Deep Learning Masterclass with TensorFlow 2 Over 15 Projects

Deep Learning Masterclass with TensorFlow 2 Over 15 Projects | Udemy
English | Size: 18.15 GB
Genre: eLearning

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.

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.

Who this course is for:
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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