Byte-Sized Deep Learning Series: Neural Networks – Overfitting, Underfitting

Duration: 90 mins Code: BDT498
In deep learning, a model can either underfit (not learn enough from the data) or overfit (memorize the data, losing...
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Byte-Sized Deep Learning Series: Understanding Transfer Learning

Duration: 90 mins Code: BDT497
Deep learning models are data-hungry; but what if you could leverage a pre-trained network to solve your problem with much...
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Byte-Sized Deep Learning Series: Training Optimization of Neural Networks

Duration: 90 mins Code: BDT496
In this 90-minute session, you’ll learn the critical hyperparameters that control how your model learns, including optimizers, loss functions, evaluation...
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Byte-Sized Deep Learning Series: Handling Text Data with Keras

Duration: 90 mins Code: BDT495
In this 90-minute hands-on session, you’ll learn essential text preprocessing steps like tokenization, padding, and vocabulary building, and see how...
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Byte-Sized Deep Learning Series: Image Classification with CNN

Duration: 90 mins Code: BDT494
This session will introduce you to Convolutional Neural Networks (CNNs): the deep learning architecture that powers technologies like facial recognition,...
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Byte-Sized Deep Learning Series: Demystifying Neural Networks

Duration: 90 mins Code: BDT493
Step into the world of deep learning by building your first Artificial Neural Network! In this 90-minute, beginner-friendly session,
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Byte-Sized Deep Learning Series: Introduction to TensorFlow

Duration: 90 mins Code: BDT492
In this 90-minute hands-on session, you’ll dive deep into creating, manipulating, and performing operations with tensors: the essential data structures...
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Byte-Sized ML Series: Feature Importance with SHAP Values

Duration: 90 mins Code: BDT491
This 90-minute session introduces participants to the critical topic of model interpretability, focusing on feature importance using SHAP (Shapley Additive...
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Byte-Sized ML Series: Tackling Imbalanced Classes with SMOTE

Duration: 90 mins Code: BDT490
Class imbalance is one of the most common and tricky problems in real-world classification tasks.
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