Deep Learning (from basics)
- Description
- Curriculum
- FAQ
- Reviews
Following topics are covered as part of the course
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Explore building blocks of neural networks
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Data representation, Tensor, Back propagation
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Keras
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Dataset, Applying Keras to cases studies, over fitting / under fitting
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Artificial Neural Networks (ANN)
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Activation functions
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Loss functions
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Gradient Descent
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Optimizer
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Image Processing
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Convnets (CNN), hands-on with CNN
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Text and Sequences
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Text data, Language Processing
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Recurrent Neural Network (RNN)
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LSTM
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Bidirectional RNN
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Gradients and Back Propagation – Mathematics
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1IntroductionVideo lesson
The video gently introduces AI - ML- DL and then takes you to what's "deep" in deep learning, what's "learning" in Deep Learning and ends with what are next activities. In the end, the video gives the course overview which will get covered in next videos.
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2TensorsVideo lesson
The video describes what is a Tensor and what are various sensor types.
This video shows hands-on Implementation about tensors.
Lab: Perform the hands-on implementation that is demonstrated in the video (by doing coding).
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3Tensor OperationsVideo lesson
The video describes various tensor operations. It also shows hands-on Implementation of these tensor operations
Lab: Perform the implementation shown in the video (by doing coding)
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4Tensor Flow and Keras - OverviewVideo lesson
The video first describes in brief what is Tensor Flow, how it is related to Keras.
The video introduces important aspects of Keras.
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5ANN- LayersVideo lesson
The video introduces what is Artificial Neural Network (ANN). Meaning of Layers in ANN. It also gives overview of Back Propagation approach in ANN.
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6ANN- Optimizer and Activation FunctionsVideo lesson
This video covers optimizer - brief information about Adam optimizer.
The video also covers various Activation Functions. Working of RELU is also demonstrated as part of Activation Function.
Lab: Perform the demonstration covered in this video (by doing coding).
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7ANN- Loss FunctionsVideo lesson
This video talks about various Loss functions used in Deep Learning. It also suggests applicability of loss function in various scenarios
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8Keras- Getting StartedVideo lesson
This video talks about installation required for using Keras.
It describes Keras workflow. It also describes an hands-on experiment using Keras.
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9Image Classification- Hands-onVideo lesson
This video gives hands-on implementation of Keras for image classification.
Lab: Perform the hands-on implementation (by doing coding)
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10Convolution Neural Network- Image Processing / Computer VisionVideo lesson
This video talks about details of Convolutional Neural Network.
- what is convolution,
- Pooling concept,
- layer requirements,
- CNN structure
It also talks about applying CNN to image data sets and doing it hands-on
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11CNN- Hands-on (part1)Video lesson
This video shows hands-on Implementation of CNN using large Image data sets.
Lab: Perform the hands-on implementation (by doing coding)
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12CNN- Hands-on (part2)Video lesson
This video shows hands-on Implementation of CNN using large Image data sets.
Lab: Perform the hands-on implementation (by doing coding)
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13Handling Text SequencesVideo lesson
This video talks about Text Sequences and techniques for handling text sequences.
It talks about word embeddings and its use for movie reviews classification.
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14Hands-on with Text Sequences (/ Word Embeddings)Video lesson
This video gives hands-on implementation of word embeddings technique for handling text sequences.
It makes of pretrained word embedding model and classifies movie reviews comments positive or negative.
Lab: Perform the hands-on implementation (by doing coding)
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15Recurrent Neural Network (RNN)Video lesson
This video talks about what is Recurrent Neural Network, the concepts behind RNN and support in Keras for RNN.
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16Hands-on with RNNVideo lesson
This video gives implementation of RNN.
It also gives how to use RNN support of Keras with an example.
Lab: Perform the hands-on implementation (by doing coding)
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17LSTM, Bidirectional RNNsVideo lesson
This video talks about LSTM , concept behind LSTM and then graduate towards Bidirectional RNN. The concept behind Bidirectional RNNs is also explained.
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18Hands-on with LSTMVideo lesson
This video provides hands-on implementation of using LSTM support in keras for text analysis.
Lab: Perform the hands-on implementation (by doing coding)
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19Hands-on with Bidirectional RNNVideo lesson
This video gives Bidirectional RNN implementation with Keras.
It also tells how reversed implementation can be done using LSTM. It gives implementation for Bidirectional RNNs (LSTM, GRU).
Lab: Perform the hands-on implementation (by doing coding)
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