Deep Learning and Neural Networks Theory and Applications with PyTorch! Including Transformers, BERT and GPT!
InstructorFawaz Sammani
Free
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This image shows a Jupyter Notebook session where Python libraries like pandas and scikit-learn are used to load, clean, and preprocess diabetes dataset features before neural network training.
This diagram illustrates the Seq2Seq Encoder-Decoder architecture for machine translation, demonstrating how an encoder processes English inputs while a decoder generates corresponding German outputs.
This educational slide explains the Sigmoid activation function in neural networks, displaying its S-shaped curve graph, mathematical formula, numerical examples, and key disadvantages like vanishing gradients.
This screenshot shows a Jupyter Notebook cell containing Python code that processes image tensors and displays the resulting classification predictions for ants and bees.
The curriculum progresses from basic optimization to advanced sequence modeling, including Transformers, BERT, and GPT architectures.
Instruction includes detailed coverage of gradient descent, loss functions, and weight initialization to build a theoretical base.
Learner signals suggest difficulty following backpropagation and potential contradictions in lecture slides or mathematical explanations.
While the course includes hands-on applications, signals indicate a need for more structured exercises or formal tests.
The curriculum aligns with the target audience's interest in deep learning, though some learners find the early math difficult.