Deep Learning Masterclass with TensorFlow 2 Over 20 Projects

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

Ratings 4.53 / 5.00

Deep Learning Masterclass with TensorFlow 2 Over 20 Projects

What You Will 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

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 Transformers
Evaluation of Classification Models (Precision, Recall, Accuracy, F1-score, Confusion Matrix, ROC Curve)
Mitigating overfitting with Data augmentation
Advanced Tensorflow concepts like Custom Losses and Metrics, Eager and Graph Modes and Custom Training Loops, Tensorboard
Machine 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 Detection
Transfer learning with modern Convnets (Vggnet, Resnet, Mobilenet, Efficientnet) and Vision Transformers (VITs)
Object Detection with YOLO (You Only Look Once)
Image Segmentation with UNet
People Counting with Csrnet
Model Deployment (Distillation, Onnx format, Quantization, Fastapi, Heroku Cloud)
Digit generation with Variational Autoencoders
Face generation with Generative Adversarial Neural Networks
Text 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 transformers
Named Entity Relation with Roberta 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
Speech recognition with RNNs

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.

Enjoy!!!

Who Should Attend!

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

TAKE THIS COURSE