Machine Learning Course — Deep Learning Masterclass Using TensorFlow 2 Building 20 Projects

Master Deep Learning with TensorFlow 2 with Computer Vision, Natural Language Processing, Speech Recognition & Applications

What you will learn

• Fundamentals of Tensors and Variables with TensorFlow

• Fundamentals 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 such as: Precision, Recall, Accuracy, and F1 Score

• Evaluating Classification Models with Confusion Matrix and ROC Curve

• TensorFlow Callbacks, Learning Rate Scheduling, and Model Checkpointing

• Reducing Overfitting and Underfitting with Dropout, Regularization, Data Augmentation

• Data augmentation with TensorFlow using TensorFlow Image and Keras Layers

• Advanced augmentation strategies such as Cutmix and Mixup

• Data augmentation with Albumentations with TensorFlow 2 and PyTorch

• Custom Loss and Metrics in TensorFlow 2

• Eager and Graph Mode 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 emotion detection

• Modern convolutional neural networks (Alexnet, Vggnet, Resnet, Mobilenet, EfficientNet)

• Transfer learning

• Visualizing intermediate convolutional layers

• Gradient-based methods

• Model ensemble and class imbalance

• Transformers in Vision

• Model deployment

• Converting from TensorFlow to ONNX Model