Deep Learning Masterclass with TensorFlow 2 Over 15 Projects

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

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.

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