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    Deep Learning Masterclass With Tensorflow 2 Over 15 Projects

    Posted By: ELK1nG
    Deep Learning Masterclass With Tensorflow 2 Over 15 Projects

    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