Deep Learning Masterclass With Tensorflow 2 Over 15 Projects

Published 6/2022
MP4 | Video: h264, 1280×720 | 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

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