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. 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 There's so much information out there, much of which is outdated and many times don't take the beginners into consideration:(In this course, we shall take you on an amazing journey in which you'll master different concepts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 TransformersEvaluation of Classification Models (Precision, Recall, Accuracy, F1-score, Confusion Matrix, ROC Curve)Mitigating overfitting with Data augmentationAdvanced Tensorflow concepts like Custom Losses and Metrics, Eager and Graph Modes and Custom Training Loops, TensorboardMachine 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 DetectionTransfer learning with modern Convnets (Vggnet, Resnet, Mobilenet, Efficientnet) and Vision Transformers (VITs)Object Detection with YOLO (You Only Look Once)Image Segmentation with UNetPeople Counting with Csrnet Model Deployment (Distillation, Onnx format, Quantization, Fastapi, Heroku Cloud)Digit generation with Variational AutoencodersFace generation with Generative Adversarial Neural NetworksText 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 transformersNamed Entity Relation with Roberta in Huggingface transformersNeural Machine Translation with T5 in Huggingface transformersExtractive Question Answering with Longformer in Huggingface transformersE-commerce search engine with Sentence transformersLyrics Generator with GPT2 in Huggingface transformersGrammatical Error Correction with T5 in Huggingface transformersElon Musk Bot with BlenderBot in Huggingface transformersSpeech recognition with RNNsIf 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.