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Deep Learning Masterclass With Tensorflow 2 Over 15 Projects - Panter - 31.07.2022 Deep Learning Masterclass With Tensorflow 2 Over 15 Projects Published 6/2022 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 18.00 GB | Duration: 43h 40m Master Deep Learning with TensorFlow 2 with Computer Vision,Natural Language Processing, Sound Recognition & Deployment What you'll learn Introductory Python, to more advanced concepts like Object Oriented Programming, decorators, generators, and even specialized libraries like Numpy & Matplotlib Mastery of the fundamentals of Machine Learning and The Machine Learning Developmment Lifecycle. Linear Regression, Logistic Regression and Neural Networks built from scratch. TensorFlow installation, Basics and training neural networks with TensorFlow 2. Convolutional Neural Networks, Modern ConvNets, training object recognition models with TensorFlow 2. Breast Cancer detection, people counting, object detection with yolo and image segmentation Generative Adversarial neural networks from scratch and image generation Recurrent Neural Networks, Modern RNNs, training sentiment analysis models with TensorFlow 2. Neural Machine Translation, Question Answering, Image Captioning, Sentiment Analysis, Speech recognition Deploying a Deep Learning Model with Google Cloud Function. Requirements Basic Math No Programming experience. You will learn everything you need to know Description In this course, we shall look at core Deep Learning concepts and apply our knowledge to solve real world problems in Computer Vision and Natural Language Processing using the Python Programming Language and TensorFlow 2. We shall explain core Machine Learning topics like Linear Regression, Logistic Regression, Multi-class classification and Neural Networks. If you've gotten to this point, it means you are interested in mastering Deep Learning For Computer Vision and Deep Learning, using your skills to solve practical problems.You may already have some knowledge on Machine learning, Computer vision, Natural Language Processing or Deep Learning, or you may be coming in contact with Deep Learning for the very first time. It doesn't matter from which end you come from, because at the end of this course, you shall be an expert with much hands-on experience.You shall work on several projects like object detection, image generation, object counting, object recognition, disease detection, image segmentation, Sentiment Analysis, Machine Translation, Question Answering, Image captioning, speech recognition and more, using knowledge gained from this course.If you are willing to move a step further in your career, this course is destined for you and we are super excited to help achieve your goals!This course is offered to you by Neuralearn. And just like every other course by Neuralearn, we lay much emphasis on feedback. Your reviews and questions in the forum, will help us better this course. Feel free to ask as many questions as possible on the forum. We do our very best to reply in the shortest possible time.Here are the different concepts you'll master after completing this course.Fundamentals Machine Learning.Essential Python ProgrammingChoosing Machine Model based on taskError sanctioningLinear RegressionLogistic RegressionMulti-class RegressionNeural NetworksTraining and optimizationPerformance MeasurementValidation and TestingBuilding Machine Learning models from scratch in python.Overfitting and UnderfittingShufflingEnsemblingWeight initializationData imbalanceLearning rate decayNormalizationHyperparameter tuningTensorFlow InstallationTraining neural networks with TensorFlow 2Imagenet training with TensorFlowConvolutional Neural NetworksVGGNetsResNetsInceptionNetsMobileNetsEfficientNetsTransfer Learning and FineTuningData AugmentationCallbacksMonitoring with TensorboardBreast cancer detectionObject detection with YOLOImage segmentation with UNETsPeople countingGenerative modeling with GANsImage generationIMDB Dataset Sentiment AnalysisRecurrent Neural Networks.LSTMGRU1D ConvolutionBi directional RNNWord2VecMachine TranslationAttention ModelTransformer NetworkVision TransformersLSH AttentionImage CaptioningQuestion AnsweringBERT ModelHuggingFaceDeploying A Deep Learning Model with Google Cloud FunctionsWho this course is for:Beginner Python Developers curious about Applying Deep Learning for Computer vision and NLPComputer Vision practitioners who want to learn how state of art computer vision models are built and trained using deep learning.Anyone who wants to master deep learning fundamentals and also practice deep learning for computer vision using best practices in TensorFlow.Deep Learning for Computer vision Practitioners who want gain a mastery of how things work under the hood.NLP practitioners who want to learn how state of art Natural Language Processing models are built and trained using deep learning.Anyone who wants to master deep learning fundamentals and also practice deep learning for NLP using best practices in TensorFlow 2.Deep Learning for NLP Practitioners who want gain a mastery of how things work under the hood.ENjoy!!!Let's make this course as interactive as possible, so that we still gain that classroom experience. Overview Section 1: Introduction Lecture 1 Welcome Lecture 2 General Introduction Lecture 3 Applications of Deep Learning Lecture 4 About this Course Section 2: Essential Python Programming Lecture 5 Python Installation Lecture 6 Variables and Basic Operators Lecture 7 Conditional Statements Lecture 8 Loops Lecture 9 Methods Lecture 10 Objects and Classes Lecture 11 Operator Overloading Lecture 12 Method Types Lecture 13 Inheritance Lecture 14 Encapsulation Lecture 15 Polymorphism Lecture 16 Decorators Lecture 17 Generators Lecture 18 Numpy Package Lecture 19 Matplotlib Introduction Section 3: Introduction to Machine Learning Lecture 20 Task - Machine Learning Development Life Cycle Lecture 21 Data - Machine Learning Development Life Cycle Lecture 22 Model - Machine Learning Development Life Cycle Lecture 23 Error Sanctioning - Machine Learning Development Life Cycle Lecture 24 Linear Regression Lecture 25 Logistic Regression Lecture 26 Linear Regression Practice Lecture 27 Logistic Regression Practice Lecture 28 Optimization Lecture 29 Performance Measurement Lecture 30 Validation and Testing Lecture 31 Softmax Regression - Data Lecture 32 Softmax Regression - Modeling Lecture 33 Softmax Regression - Errror Sanctioning Lecture 34 Softmax Regression - Training and Optimization Lecture 35 Softmax Regression - Performance Measurement Lecture 36 Neural Networks - Modeling Lecture 37 Neural Networks - Error Sanctioning Lecture 38 Neural Networks - Training and Optimization Lecture 39 Neural Networks - Training and Optimization Practicals Lecture 40 Neural Networks - Performance Measurement Lecture 41 Neural Networks - Validation and testing Lecture 42 Solving Overfitting and Underfitting Lecture 43 Shuffling Lecture 44 Ensembling Lecture 45 Weight Initialization Lecture 46 Data Imbalance Lecture 47 Learning rate decay Lecture 48 Normalization Lecture 49 Hyperparameter tuning Lecture 50 In Class Exercise Section 4: Introduction to TensorFlow 2 Lecture 51 TensorFlow Installation Lecture 52 Introduction to TensorFlow Lecture 53 TensorFlow Basics Lecture 54 Training a Neural Network with TensorFlow Section 5: Introduction to Deep Computer Vision with TensorFlow 2 Lecture 55 Tiny Imagenet Dataset Lecture 56 TinyImagenet Preparation Lecture 57 Introduction to Convolutional Neural Networks Lecture 58 Error Sanctioning Lecture 59 Training, Validation and Performance Measurement Lecture 60 Reducing overfitting Lecture 61 VGGNet Lecture 62 InceptionNet Lecture 63 ResNet Lecture 64 MobileNet Lecture 65 EfficientNet Lecture 66 Transfer Learning and FineTuning Lecture 67 Data Augmentation Lecture 68 Callbacks Lecture 69 Monitoring with TensorBoard Lecture 70 ConvNet Project 1 Lecture 71 ConvNet Project 2 Section 6: Introduction to Deep NLP with TensorFlow 2 Lecture 72 Sentiment Analysis Dataset Lecture 73 Imdb Dataset Code Lecture 74 Recurrent Neural Networks Lecture 75 Training and Optimization, Evaluation Lecture 76 Embeddings Lecture 77 LSTM Lecture 78 GRU Lecture 79 1D Convolutions Lecture 80 Bidirectional RNNs Lecture 81 Word2Vec Lecture 82 Word2Vec Practice Lecture 83 RNN Project Section 7: Breast Cancer Detection Lecture 84 Breast Cancer Dataset Lecture 85 ResNet Model Lecture 86 Training and Performance Measurement Lecture 87 Corrective Measures Lecture 88 Plant Disease Project Section 8: Object Detection with YOLO Lecture 89 Object Detection Lecture 90 Pascal VOC Dataset Lecture 91 Modeling - YOLO v1 Lecture 92 Error Sanctioning Lecture 93 Training and Optimization Lecture 94 Testing Lecture 95 Performance Measurement - Mean Average Precision (mAP) Lecture 96 Data Augmentation Lecture 97 YOLO v3 Lecture 98 Instance Segmentation Project Section 9: Semantic Segmentation with UNET Lecture 99 Image Segmentation - Oxford IIIT Pet Dataset Lecture 100 UNET model Lecture 101 Training and Optimization Lecture 102 Data Augmentation and Dropout Lecture 103 Class weighting and reduced network Lecture 104 Semantic Segmentation Project Section 10: People Counting Lecture 105 People Counting - Shangai Tech Dataset Lecture 106 Dataset Preparation Lecture 107 CSRNET Lecture 108 Training and Optimization Lecture 109 Data Augmentation Lecture 110 Object Counting Project Section 11: Neural Machine Translation with TensorFlow 2 Lecture 111 Fre-Eng Dataset and Task Lecture 112 Sequence to Sequence Models Lecture 113 Training Sequence to Sequence Models Lecture 114 Performance Measurement - BLEU Score Lecture 115 Testing Sequence to Sequence Models Lecture 116 Attention Mechanism - Bahdanau Attention Lecture 117 Transformers Theory Lecture 118 Building Transformers with TensorFlow 2 Lecture 119 Text Normalization project Section 12: Question Answering with TensorFlow 2 Lecture 120 Understanding Question Answering Lecture 121 SQUAD dataset Lecture 122 SQUAD dataset preparation Lecture 123 Context - Answer Network Lecture 124 Training and Optimization Lecture 125 Data Augmentation Lecture 126 LSH Attention Lecture 127 BERT Model Lecture 128 BERT Practice Lecture 129 GPT Based Chatbot Section 13: Automatic Speech Recognition Lecture 130 What is Automatic Speech Recognition Lecture 131 LJ- Speech Dataset Lecture 132 Fourier Transform Lecture 133 Short Time Fourier Transform Lecture 134 Conv - CTC Model Lecture 135 Speech Transformer Lecture 136 Audio Classification project Section 14: Image Captioning Lecture 137 Flickr 30k Dataset Lecture 138 CNN- Transformer Model Lecture 139 Training and Optimization Lecture 140 Vision Transformers Lecture 141 OCR Project Section 15: Image Generative Modeling Lecture 142 Introduction to Generative Modeling Lecture 143 Image Generation Lecture 144 GAN Loss Lecture 145 GAN training and Optimization Lecture 146 Wasserstein GAN Lecture 147 Image to Image Translation Project Section 16: Shipping a Model with Google Cloud Function Lecture 148 Introduction Lecture 149 Model Preparation Lecture 150 Deployment Beginner Python Developers curious about Applying Deep Learning for Computer vision and Natural Language Processing,Deep Learning for Computer vision Practitioners who want gain a mastery of how things work under the hood,Anyone who wants to master deep learning fundamentals and also practice deep learning for computer vision using best practices in TensorFlow.,Computer Vision practitioners who want to learn how state of art computer vision models are built and trained using deep learning.,Natural Language Processing practitioners who want to learn how state of art NLP models are built and trained using deep learning.,Anyone wanting to deploy ML Models,Learners who want a practical approach to Deep learning for Computer vision, Natural Language Processing and Sound recognition Download from RapidGator Download from Rapidgator: Download from Keep2Share |