- Introduction to Neural Networks and Deep Learning
- Activation Functions
- Feedforward Neural Network
- Backpropagation and Gradient Descent
- Learning Rate Setting and Tuning
- Introduction to Keras
- Fully Connected Layer-Forward and Backward Pass
- Softmax and Cross-Entropy Loss
- Introduction to babysitting the learning Process12
- Data Preprocessing
- Data Augmentation
- Weight Initialization
- Regularization-Batch Normalization
- Regularization-Dropout
- Working with Google collab
- Quiz Module 1
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- Henry Harvin
- Courses
- Certified Artificial Intelligence Practitioner
Online
32 Hours
₹ 13,500 15,000
Quick facts
particular | details | ||
---|---|---|---|
Medium of instructions
English
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Mode of learning
Self study, Virtual Classroom
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Mode of Delivery
Video and Text Based
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Frequency of Classes
Weekends
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Course and certificate fees
Fees information
₹ 13,500 ₹15,000
certificate availability
Yes
certificate providing authority
Henry Harvin
The syllabus
Module 1: Neural Networks
Module 2: Computer Vision
- Working with Images
- Introduction to convolutions2D convolutions for Images
- Convolution-Forward and Backward
- Transposed Convolution and Fully Connected Layer as a Convolution
- Pooling: Max Pooling and other Poolings
- CNN Architectures
- ALexNet, LeNet,ZFNet,VGGNet, GoogleNet, ResNet
- GPU Vs CPU
- Transfer Learning
- Semantic Segmentation using UNet
- Inception and Mobile Net Models
- Object Detection with region proposals, YOLO and SSD
- Bounding Box Regressor
- Siamese Network for Metric Learning
- Quiz Module 2
Module 3: Natural Language Processing
- Introduction to NLP
- Preprocessing in NLP-Tokenization, Lemmatization, Stemming, Normalisation, Stop words, BOW, TF-IDF
- N-gram models and channel models
- Word Embedding
- Dense Encoding
- Word-2-Vec Vectors and Glove Vectors
- POS Tagging
- Named Entity recognition
- Sequential models
- Introduction to RNN, LSTM
- LSTM Forward Pass
- LSTM Backprop through time
- Applications of LSTM
- Advanced LSTM Structures
- Encoder-Decoder Attention
- Quiz Module 3