Introduction to Deep Learning
( 24 Modules )

Module #1
Introduction to Deep Learning
Overview of deep learning, its applications, and importance

Module #2
Mathematical Prerequisites
Review of linear algebra, calculus, and probability theory

Module #3
Introduction to Python and TensorFlow
Setting up Python and TensorFlow, basic syntax, and data types

Module #4
Introduction to Neural Networks
Basic concepts of neural networks, perceptron, and multilayer perceptron

Module #5
Types of Machine Learning
Supervised, unsupervised, and reinforcement learning, and their differences

Module #6
Convolutional Neural Networks (CNNs)
Overview of CNNs, architecture, and applications

Module #7
Building a Simple CNN Model
Hands-on experience building a simple CNN model using TensorFlow

Module #8
Recurrent Neural Networks (RNNs)
Overview of RNNs, architecture, and applications

Module #9
Long Short-Term Memory (LSTM) Networks
Overview of LSTM networks, architecture, and applications

Module #10
Building a Simple RNN Model
Hands-on experience building a simple RNN model using TensorFlow

Module #11
Autoencoders
Overview of autoencoders, architecture, and applications

Module #12
Building an Autoencoder Model
Hands-on experience building an autoencoder model using TensorFlow

Module #13
Batch Normalization and Regularization
Batch normalization, regularization techniques, and their importance

Module #14
Optimizer Algorithms
Overview of optimizer algorithms, such as SGD, Adam, and RMSProp

Module #15
Evaluation Metrics and Loss Functions
Overview of evaluation metrics and loss functions, including accuracy, precision, recall, and F1 score

Module #16
Transfer Learning and Fine-Tuning
Overview of transfer learning and fine-tuning, and their applications

Module #17
Deep Learning for Computer Vision
Overview of deep learning applications in computer vision, including object detection and segmentation

Module #18
Deep Learning for Natural Language Processing
Overview of deep learning applications in NLP, including language modeling and text classification

Module #19
Generative Adversarial Networks (GANs)
Overview of GANs, architecture, and applications

Module #20
Building a Simple GAN Model
Hands-on experience building a simple GAN model using TensorFlow

Module #21
Deep Learning for Time Series Analysis
Overview of deep learning applications in time series analysis, including forecasting and anomaly detection

Module #22
Deep Learning for Speech Recognition
Overview of deep learning applications in speech recognition, including acoustic modeling and language modeling

Module #23
Deep Learning Best Practices
Best practices for deep learning, including model selection, hyperparameter tuning, and model evaluation

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Introduction to Deep Learning career

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Introduction to Deep Learning ( 24 Modules )

Introduction to Deep Learning
( 24 Modules )