• What is Artificial Intelligence and Machine Learning?
• Understanding Learning Representation of Data
• Probabilistic Modeling
• Kernel Methods
• Decision trees, Random Forests, and Gradient Boosting Machines
• Fundamentals of Deep Learning

• Understanding Neural Networks
• What Makes Deep Learning Different
• Hardware, Data, and Algorithms of Deep Learning
• How Deep Learning Works
• Deep Learning and Its Application
• Future of Deep Learning

• Scalars, Vectors, Matrices
• 3D Tensors and higher-dimensional tensors
• Key Attributes
• Manipulating Tensors in Numpy
• The Notion of Data Batch

• Examples of Data Tensors
• Vector Data
• Timeseries Data or Sequence Data
• Image and Video Data

• Element-wise Operations
• Broadcasting
• Tensor Dot
• Tensor Reshaping
• Geometric Interpretation of Tensor Operations

• Geometric Interpretation of Deep Learning
• Introduction to Derivative
• Derivative of a Tensor Operation: Gradient
• Stochastic Gradient Descent
• The Backpropagation Algorithm

• Key Considerations
• Setting up Jupyter Notebooks
• Setting up of Keras
• Deep Learning in a Cloud
• Identifying the Best GPU for Deep Learning

• What are the Layers for Deep Learning?
• Neural Network Models
• Core Elements to Configuring a Learning Process
• Introduction to Keras, TensorFlow, Theano, and CNTK
• Brief Overview of Keras

• Data preparation for Binary Classification
• Building a Binary Classification Network
• Validating Approach
• Using a Trained Network to Generate Predictions on New Data
• Data Preparation for Multi-Class Classification
• Building Multi-Class Classification Network
• Validating Approach

• Generate Predictions on New Data for Regression Model
• Alternative Ways to Handle Labels and Loss
• Importance of Having Sufficiently Large Intermediate Layers
• Preparing the Data
• Building Network
• Validating Approach using K-Fold Validation

• Introduction to Convnets
• Understanding Convolution Operation and Max Pooling Operation
• Training a Convnet on a Small Dataset
• Understanding the Relevance of Deep Learning for Small-Data Problems
• Downloading the Data and Building the Network

• Data Pre-processing and Data Augmentation
• Visualizing Intermediate Activations
• Visualizing Convnet Filters
• Visualizing Heatmaps of Class Activation

• Encoding of Words or Characters
• How is Word Embedding Being Used?
• From Raw Text to Word Embeddings
• Understanding Recurrent Neural Networks
• What is LSTM and GRU Layers?
• What is a First Recurrent Baseline
• Using Recurrent Dropout to Fight Overfitting

• Stacking Recurrent Layers
• Using Bidirectional RNNs
• Understanding 1D Convolution for Sequence Data
• 1D Pooling for Sequence Data
• Implementing a 1D Convnet
• Combing CNNs and RNNs to Process Long Sequences

Certificate Verification

All certificates issued by CASUGOL are to individuals who have successfully completed CASUGOL Certification Programs / Executive Workshops and have fulfilled all requirements by demonstrating proficiency in applying the knowledge and skills acquired.

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