Deep Learning program

About Course

This Deep Learning course will take you through the fundamental and advanced concepts of neural networks and deep learning models. You’ll explore a variety of techniques and algorithms used in real-world applications like image recognition, NLP, and reinforcement learning. By the end of this course, you’ll have the skills to build, train, and deploy deep learning models for various tasks using modern tools and frameworks.

Course Duration:
12 Weeks

What Will You Learn?

Master deep learning concepts such as neural networks, CNNs, RNNs, and GANs.
Understand how to optimize and fine-tune models for performance and real-world deployment.
Explore cutting-edge technologies like self-supervised learning, meta-learning, and graph neural networks.
Gain hands-on experience by implementing models for tasks like image classification, sentiment analysis, and text generation.

Material Includes

Video lectures and practical demonstrations
Coding exercises and real-world projects
Access to tools like TensorFlow, PyTorch, and cloud platforms for deployment
Code samples and Jupyter notebook templates

Course Curriculum

Module 1: Introduction to Deep Learning

This module introduces core concepts of deep learning, its relationship with AI and machine learning, and the tools and frameworks used in the field, including TensorFlow and PyTorch.

Module 2: Mathematical Foundations for Deep Learning

Module 3: Neural Networks and Backpropagation

Module 4: Convolutional Neural Networks (CNNs)

Module 5: Recurrent Neural Networks (RNNs) and LSTMs

Module 6: Generative Models

Module 7: Natural Language Processing (NLP)

Module 8: Reinforcement Learning (RL)

Module 9: Advanced Deep Learning Topics

Module 10: Deployment and Productionalization

Module 11: Real-World Projects and Case Studies

Module 12: Emerging Trends in Deep Learning

Your Instructors

kodestree

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Deep Learning program

kodestree

Data Science & Analytics

March 3, 2025

About Course

What Will You Learn?

Material Includes

Course Curriculum

Module 1: Introduction to Deep Learning

This module introduces core concepts of deep learning, its relationship with AI and machine learning, and the tools and frameworks used in the field, including TensorFlow and PyTorch.

Module 2: Mathematical Foundations for Deep Learning

Build a solid understanding of the mathematical concepts needed for deep learning, such as linear algebra, calculus, and probability, crucial for implementing algorithms.

Module 3: Neural Networks and Backpropagation

Learn how to construct neural networks, understand activation functions, loss functions, and optimize networks using backpropagation and various optimizers.

Module 4: Convolutional Neural Networks (CNNs)

Dive into the architectures used for image classification and processing, learning about convolution operations, CNN layers, and transfer learning techniques.

Module 5: Recurrent Neural Networks (RNNs) and LSTMs

Explore sequence models for time series prediction, NLP, and speech recognition. Study the vanishing gradient problem and learn about LSTMs, GRUs, and applications like sentiment analysis.

Module 6: Generative Models

Understand generative models like GANs and VAEs, learning how they can be used for tasks like image synthesis and anomaly detection.

Module 7: Natural Language Processing (NLP)

Master NLP techniques using deep learning, including text preprocessing, word embeddings, and transformers like BERT and GPT-3, and apply these models to text classification and translation tasks.

Module 8: Reinforcement Learning (RL)

Learn about RL fundamentals, including the Markov Decision Process, Q-learning, and deep Q-networks (DQN). Understand how RL can be applied to problems like gaming and robotics.

Module 9: Advanced Deep Learning Topics

Explore advanced topics such as self-supervised learning, meta-learning, and graph neural networks. Study cutting-edge techniques like vision transformers and the latest research in the field.

Module 10: Deployment and Productionalization

Learn how to deploy deep learning models at scale, including optimization techniques, cloud platforms, edge deployment, and how to monitor models in production.

Module 11: Real-World Projects and Case Studies

Apply your knowledge through real-world projects, including image classification with CNNs, sentiment analysis with RNNs, text generation with GPT, and deploying models on the cloud.

Module 12: Emerging Trends in Deep Learning

Understand the latest advancements in AI and deep learning, including ethics, bias, quantum machine learning, and cutting-edge applications in healthcare, autonomous vehicles, and robotics.

Your Instructors

kodestree

This course includes:

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Contact US

Follow Us

Deep Learning program

March 3, 2025

About Course

What Will You Learn?

Material Includes

Course Curriculum

Module 1: Introduction to Deep Learning

This module introduces core concepts of deep learning, its relationship with AI and machine learning, and the tools and frameworks used in the field, including TensorFlow and PyTorch.

Module 2: Mathematical Foundations for Deep Learning

Build a solid understanding of the mathematical concepts needed for deep learning, such as linear algebra, calculus, and probability, crucial for implementing algorithms.

Module 3: Neural Networks and Backpropagation

Learn how to construct neural networks, understand activation functions, loss functions, and optimize networks using backpropagation and various optimizers.

Module 4: Convolutional Neural Networks (CNNs)

Dive into the architectures used for image classification and processing, learning about convolution operations, CNN layers, and transfer learning techniques.

Module 5: Recurrent Neural Networks (RNNs) and LSTMs

Explore sequence models for time series prediction, NLP, and speech recognition. Study the vanishing gradient problem and learn about LSTMs, GRUs, and applications like sentiment analysis.

Module 6: Generative Models

Understand generative models like GANs and VAEs, learning how they can be used for tasks like image synthesis and anomaly detection.

Module 7: Natural Language Processing (NLP)

Master NLP techniques using deep learning, including text preprocessing, word embeddings, and transformers like BERT and GPT-3, and apply these models to text classification and translation tasks.

Module 8: Reinforcement Learning (RL)

Learn about RL fundamentals, including the Markov Decision Process, Q-learning, and deep Q-networks (DQN). Understand how RL can be applied to problems like gaming and robotics.

Module 9: Advanced Deep Learning Topics

Explore advanced topics such as self-supervised learning, meta-learning, and graph neural networks. Study cutting-edge techniques like vision transformers and the latest research in the field.

Module 10: Deployment and Productionalization

Learn how to deploy deep learning models at scale, including optimization techniques, cloud platforms, edge deployment, and how to monitor models in production.

Module 11: Real-World Projects and Case Studies

Apply your knowledge through real-world projects, including image classification with CNNs, sentiment analysis with RNNs, text generation with GPT, and deploying models on the cloud.

Module 12: Emerging Trends in Deep Learning

Understand the latest advancements in AI and deep learning, including ethics, bias, quantum machine learning, and cutting-edge applications in healthcare, autonomous vehicles, and robotics.

Your Instructors

kodestree

This course includes:

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