linear Classification)BackpropagationRole of Activation Functions and BackpropagationDemo Code #2: Activation FunctionDemo Code #3: Backprop IllustrationOptimizationRegularizationDropout layerDemo Code #4: Dropout Illustration, Lesson-end Exercise (Classification Kaggle Dataset)Key TakeawaysKnowledge CheckLesson-end Project
Lesson 04 - Deep Neural Network & ToolsDeep Neural Network: Why and ApplicationsDesigning a Deep Neural NetworkHow to Choose Your Loss Function?Tools for Deep Learning ModelsKeras and its ElementsDemo Code #5: Build a Deep Learning Model Using KerasTensorflow and Its EcosystemDemo Code #6: Build a Deep Learning Model Using TensorflowTFlearnPytorch and its ElementsDemo Code #7: Build a Deep Learning Model Using PytorchDemo Code #8: Lesson-end ExerciseKey TakeawaysKnowledge CheckLesson-end ProjectLesson 05 - Deep Neural Net optimization, tuning,interpretabilityOptimization AlgorithmsSGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, AdamDemo code #9: MNIST DatasetBatch NormalizationDemo Code #10Exploding and Vanishing GradientsHyperparameter TuningDemo Code #11InterpretabilityDemo Code#12: MNIST– Lesson-end Project with Interpretability LessonsWidth vs DepthKey TakeawaysKnowledge CheckLesson-end Project
Lesson 06 - Convolutional Neural NetSuccess and HistoryCNN Network Design and ArchitectureDemo Code #13: KerasDemo Code #14: Two Image Type Classification (Kaggle), Using KerasDeep Convolutional ModelsKey TakeawaysKnowledge CheckLesson-end ProjectLesson 07 - Recurrent Neural NetworksSequence DataSense of TimeRNN IntroductionDemo Code #15: Share Price Prediction with RNNLSTM (Retail Sales Dataset Kaggle)Demo Code #16:Word Embedding and LSTMDemo Code #17: Sentiment Analysis (Movie Review)GRUsLSTM vs GRUsDemo Code #18: Movie Review (Kaggle), Lesson-end Project)Key TakeawaysKnowledge CheckLesson-end ProjectLesson 08 - AutoencodersIntroduction to AutoencodersApplications of AutoencodersAutoencoder for Anomaly DetectionDemo Code #19: Autoencoder Model for MNIST DataKey TakeawaysKnowledge CheckLesson-end Project
Course End Projects:The course includes a real-world, industry-based project. Successful evaluation of the followingproject is a part of the certification eligibility criteria:Project: Pet Classification Model Using CNNIn this project, you build a CNN model that classifies the given pet images correctly intodog and cat images. The code template is given with essential code blocks. TensorFlow canbe used to train the data and calculate the accuracy score on the test dataTools Covered:
Customer Reviews:Abhishek TripathiSenior Software Developer at SAP.Good online content for data science. I completed Data Sciencewith R and Python. The instructors have good knowledge on thesubject. Self-learning videos help a lot, too. Thanks, Simplilearn.Angiras ModakJD Edwards Technical Consultant at EPIQ Softtech Pvt. Ltd.Simplilearn is one of the best online training providers available. Thetrainer was really great in explaining the concepts in detail and alsogave multiple real-world examples. The course content was veryinformative. I understood the concept of CNN. Overall I really enjoyedthe training a lot.A. Anthony DavisGeneral ManagerThe Simplilearn Data Scientist Master’s Program is an awesomecourse! You learn how to solve real-world problems, and the widevariety of projects give you hands-on experience to make youindustry-ready. The lecturers are experts and share their knowledgeenergetically. Thank you for an excellent learning experience.
About Us:Simplilearn is a leader in digital skills training, focused on the emerging technologies thatare transforming our world. Our blended learning approach drives learner engagementand is backed by the industry’s highest completion rates. Partnering with professionals andcompanies, we identify their unique needs and provide outcome-centric solutions to helpthem achieve their professional goals.For more information, please visit our ed in 2009, Simplilearn is one of the world’s leading providers of online training for Digital Marketing,Cloud Computing, Project Management, Data Science, IT Service Management, Software Development andmany other emerging technologies. Based in Bangalore, India, San Francisco, California, and Raleigh, NorthCarolina, Simplilearn partners with companies and individuals to address their unique needs, providingtraining and coaching to help working professionals meet their career goals. Simplilearn has enabled over 1million professionals and companies across 150 countries train, certify and upskill their employees.Simplilearn’s 400 training courses are designed and updated by world-class industry experts. Theirblended learning approach combines e-learning classes, instructor-led live virtual classrooms, appliedlearning projects, and 24/7 teaching assistance. More than 40 global training organizations have recognizedSimplilearn as an official provider of certification training. The company has been named the 8th mostinfluential education brand in the world by LinkedIn.India – United States – Singapore 2009-2019 - Simplilearn Solutions. All Rights Reserved.The certification names are the trademarks of their respective owners.
intelligence and machine learning. This Deep Learning with Keras and TensorFlow course is designed to help you master deep learning techniques and enables you to build deep learning models using the Keras and TensorFlow frameworks. These frameworks are used in deep neural networks and ma
