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Data Science Model Deployments and Cloud Computing on GCP - Course Introduction and Section Walkthrough
This video provides an introduction to the course and talks about section learnings in brief. This clip is from the chapter "Course Introduction and Prerequisites" of the series "Data Science Model Deployments and Cloud Computing on...
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Learn Java from Scratch - A Beginner's Guide - Step 02 – Getting Started with JShell
In this video, we will work with JShell.
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Fundamentals of Neural Networks - Lab 2 - Sequence to Sequence Stock Candlestick Forecast
This video demonstrates sequence-to-sequence stock candlestick forecast. This clip is from the chapter "Recurrent Neural Networks" of the series "Fundamentals in Neural Networks".This section explains NLP, we will start with recurrent...
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Fundamentals of Neural Networks - Lab 1 - RNN in Text Classification
This video demonstrates how to design a recurrent neural network or RNN. This clip is from the chapter "Recurrent Neural Networks" of the series "Fundamentals in Neural Networks".This section explains NLP, we will start with recurrent...
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Fundamentals of Neural Networks - Lab 4 - Transfer Learning
This video demonstrates transfer learning. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains convolutional neural networks where you will start with image...
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Fundamentals of Neural Networks - Lab 3 - Deep CNN
This video demonstrates a deeper CNN, where you will build a much bigger number of trainable parameters. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains...
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Fundamentals of Neural Networks - Lab 2 - Introduction to CNN
This video demonstrates the architecture and how to carry out the code using TensorFlow in collab and building a convolutional neural network. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in...
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Fundamentals of Neural Networks - Lab 4 - Functional API
This video demonstrates functional API versus sequential API. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This section explains artificial neural networks where you will...
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Fundamentals of Neural Networks - Lab 3 - Introduction to Neural Network
This video demonstrates how to use Keras TensorFlow as API to essentially design and craft the neural network architecture. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This...
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Fundamentals of Neural Networks - Lab 2 - Introduction to TensorFlow — Remove the Throat-Clearing Sound in the Start of the Video
This video demonstrates some basic operations in TensorFlow such as objects and we will apply some mathematical operations to the Tensor objects. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in...
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Fundamentals of Neural Networks - Lab 1 - Introduction to Python
This video demonstrates some of the basic commands in Python specifically the print statement, data structures, variables, and how to define a function. This clip is from the chapter "Artificial Neural Networks" of the series...
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Fundamentals of Neural Networks - Bi-Directional RNN
Bidirectional recurrent neural networks (BRNN) connect two hidden layers of opposite directions to the same output. BRNNs are especially useful when the context of the input is needed. For example, in handwriting recognition, the...
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Fundamentals of Neural Networks - Long Short-Term Memory (LSTM)
Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. LSTM networks are well-suited to classifying, processing, and making predictions based on time series data...
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Fundamentals of Neural Networks - Gated Recurrent Unit (GRU)
Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks. GRUs have been shown to exhibit better performance on certain smaller and less frequent datasets. This clip is from the chapter "Recurrent Neural Networks"...
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Fundamentals of Neural Networks - Backward Propagation Through Time
Backpropagation through time (BPTT) is a gradient-based technique for training certain types of recurrent neural networks. It can be used to train Elman networks. The algorithm was independently derived by numerous researchers. This clip...
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Fundamentals of Neural Networks - Forward Propagation in RNN
The forward propagation in an RNN makes a few assumptions: 1) We assume the hyperbolic tangent activation function for the hidden layer. 2) We assume that the output is discrete as if the RNN is used to predict words or characters. This...
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Fundamentals of Neural Networks - Language Processing
NLP is a tool for structuring data in a way that AI systems can process that deals with language. NLP uses AI to 'read' through a document and extract key information. This clip is from the chapter "Recurrent Neural Networks" of the...
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Fundamentals of Neural Networks - Why Use RNN
A Recurrent neural network is a type of artificial neural network commonly used in speech recognition and natural language processing. Recurrent neural networks recognize data's sequential characteristics and use patterns to predict the...
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Fundamentals of Neural Networks - Welcome to RNN
This video explains recurrent neural networks and why we want to use RNN. This clip is from the chapter "Recurrent Neural Networks" of the series "Fundamentals in Neural Networks".This section explains NLP, we will start with recurrent...
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Fundamentals of Neural Networks - Lab 1 - Introduction to Convolutional 1-Dimensional
This video demonstrates convolutional operations in 1-dimension. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains convolutional neural networks where you...
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Fundamentals of Neural Networks - Residual Network
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. This clip is from the chapter "Convolutional Neural...
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Fundamentals of Neural Networks - VGG16
This video explains VGG16 which is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford in the paper "Very Deep Convolutional Networks for Large-Scale Image Recognition". This clip...
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Fundamentals of Neural Networks - Convolution in 2D and 3D
This video explains Convolution in 2D and 3D. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains convolutional neural networks where you will start with...
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Fundamentals of Neural Networks - Stride
For a convolutional or pooling operation, the stride denotes the number of pixels by which the window moves after each operation. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural...