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    Deep Learning Masterclass With Tensorflow 2 Over 20 Projects

    Posted By: ELK1nG
    Deep Learning Masterclass With Tensorflow 2 Over 20 Projects

    Deep Learning Masterclass With Tensorflow 2 Over 20 Projects
    Last updated 2/2023
    MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
    Language: English | Size: 45.88 GB | Duration: 102h 36m

    Master Deep Learning with TensorFlow 2 with Computer Vision,Natural Language Processing, Sound Recognition & Deployment

    What you'll learn

    The Basics of Tensors and Variables with Tensorflow

    Basics of Tensorflow and training neural networks with TensorFlow 2.

    Convolutional Neural Networks applied to Malaria Detection

    Building more advanced Tensorflow models with Functional API, Model Subclassing and Custom Layers

    Evaluating Classification Models using different metrics like: Precision,Recall,Accuracy and F1-score

    Classification Model Evaluation with Confusion Matrix and ROC Curve

    Tensorflow Callbacks, Learning Rate Scheduling and Model Check-pointing

    Mitigating Overfitting and Underfitting with Dropout, Regularization, Data augmentation

    Data augmentation with TensorFlow using TensorFlow image and Keras Layers

    Advanced augmentation strategies like Cutmix and Mixup

    Data augmentation with Albumentations with TensorFlow 2 and PyTorch

    Custom Loss and Metrics in TensorFlow 2

    Eager and Graph Modes in TensorFlow 2

    Custom Training Loops in TensorFlow 2

    Integrating Tensorboard with TensorFlow 2 for data logging, viewing model graphs, hyperparameter tuning and profiling

    Machine Learning Operations (MLOps) with Weights and Biases

    Experiment tracking with Wandb

    Hyperparameter tuning with Wandb

    Dataset versioning with Wandb

    Model versioning with Wandb

    Human emotions detection

    Modern convolutional neural networks(Alexnet, Vggnet, Resnet, Mobilenet, EfficientNet)

    Transfer learning

    Visualizing convnet intermediate layers

    Grad-cam method

    Model ensembling and class imbalance

    Transformers in Vision

    Model deployment

    Conversion from tensorflow to Onnx Model

    Quantization Aware training

    Building API with Fastapi

    Deploying API to the Cloud

    Object detection from scratch with YOLO

    Image Segmentation from scratch with UNET model

    People Counting from scratch with Csrnet

    Digit generation with Variational autoencoders (VAE)

    Face generation with Generative adversarial neural networks (GAN)

    Sentiment Analysis with Recurrent neural networks, Attention Models and Transformers from scratch

    Neural Machine Translation with Recurrent neural networks, Attention Models and Transformers from scratch

    Intent Classification with Deberta in Huggingface transformers

    Neural Machine Translation with T5 in Huggingface transformers

    Extractive Question Answering with Longformer in Huggingface transformers

    E-commerce search engine with Sentence transformers

    Lyrics Generator with GPT2 in Huggingface transformers

    Grammatical Error Correction with T5 in Huggingface transformers

    Elon Musk Bot with BlenderBot in Huggingface transformers

    Requirements

    Basic Math

    Access to an internet connection, as we shall be using Google Colab (free version)

    Basic Knowledge of Python

    Description

    Deep Learning is one of the most popular fields in computer science today. It has applications in many and very varied domains. With the publishing of much more efficient deep learning models in the early 2010s, we have seen a great improvement in the state of the art in domains like Computer Vision, Natural Language Processing, Image Generation, and Signal Processing. The demand for Deep Learning engineers is skyrocketing and experts in this field are highly paid, because of their value. However, getting started in this field isn’t easy. There’s so much information out there, much of which is outdated and many times don't take the beginners into consideration :(In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step and project-based approach. You shall be using Tensorflow 2 (the world's most popular library for deep learning, and built by Google) and Huggingface. We shall start by understanding how to build very simple models (like Linear regression models for car price prediction, text classifiers for movie reviews, binary classifiers for malaria prediction) using Tensorflow and Huggingface transformers, to more advanced models (like object detection models with YOLO, lyrics generator model with GPT2 and Image generation with GANs)After going through this course and carrying out the different projects, you will develop the skill sets needed to develop modern deep-learning solutions that big tech companies encounter.You will learn:The Basics of Tensorflow (Tensors, Model building, training, and evaluation)Deep Learning algorithms like Convolutional neural networks and Vision TransformersEvaluation of Classification Models (Precision, Recall, Accuracy, F1-score, Confusion Matrix, ROC Curve)Mitigating overfitting with Data augmentationAdvanced Tensorflow concepts like Custom Losses and Metrics, Eager and Graph Modes and Custom Training Loops, TensorboardMachine Learning Operations (MLOps) with Weights and Biases (Experiment Tracking, Hyperparameter Tuning, Dataset Versioning, Model Versioning)Binary Classification with Malaria detection Multi-class Classification with Human Emotions DetectionTransfer learning with modern Convnets (Vggnet, Resnet, Mobilenet, Efficientnet) and Vision Transformers (VITs)Object Detection with YOLO (You Only Look Once)Image Segmentation with UNetPeople Counting with Csrnet Model Deployment (Distillation, Onnx format, Quantization, Fastapi, Heroku Cloud)Digit generation with Variational AutoencodersFace generation with Generative Adversarial Neural NetworksText Preprocessing for Natural Language Processing.Deep Learning algorithms like Recurrent Neural Networks, Attention Models, Transformers, and Convolutional neural networks.Sentiment analysis with RNNs, Transformers, and Huggingface Transformers (Deberta)Transfer learning with Word2vec and modern Transformers (GPT, Bert, ULmfit, Deberta, T5…)Machine translation with RNNs, attention, transformers, and Huggingface Transformers (T5)Model Deployment (Onnx format, Quantization, Fastapi, Heroku Cloud)Intent Classification with Deberta in Huggingface transformersNamed Entity Relation with Roberta in Huggingface transformersNeural Machine Translation with T5 in Huggingface transformersExtractive Question Answering with Longformer in Huggingface transformersE-commerce search engine with Sentence transformersLyrics Generator with GPT2 in Huggingface transformersGrammatical Error Correction with T5 in Huggingface transformersElon Musk Bot with BlenderBot in Huggingface transformersSpeech recognition with RNNsIf 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.Enjoy!!!

    Overview

    Section 1: Introduction

    Lecture 1 Welcome

    Lecture 2 General Introduction

    Lecture 3 Link to Code

    Section 2: Tensors and Variables

    Lecture 4 Tensor Basics

    Lecture 5 Tensor Initialization and Casting

    Lecture 6 Indexing

    Lecture 7 Maths Operations in Tensorflow

    Lecture 8 Linear Algebra Operations in Tensorflow

    Lecture 9 Ragged Tensors

    Lecture 10 Sparse Tensors

    Lecture 11 String Tensors

    Lecture 12 Tensorflow Variables

    Section 3: Building a Simple Neural Network in Tensorflow

    Lecture 13 Task Understanding

    Lecture 14 Data Preparation

    Lecture 15 Linear Regression Model

    Lecture 16 Error sanctioning

    Lecture 17 Training and Optimization

    Lecture 18 Performance Measurement

    Lecture 19 Validation and testing

    Lecture 20 Corrective Measures

    Section 4: Building Convolutional Neural Networks [Malaria Diagnosis]

    Lecture 21 Task understanding

    Lecture 22 Data Preparation

    Lecture 23 Data visualization

    Lecture 24 Data Processing

    Lecture 25 How and Why Convolutional Neural Networks work

    Lecture 26 Building Convnets in Tensorflow

    Lecture 27 Binary Crossentropy loss

    Lecture 28 Convnet training

    Lecture 29 Model evaluation and testing

    Lecture 30 Loading and Saving Tensorflow Models to Google Drive

    Section 5: Building more advanced Models with Functional API, Subclassing and Custom Layers

    Lecture 31 Functional API

    Lecture 32 Model Subclassing

    Lecture 33 Custom Layers

    Section 6: Evaluating Classification Models

    Lecture 34 Precision,Recall and Accuracy

    Lecture 35 Confusion Matrix

    Lecture 36 ROC Plots

    Section 7: Improving Model Performance

    Lecture 37 Tensorflow Callbacks

    Lecture 38 Learning rate scheduling

    Lecture 39 Model checkpointing

    Lecture 40 Mitigating overfitting and underfitting

    Section 8: Data augmentation

    Lecture 41 Data augmentation with TensorFlow using tf.image and Keras Layers

    Lecture 42 Mixup Data augmentation with TensorFlow 2 with intergration in tf.data

    Lecture 43 Cutmix Data augmentation with TensorFlow 2 and intergration in tf.data

    Lecture 44 Albumentations with TensorFlow 2 and PyTorch for Data augmentation

    Section 9: Advanced Tensorflow Concepts

    Lecture 45 Custom Loss and Metrics

    Lecture 46 Eager and graph modes

    Lecture 47 Custom training loops

    Section 10: Tensorboard integration

    Lecture 48 Data logging

    Lecture 49 Viewing model graphs

    Lecture 50 Hyperparameter tuning

    Lecture 51 Profiling and other visualizations with Tensorboard.

    Section 11: MLOps with Weights and Biases

    Lecture 52 Experiment tracking

    Lecture 53 Hyperparameter tuning with wandb

    Lecture 54 Dataset Versioning with Weights and Biases and TensorFlow 2

    Lecture 55 Model Versioning with Weights and Biases and TensorFlow 2

    Section 12: Human Emotions Detection

    Lecture 56 Data preparation

    Lecture 57 Modeling and training

    Lecture 58 Data augmentation

    Lecture 59 Tensorflow records

    Section 13: Modern Convolutional Neural Networks

    Lecture 60 Alexnet

    Lecture 61 Vggnet

    Lecture 62 Resnet

    Lecture 63 Coding Resnets

    Lecture 64 Mobilenet

    Lecture 65 Efficientnet

    Section 14: Transfer Learning

    Lecture 66 Leveraging pretrained models

    Lecture 67 Finetuning

    Section 15: Understanding the blackbox

    Lecture 68 Visualizing intermediate layers

    Lecture 69 Grad-cam method

    Section 16: Ensembling and class imbalance

    Lecture 70 Ensembling

    Lecture 71 Class Imbalance

    Section 17: Transformers in Vision

    Lecture 72 Understanding VITs

    Lecture 73 Building VITs from scratch

    Lecture 74 Finetuning Huggingface transformers

    Lecture 75 Model evaluation with wandb

    Lecture 76 Data efficient transformers

    Lecture 77 Swin transformers

    Section 18: Model deployment

    Lecture 78 Model Conversion from Tensorflow to Onnx

    Lecture 79 Understanding quantization

    Lecture 80 Practical quantization of Onnx

    Lecture 81 Quantization aware training

    Lecture 82 Conversion to Tensorflow Lite

    Lecture 83 What is an API

    Lecture 84 Building the Emotions Detection API with Fastapi

    Lecture 85 Deploy the Emotions Detection API to the Cloud

    Lecture 86 Load tesing the Emotions Detection API with Locust

    Section 19: Object Detection with YOLO algorithm

    Lecture 87 Understanding object detection

    Lecture 88 YOLO paper

    Lecture 89 Dataset Preparation

    Lecture 90 YOLO Resnet

    Lecture 91 Data augmentation

    Lecture 92 Testing

    Lecture 93 Data generators

    Section 20: Image segmentation

    Lecture 94 Image Segmentation - Oxford IIIT Pet Dataset

    Lecture 95 UNET Model

    Lecture 96 Training and optimization

    Lecture 97 Data augmentation and dropout

    Lecture 98 Class weighting

    Section 21: People counting

    Lecture 99 People Counting - Shangai Tech Dataset

    Lecture 100 Dataset preparation

    Lecture 101 CSRNET

    Lecture 102 Training and optimization

    Lecture 103 Data augmentation

    Section 22: Image generation

    Lecture 104 Introduction to image generation

    Lecture 105 Understanding variational autoencoders

    Lecture 106 VAE training and digit generation

    Lecture 107 Latent space visualizations

    Lecture 108 How GANs work

    Lecture 109 Improving GAN training

    Lecture 110 Face generation with GANs

    Section 23: Text Preprocessing for Sentiment analysis

    Lecture 111 Understanding sentiment analysis

    Lecture 112 Text standardization

    Lecture 113 Tokenization

    Lecture 114 One-hot encoding and Bag of Words

    Lecture 115 Term frequency - Inverse Document frequency (TF-IDF)

    Lecture 116 Embeddings

    Section 24: Sentiment Analysis with Recurrent neural networks

    Lecture 117 How Recurrent neural networks work

    Lecture 118 Data preparation

    Lecture 119 Building and training RNNs

    Lecture 120 Advanced RNNs (LSTM and GRU)

    Lecture 121 1D Convolutional Neural Network

    Section 25: Sentiment Analysis with transfer learning

    Lecture 122 Understanding Word2vec

    Lecture 123 Integrating pretrained Word2vec embeddings

    Lecture 124 Testing

    Lecture 125 Visualizing embeddings

    Section 26: Neural Machine Translation with Recurrent Neural Networks

    Lecture 126 Understanding Machine Translation

    Lecture 127 Data preparation

    Lecture 128 Building, training and testing Model

    Lecture 129 Understanding BLEU Score

    Lecture 130 Coding BLEU score from scratch

    Section 27: Neural Machine Translation with Attention

    Lecture 131 Understanding Bahdanau Attention

    Lecture 132 Building, training and testing Bahdanau Attention

    Section 28: Neural Machine Translation with Transformers

    Lecture 133 Understanding Transformer Networks

    Lecture 134 Building, training and testing Transformers

    Lecture 135 Building Transformers with Custom Attention Layer

    Lecture 136 Visualizing Attention scores

    Section 29: Sentiment Analysis with Transformers

    Lecture 137 Sentiment analysis with Transformer encoder

    Lecture 138 Sentiment analysis with LSH Attention

    Section 30: Transfer Learning and Generalized Language Models

    Lecture 139 Understanding Transfer Learning

    Lecture 140 Ulmfit

    Lecture 141 Gpt

    Lecture 142 Bert

    Lecture 143 Albert

    Lecture 144 Gpt2

    Lecture 145 Roberta

    Lecture 146 T5

    Section 31: Sentiment Analysis with Deberta in Huggingface transformers

    Lecture 147 Data Preparation

    Lecture 148 Building,training and testing model

    Section 32: Intent Classification with Deberta in Huggingface transformers

    Lecture 149 Problem Understanding and Data Preparation

    Lecture 150 Building,training and testing model

    Section 33: Named Entity Relation with Roberta in Huggingface transformers

    Lecture 151 Problem Understanding and Data Preparation

    Lecture 152 Building,training and testing model

    Section 34: Extractive Question Answering with Longformer in Huggingface transformers

    Lecture 153 Problem Understanding and Data Preparation

    Lecture 154 Building,training and testing model

    Section 35: Ecommerce search engine with Sentence transformers

    Lecture 155 Problem Understanding and Sentence Embeddings

    Lecture 156 Dataset preparation

    Lecture 157 Building,training and testing model

    Section 36: Lyrics Generator with GPT2 in Huggingface transformers

    Lecture 158 Problem Understanding and Data Preparation

    Lecture 159 Building,training and testing model

    Section 37: Grammatical Error Correction with T5 in Huggingface transformers

    Lecture 160 Problem Understanding and Data Preparation

    Lecture 161 Building,training and testing model

    Section 38: Elon Musk Bot with BlenderBot in Huggingface transformers

    Lecture 162 Problem Understanding and Data Preparation

    Lecture 163 Building,training and testing model

    Section 39: [DEPRECATED] Introduction

    Lecture 164 Welcome

    Lecture 165 General Introduction

    Lecture 166 Applications of Deep Learning

    Lecture 167 About this Course

    Lecture 168 Link to Code

    Section 40: Essential Python Programming

    Lecture 169 Python Installation

    Lecture 170 Variables and Basic Operators

    Lecture 171 Conditional Statements

    Lecture 172 Loops

    Lecture 173 Methods

    Lecture 174 Objects and Classes

    Lecture 175 Operator Overloading

    Lecture 176 Method Types

    Lecture 177 Inheritance

    Lecture 178 Encapsulation

    Lecture 179 Polymorphism

    Lecture 180 Decorators

    Lecture 181 Generators

    Lecture 182 Numpy Package

    Lecture 183 Matplotlib Introduction

    Section 41: [DEPRECATED] Introduction to Machine Learning

    Lecture 184 Task - Machine Learning Development Life Cycle

    Lecture 185 Data - Machine Learning Development Life Cycle

    Lecture 186 Model - Machine Learning Development Life Cycle

    Lecture 187 Error Sanctioning - Machine Learning Development Life Cycle

    Lecture 188 Linear Regression

    Lecture 189 Logistic Regression

    Lecture 190 Linear Regression Practice

    Lecture 191 Logistic Regression Practice

    Lecture 192 Optimization

    Lecture 193 Performance Measurement

    Lecture 194 Validation and Testing

    Lecture 195 Softmax Regression - Data

    Lecture 196 Softmax Regression - Modeling

    Lecture 197 Softmax Regression - Errror Sanctioning

    Lecture 198 Softmax Regression - Training and Optimization

    Lecture 199 Softmax Regression - Performance Measurement

    Lecture 200 Neural Networks - Modeling

    Lecture 201 Neural Networks - Error Sanctioning

    Lecture 202 Neural Networks - Training and Optimization

    Lecture 203 Neural Networks - Training and Optimization Practicals

    Lecture 204 Neural Networks - Performance Measurement

    Lecture 205 Neural Networks - Validation and testing

    Lecture 206 Solving Overfitting and Underfitting

    Lecture 207 Shuffling

    Lecture 208 Ensembling

    Lecture 209 Weight Initialization

    Lecture 210 Data Imbalance

    Lecture 211 Learning rate decay

    Lecture 212 Normalization

    Lecture 213 Hyperparameter tuning

    Lecture 214 In Class Exercise

    Section 42: [DEPRECATED] Introduction to TensorFlow 2

    Lecture 215 TensorFlow Installation

    Lecture 216 Introduction to TensorFlow

    Lecture 217 TensorFlow Basics

    Lecture 218 Training a Neural Network with TensorFlow

    Section 43: [DEPRECATED] Introduction to Deep Computer Vision with TensorFlow 2

    Lecture 219 Tiny Imagenet Dataset

    Lecture 220 TinyImagenet Preparation

    Lecture 221 Introduction to Convolutional Neural Networks

    Lecture 222 Error Sanctioning

    Lecture 223 Training, Validation and Performance Measurement

    Lecture 224 Reducing overfitting

    Lecture 225 VGGNet

    Lecture 226 InceptionNet

    Lecture 227 ResNet

    Lecture 228 MobileNet

    Lecture 229 EfficientNet

    Lecture 230 Transfer Learning and FineTuning

    Lecture 231 Data Augmentation

    Lecture 232 Callbacks

    Lecture 233 Monitoring with TensorBoard

    Lecture 234 ConvNet Project 1

    Lecture 235 ConvNet Project 2

    Section 44: [DEPRECATED] Introduction to Deep NLP with TensorFlow 2

    Lecture 236 Sentiment Analysis Dataset

    Lecture 237 Imdb Dataset Code

    Lecture 238 Recurrent Neural Networks

    Lecture 239 Training and Optimization, Evaluation

    Lecture 240 Embeddings

    Lecture 241 LSTM

    Lecture 242 GRU

    Lecture 243 1D Convolutions

    Lecture 244 Bidirectional RNNs

    Lecture 245 Word2Vec

    Lecture 246 RNN Project

    Section 45: [DEPRECATED] Breast Cancer Detection

    Lecture 247 Breast Cancer Dataset

    Lecture 248 ResNet Model

    Lecture 249 Training and Performance Measurement

    Lecture 250 Corrective Measures

    Lecture 251 Plant Disease Project

    Section 46: [DEPRECATED] Object Detection with YOLO

    Lecture 252 Object Detection

    Lecture 253 Pascal VOC Dataset

    Lecture 254 Modeling - YOLO v1

    Lecture 255 Error Sanctioning

    Lecture 256 Training and Optimization

    Lecture 257 Testing

    Lecture 258 Performance Measurement - Mean Average Precision (mAP)

    Lecture 259 Data Augmentation

    Lecture 260 YOLO v3

    Lecture 261 Instance Segmentation Project

    Section 47: [DEPRECATED] Semantic Segmentation with UNET

    Lecture 262 Image Segmentation - Oxford IIIT Pet Dataset

    Lecture 263 UNET model

    Lecture 264 Training and Optimization

    Lecture 265 Data Augmentation and Dropout

    Lecture 266 Class weighting and reduced network

    Lecture 267 Semantic Segmentation Project

    Section 48: [DEPRECATED] People Counting

    Lecture 268 People Counting - Shangai Tech Dataset

    Lecture 269 Dataset Preparation

    Lecture 270 CSRNET

    Lecture 271 Training and Optimization

    Lecture 272 Data Augmentation

    Lecture 273 Object Counting Project

    Section 49: [DEPRECATED] Neural Machine Translation with TensorFlow 2

    Lecture 274 Fre-Eng Dataset and Task

    Lecture 275 Sequence to Sequence Models

    Lecture 276 Training Sequence to Sequence Models

    Lecture 277 Performance Measurement - BLEU Score

    Lecture 278 Testing Sequence to Sequence Models

    Lecture 279 Attention Mechanism - Bahdanau Attention

    Lecture 280 Transformers Theory

    Lecture 281 Building Transformers with TensorFlow 2

    Lecture 282 Text Normalization project

    Section 50: [DEPRECATED] Question Answering with TensorFlow 2

    Lecture 283 Understanding Question Answering

    Lecture 284 SQUAD dataset

    Lecture 285 SQUAD dataset preparation

    Lecture 286 Context - Answer Network

    Lecture 287 Training and Optimization

    Lecture 288 Data Augmentation

    Lecture 289 LSH Attention

    Lecture 290 BERT Model

    Lecture 291 BERT Practice

    Lecture 292 GPT Based Chatbot

    Section 51: [DEPRECATED] Automatic Speech Recognition

    Lecture 293 What is Automatic Speech Recognition

    Lecture 294 LJ- Speech Dataset

    Lecture 295 Fourier Transform

    Lecture 296 Short Time Fourier Transform

    Lecture 297 Conv - CTC Model

    Lecture 298 Speech Transformer

    Lecture 299 Audio Classification project

    Section 52: [DEPRECATED] Image Captioning

    Lecture 300 Flickr 30k Dataset

    Lecture 301 CNN- Transformer Model

    Lecture 302 Training and Optimization

    Lecture 303 Vision Transformers

    Lecture 304 OCR Project

    Section 53: [DEPRECATED] Image Generative Modeling

    Lecture 305 Introduction to Generative Modeling

    Lecture 306 Image Generation

    Lecture 307 GAN Loss

    Lecture 308 GAN training and Optimization

    Lecture 309 Wasserstein GAN

    Lecture 310 Image to Image Translation Project

    Section 54: [DEPRECATED] Shipping a Model with Google Cloud Function

    Lecture 311 Introduction

    Lecture 312 Model Preparation

    Lecture 313 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