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deep-learning-training

Deep Learning

Deep learning is an artificial intelligence (AI) function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Also known as deep neural learning or deep neural network.

CURRICULUM
  • Module-1 Course Introduction
  • 1.01 Introduction
  • 1.02 Learning Objectives

  • Module-2 AI and Deep Learning Introduction
  • 2.01 What is AI and Deep Learning
  • 2.02 Recap: SL, UL and RL
  • 2.03 Deep learning : successes last decade
  • 2.04 Applications of Deep learning
  • 2.05 Challenges of Deep learning
  • 2.06 Demo & discussion: Sentiment analysis using LSTM
  • 2.07 Fullcycle of a deep learning project
  • 2.08 Key Takeaways
  • 2.09 Knowledge Check

  • Module-3 Artificial Neural Network
  • 3.01 What is Neural Network?
  • 3.02 The Biological Inspiration
  • 3.03 Multilayer Perceptrons
  • 3.04 Gradient Descent
  • 3.05 Vectorization
  • 3.06 Shallow Neural Networks
  • 3.07 Activation Functions
  • 3.08 Back Propagation Algorithm
  • 3.09 Deep L-layer neural network
  • 3.10 Forward Propagation in a Deep Network
  • 3.11 Case Study: Neural Networks

  • Module-4 Computer Vision
  • 4.01 Convolutional Neural Networks (CNN)
  • 4.02 Building blocks of CNN
  • 4.03 Image Processing using CNN
  • 4.04 Pre processing and semantic segmentation
  • 4.05 Object localization and detection
  • 4.06 Introducing Tensorflow
  • 4.07 Case Study: Convolutional Neural Networks (CNN) using TensorFlow

  • Module-5 Object Detection
  • 5.01 Object localization
  • 5.02 Object detection
  • 5.03 Feature Extraction

  • Module-6 TensorFlow
  • 6.01 Introducing Tensorflow
  • 6.02 Case Study: Convolutional Neural Networks (CNN) using TensorFlow

  • Module-7 Sequence Models
  • 7.01 Recurrent Neural Networks (RNN)
  • 7.02 Backpropagation through time
  • 7.03 Different types of RNNs
  • 7.04 Language model and sequence generation
  • 7.05 Gated Recurrent Unit (GRU)
  • 7.06 Long Short Term Memory (LSTM)
  • 7.07 Bidirectional RNN
  • 7.08 Deep RNNs
  • 7.09 Case Study: Recurrent Neural Networks (RNN)

  • Module-8 Natural Language Processing (NLP)
  • 8.01 Syntax and Parsing Techniques
  • 8.02 Statistical NLP and text similarities
  • 8.03 Text summarization techniques
  • 8.04 Real-Life Case Study