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Deep Learning Masterclass With Tensorflow 2 Over 20 Projects - AD-TEAM - 01-01-2025

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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 SadIn 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

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