Deep Learning
- Offered byKnowledgeHut
Deep Learning at KnowledgeHut Overview
Duration | 40 hours |
Start from | Start Now |
Total fee | ₹59,990 |
Mode of learning | Online |
Official Website | Go to Website  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
- Admission Process
Deep Learning at KnowledgeHut Highlights
- Earn a certificate after completion of course
- Fee can be paid in installments
- Build 5 Real-World Projects for Hands-On Experience
Deep Learning at KnowledgeHut Course details
Deep Learning Enthusiasts
Big Data Analysts
Data Engineers
Software Engineers
Learn about the basics on which Deep Learning has been constructed
Understand the biological inspiration behind Neural Networks
Understand industry best-practices for building deep learning application
Learn to apply your knowledge of CNNs in computer vision
Gain knowledge about variants of RNN such as Long Short Term Memory
Learn to use word vector representations and embed layers to train recurrent neural networks
Learn to apply technologies with top notch performances in a variety of industries
KnowledgeHut brings you a comprehensive course that will help you understand Deep learning and use it to generate business value
The workshop will help you learn the foundations of Deep Learning and understand how to build neural networks
You will also learn about Adam, Dropout, BatchNorm, Convolutional networks, RNNs, LSTM, and more. You will work on real-life case studies to get hands-on experience
You will master not only the theory but also see how it is applied in the industry by learning to build models using Keras and TensorFlow
Class Schedule
09:00 AM - 01:00 PM(Weekend)
Deep Learning at KnowledgeHut Curriculum
1. Foundations of Deep Learning
Loss function
Cross entropy
K-nearest neighbour algorithm
Minimizing the error - Regression problem
2. Neural Networks Basics
What is Neural Network
The Biological Inspiration
Multilayer Perceptrons
Gradient Descent
Vectorization
Shallow Neural Networks
Activation Functions
Back Propagation Algorithm
Deep L-layer neural network
Forward Propagation in a Deep Network
Case Study: Neural Networks
3. Introduction to Deep Learning
Hyperparameters tuning
Batch Normalization
Optimization algorithms
Deep Learning frameworks
Weight initialization
Deep Learning architecture
Introducing Keras
Artificial Neural Networks (ANN)
Case Study: Artificial Neural Networks (ANN)
4. Computer Vision
Convolutional Neural Networks (CNN)
Building blocks of CNN
Image Processing using CNN
Pre processing and semantic segmentation
Object localization and detection
Introducing Tensorflow
Case Study: Convolutional Neural Networks (CNN) using TensorFlow
5. Object Detection
Object localization
Object detection
Feature Extraction
6. TensorFlow
Introducing Tensorflow
Case Study: Convolutional Neural Networks (CNN) using TensorFlow
7. Sequence Models
Recurrent Neural Networks (RNN)
Backpropagation through time
Different types of RNNs
Language model and sequence generation
Gated Recurrent Unit (GRU)
Long Short Term Memory (LSTM)
Bidirectional RNN
Deep RNNs
Case Study: Recurrent Neural Networks (RNN)
8. Natural Language Processing (NLP)
Syntax and Parsing Techniques
Statistical NLP and text similarities
Text summarization techniques
Real-Life Case Study
Deep Learning at KnowledgeHut Admission Process
Important Dates
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