What you’ll learn
- Introduction to Deep Learning
- high level understanding
- perceptrons
- layers
- activation functions
- loss functions
- optimizers
- Tensor handling
- creation and specific features of tensors
- automatic gradient calculation (autograd)
- Modeling introduction, incl.
- Linear Regression from scratch
- understanding PyTorch model training
- Batches
- Datasets and Dataloaders
- Hyperparameter Tuning
- saving and loading models
- Classification models
- multilabel classification
- multiclass classification
- Convolutional Neural Networks
- CNN theory
- develop an image classification model
- layer dimension calculation
- image transformations
- Audio Classification with torchaudio and spectrograms
- Object Detection
- object detection theory
- develop an object detection model
- YOLO v7, YOLO v8
- Faster RCNN
- Style Transfer
- Style transfer theory
- developing your own style transfer model
- Pretrained Models and Transfer Learning
- Recurrent Neural Networks
- Recurrent Neural Network theory
- developing LSTM models
- Recommender Systems with Matrix Factorization
- Autoencoders
- Transformers
- Understand Transformers, including Vision Transformers (ViT)
- adapt ViT to a custom dataset
- Generative Adversarial Networks
- Semi-Supervised Learning
- Natural Language Processing (NLP)
- Word Embeddings Introduction
- Word Embeddings with Neural Networks
- Developing a Sentiment Analysis Model based on One-Hot Encoding, and GloVe
- Application of Pre-Trained NLP models
- Model Debugging
- Hooks
- Model Deployment
- deployment strategies
- deployment to on-premise and cloud, specifically Google Cloud
- Miscellanious Topics
- ChatGPT
- ResNet
- Extreme Learning Machine (ELM)
Who this course is for:
- Python developers willing to learn one of the most interesting and in-demand techniques
Deal Score0
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