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 NetworksVGGNetsResNetsInceptionNetsMobileNetsEffic ientNetsTransfer 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
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