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Deep Learning Masterclass with TensorFlow 2 Over 20 Projects
Master Deep Learning with TensorFlow 2 with Computer Vision,Natural Language Processing, Sound Recognition & Deployment
This Course Includes
udemy
4.3 (464 reviews )
67h 10m 
english
Online - Self Paced
professional certificate
Udemy
About Deep Learning Masterclass with TensorFlow 2 Over 20 Projects
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. 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!!!
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 Show moreShow less.