Deep Learning Series

Key points of discussion:

• What is Natural Language Pricessing
• Use cases in the industry of NLP
• How Deep Learning is used in NLP
• Concepts of stemming, lemitization, word2vec, GloVe
• Actual use case building using Python for NLP

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Key points of discussion:

• What are Recurrent Neural Networks
• Use cases in the industry of RNN
• The training process in RNN
• LSTM and the process of training
• Actual use case building using Python for RNN and LSTM

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Key points of discussion:

• What is Transfer Learning
• Use cases in the industry using Transfer Learning
• Various architectures like LeNet, Alex Net, VGG 16, Inception V3 etc.
• Their respective use cases and comparisons

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Key points of discussion:

• We will be covering the following topics:
• What is Object detection and its advantages
• Use cases in the industry of Object detection
• How CNN is used in Object Detection
• Concepts of IOU, non max suppression, region proposals etc.
• Actual use case building using Python for Object Detection

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Key points of discussion:

• What is Image Classification and its advantages
• Use cases in the industry of Image Classification
• How CNN is used in Image Classification
• Actual use case building for Image Classification
• Next steps in Image Classification

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Key points of discussion:

• What is convolutional process
• What are the usages of a CNN process
• How the CNN networks are designed
• What are Pooling layers, max pooling and average pooling
• What is Padding process
• What are various Image Augmentation techniques

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Description:

In the second session, the audience have got the basics built of a neural network. The third session in series will dive deeper into neural networks. The hyper-parameters used, epochs, batches, learning rates, optimization techniques and all the options available will be discussed. How we can design a network from scratch and how it will be trained and executed will be discussed during the session.The audience will be continuing their learning in Deep Learning in this third session before they go to Convolutional Neural Network. Python will be the language used. Keras and TensorFlow are the libraries which are used.We will also be developing few use cases using Python and Keras. End-to-end model development with all the steps involved will be discussed.

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Description:

This second session in the Deep Learning series will examine the building blocks of neural networks. From the first session the audience has understood the usage of Deep Learning. From session to session various components of a neural network will be discussed.What is a neuron, what are the layers in a network, the activation functions, back-propagation algorithm, how the learning takes place, the hyper-parameters used will be discussed.The audience will be able to grasp the concepts of Neural Networks and how all the components come together and work together. It will allow the audience to build their concepts from the basics. The session will be followed by a session on developing the code using Deep Learning.

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Description:

Deep Learning (DL) and Artificial Intelligence (AI) are currently the hot topics in the industry. Most of the organisations are investing in them. And many more want to invest. Currently, there is an acute shortage of practitioners who understand these concepts.

This is the first session for the Deep Learning series. In this session, we will introduce Deep Learning to the audience. What is DL, why is it important and why it is different from traditional Machine Learning ?

We will also discuss few of the pragmatic use cases implemented in the business. We will also understand the libraries and packages available in the market, what skill sets are required to learn and implement the use cases.

The participants will be able to understand meaning of DL and how to start their journey in DL. The participants can plan to understand the deeper concepts and improve their learning and understanding of DL.

The session will also answer the questions on assumptions and notions about DL. It is an attempt to make the participants make comfortable on DL and AI as tools to use.

Resources: https://github.com/vverdhan/DeepLearningStudySessions

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