Deep Learning Masterclass with TensorFlow 2 Over 15 Projects

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

4.62 (13 reviews)
Udemy
platform
English
language
Data Science
category
120
students
43 hours
content
Jul 2022
last update
$84.99
regular price

What you will 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.

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 Programming

  • Choosing Machine Model based on task

  • Error sanctioning

  • Linear Regression

  • Logistic Regression

  • Multi-class Regression

  • Neural Networks

  • Training and optimization

  • Performance Measurement

  • Validation and Testing

  • Building Machine Learning models from scratch in python.

  • Overfitting and Underfitting

  • Shuffling

  • Ensembling

  • Weight initialization

  • Data imbalance

  • Learning rate decay

  • Normalization

  • Hyperparameter tuning

  • TensorFlow Installation

  • Training neural networks with TensorFlow 2

  • Imagenet training with TensorFlow

  • Convolutional Neural Networks

  • VGGNets

  • ResNets

  • InceptionNets

  • MobileNets

  • EfficientNets

  • Transfer Learning and FineTuning

  • Data Augmentation

  • Callbacks

  • Monitoring with Tensorboard

  • Breast cancer detection

  • Object detection with YOLO

  • Image segmentation with UNETs

  • People counting

  • Generative modeling with GANs

  • Image generation

  • IMDB Dataset

  • Sentiment Analysis

  • Recurrent Neural Networks.

  • LSTM

  • GRU

  • 1D Convolution

  • Bi directional RNN

  • Word2Vec

  • Machine Translation

  • Attention Model

  • Transformer Network

  • Vision Transformers

  • LSH Attention

  • Image Captioning

  • Question Answering

  • BERT Model

  • HuggingFace

  • Deploying A Deep Learning Model with Google Cloud Functions

Who this course is for:

  • Beginner Python Developers curious about Applying Deep Learning for Computer vision and NLP

  • Computer 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.

Screenshots

Deep Learning Masterclass with TensorFlow 2 Over 15 Projects - Screenshot_01Deep Learning Masterclass with TensorFlow 2 Over 15 Projects - Screenshot_02Deep Learning Masterclass with TensorFlow 2 Over 15 Projects - Screenshot_03Deep Learning Masterclass with TensorFlow 2 Over 15 Projects - Screenshot_04

Content

Introduction

Welcome
General Introduction
Applications of Deep Learning
About this Course

Essential Python Programming

Python Installation
Variables and Basic Operators
Conditional Statements
Loops
Methods
Objects and Classes
Operator Overloading
Method Types
Inheritance
Encapsulation
Polymorphism
Decorators
Generators
Numpy Package
Matplotlib Introduction

Introduction to Machine Learning

Task - Machine Learning Development Life Cycle
Data - Machine Learning Development Life Cycle
Model - Machine Learning Development Life Cycle
Error Sanctioning - Machine Learning Development Life Cycle
Linear Regression
Logistic Regression
Linear Regression Practice
Logistic Regression Practice
Optimization
Performance Measurement
Validation and Testing
Softmax Regression - Data
Softmax Regression - Modeling
Softmax Regression - Errror Sanctioning
Softmax Regression - Training and Optimization
Softmax Regression - Performance Measurement
Neural Networks - Modeling
Neural Networks - Error Sanctioning
Neural Networks - Training and Optimization
Neural Networks - Training and Optimization Practicals
Neural Networks - Performance Measurement
Neural Networks - Validation and testing
Solving Overfitting and Underfitting
Shuffling
Ensembling
Weight Initialization
Data Imbalance
Learning rate decay
Normalization
Hyperparameter tuning
In Class Exercise

Introduction to TensorFlow 2

TensorFlow Installation
Introduction to TensorFlow
TensorFlow Basics
Training a Neural Network with TensorFlow

Introduction to Deep Computer Vision with TensorFlow 2

Tiny Imagenet Dataset
TinyImagenet Preparation
Introduction to Convolutional Neural Networks
Error Sanctioning
Training, Validation and Performance Measurement
Reducing overfitting
VGGNet
InceptionNet
ResNet
MobileNet
EfficientNet
Transfer Learning and FineTuning
Data Augmentation
Callbacks
Monitoring with TensorBoard
ConvNet Project 1
ConvNet Project 2

Introduction to Deep NLP with TensorFlow 2

Sentiment Analysis Dataset
Imdb Dataset Code
Recurrent Neural Networks
Training and Optimization, Evaluation
Embeddings
LSTM
GRU
1D Convolutions
Bidirectional RNNs
Word2Vec
Word2Vec Practice
RNN Project

Breast Cancer Detection

Breast Cancer Dataset
ResNet Model
Training and Performance Measurement
Corrective Measures
Plant Disease Project

Object Detection with YOLO

Object Detection
Pascal VOC Dataset
Modeling - YOLO v1
Error Sanctioning
Training and Optimization
Testing
Performance Measurement - Mean Average Precision (mAP)
Data Augmentation
YOLO v3
Instance Segmentation Project

Semantic Segmentation with UNET

Image Segmentation - Oxford IIIT Pet Dataset
UNET model
Training and Optimization
Data Augmentation and Dropout
Class weighting and reduced network
Semantic Segmentation Project

People Counting

People Counting - Shangai Tech Dataset
Dataset Preparation
CSRNET
Training and Optimization
Data Augmentation
Object Counting Project

Neural Machine Translation with TensorFlow 2

Fre-Eng Dataset and Task
Sequence to Sequence Models
Training Sequence to Sequence Models
Performance Measurement - BLEU Score
Testing Sequence to Sequence Models
Attention Mechanism - Bahdanau Attention
Transformers Theory
Building Transformers with TensorFlow 2
Text Normalization project

Question Answering with TensorFlow 2

Understanding Question Answering
SQUAD dataset
SQUAD dataset preparation
Context - Answer Network
Training and Optimization
Data Augmentation
LSH Attention
BERT Model
BERT Practice
GPT Based Chatbot

Automatic Speech Recognition

What is Automatic Speech Recognition
LJ- Speech Dataset
Fourier Transform
Short Time Fourier Transform
Conv - CTC Model
Speech Transformer
Audio Classification project

Image Captioning

Flickr 30k Dataset
CNN- Transformer Model
Training and Optimization
Vision Transformers
OCR Project

Image Generative Modeling

Introduction to Generative Modeling
Image Generation
GAN Loss
GAN training and Optimization
Wasserstein GAN
Image to Image Translation Project

Shipping a Model with Google Cloud Function

Introduction
Model Preparation
Deployment

Reviews

Chiron
July 1, 2022
Still taking the course but it is quite promising. All the basis are covered and the upcoming (really practical) problems are interesting!
Benjamin
June 23, 2022
This course is just what I needed for months now from building my own neural networks to using Tensorflow to build really cool stuffs. Thanks again.
Anastasia
June 23, 2022
Great content. I really was interested in only the part on computer vision but have fallen in love with NLP.
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udemy ID
6/15/2022
course created date
6/17/2022
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