Deep Learning course

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About Course

The Deep Learning course provides an in-depth understanding of neural networks and the computational foundations for building and training these models. Through this course, you’ll gain knowledge of neural network architectures, optimization techniques, and applications in areas like computer vision, natural language processing, and reinforcement learning. This curriculum is highly practical, involving hands-on projects with frameworks like TensorFlow and PyTorch.

1. Understand the principles of deep learning and neural networks.
2. Gain familiarity with various neural network architectures (CNNs, RNNs, GANs, etc.).
3. Develop practical experience with TensorFlow and PyTorch for model building and deployment.
4. Explore real-world applications in computer vision, NLP, and reinforcement learning.
5. Build and optimize models for better accuracy and performance.

Course Content

Foundations of Deep Learning

Introduction to Deep Learning: What is deep learning, historical background, applications
Mathematics for Deep Learning:
Machine Learning Overview: Supervised, unsupervised, reinforcement learning
Introduction to Neural Networks:

Neural Network Basics

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs) and Sequence Models

Generative Adversarial Networks (GANs)

Autoencoders and Unsupervised Learning

Reinforcement Learning (RL) Basics

Model Deployment and Optimization

Real-World Applications and Capstone Project

Student Ratings & Reviews

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Call: 6290703325

Email: databitstechno@gmail.com

Call: 6290703325

databitstechno@gmail.com

About Course

What Will You Learn?

Course Content

Foundations of Deep Learning

Introduction to Deep Learning: What is deep learning, historical background, applications

Mathematics for Deep Learning:

Machine Learning Overview: Supervised, unsupervised, reinforcement learning

Introduction to Neural Networks:

Neural Network Basics

Deep Neural Networks (DNNs):

Optimization Techniques:

Cost Functions and Loss: Cross-entropy, mean squared error, and custom loss functions

Introduction to TensorFlow and PyTorch:

Convolutional Neural Networks (CNNs)

Foundations of CNNs:

Image Processing and Computer Vision:

Transfer Learning and Fine-Tuning:

CNN Implementation: Hands-on projects for image classification and object detection

Recurrent Neural Networks (RNNs) and Sequence Models

RNN Basics:

Types of RNNs:

Applications in NLP:

Attention Mechanism and Transformers:

Hands-on Projects:

Generative Adversarial Networks (GANs)

GAN Basics:

Types of GANs:

Applications of GANs:

Project: Building a GAN to generate images or perform style transfer

Autoencoders and Unsupervised Learning

Autoencoder Fundamentals:

Types of Autoencoders:

Clustering and Dimensionality Reduction:

Project: Implementing an autoencoder for image compression or anomaly detection

Reinforcement Learning (RL) Basics

Introduction to Reinforcement Learning:

RL Algorithms:

Applications of RL:

Project: Building a reinforcement learning agent for a simple game or task

Model Deployment and Optimization

Model Evaluation and Performance:

Model Deployment:

Model Optimization Techniques:

Real-World Applications and Capstone Project

Real-World Case Studies:

Capstone Project:

Project Presentation and Documentation:

Student Ratings & Reviews

Quick Links

Internship

Trainings

Summer/ Winter Trainings

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