In this video, we will work on a more conventional prediction called stock return.
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This clip is from the chapter "Recurrent Neural Networks (RNNs), Time Series, and Sequence Data" of the series "Deep Learning - Recurrent...

In this video, you will learn how to preform time series prediction with a complex signal.
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This clip is from the chapter "Recurrent Neural Networks (RNNs), Time Series, and Sequence Data" of the series "Deep Learning -...

In this video, we will do the same forecasting exercise that we did with an autoregressive liner model, but with a simple RNN instead.
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This clip is from the chapter "Recurrent Neural Networks (RNNs), Time Series, and Sequence...

This video demonstrates how to use Keras TensorFlow as API to essentially design and craft the neural network architecture.
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This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural...

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.
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This video explains the optimization problem using the gradient descent algorithm.
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This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This section explains artificial...

This video explains the cross-entropy function, which is designed under the assumption that the variable you are trying to predict is binary.
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This clip is from the chapter "Artificial Neural Networks" of the series...

Dive into the intricacies of how we hear, from the journey of sound waves through the ear to the brain's ability to isolate and enhance conversations in noisy environments. Witness the fascinating tests conducted on diverse age groups...

In this video, you will learn how to solve a regression problem using the Boston housing dataset.
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This clip is from the chapter "Applying Deep Learning" of the series "Deep Learning - Crash Course 2023".In this section, you...

In this video, we will revisit the code and understand some Keras basics. Additionally, you will find a deep learning neural networks assignment in the code bundle.
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This clip is from the chapter "Applying Deep Learning" of...

In this video, we will evaluate the performance of the trained neural network using various performance metrics and techniques.
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This clip is from the chapter "Deep Learning with TensorFlow 2.x" of the series "Deep Learning -...

In this video, you will learn about training the neural network by selecting the right hyperparameters, choosing the right optimization algorithm, and evaluating the performance of the model.
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This clip is from the chapter...

In this video, we will start building a neural network with TensorFlow by defining the layers, activation functions, and optimization techniques.
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This clip is from the chapter "Deep Learning with TensorFlow 2.x" of the series...

In this video, we will confirm the effectiveness of the Universal Approximation Theorem by implementing it on a practical problem.
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This clip is from the chapter "Universal Approximation Theorem" of the series "Deep Learning -...

In this video, we will dive into the implementation of a class for the Sigmoid activation function. You will also get an assignment to further solidify your understanding of Sigmoid.
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This clip is from the chapter "Sigmoid...

In this video, we will build a program in Python to implement Sigmoid Neuron.
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This clip is from the chapter "Sigmoid Neuron" of the series "Deep Learning - Crash Course 2023".In this section, we will begin our journey with...

In this video, we will discuss our program goal that we will be building in the next video.
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This clip is from the chapter "Sigmoid Neuron" of the series "Deep Learning - Crash Course 2023".In this section, we will begin our...

In this video, you will learn about gradient descent.<br<br/>/>

This clip is from the chapter "Sigmoid Neuron" of the series "Deep Learning - Crash Course 2023".In this section, we will begin our journey with Sigmoid Neuron.

In this video, you will learn about the loss function and parameter update.<br<br/>/>

This clip is from the chapter "Perceptron" of the series "Deep Learning - Crash Course 2023".In this section, you will learn about Perceptron.

In this video, you will learn about loss functions.
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This clip is from the chapter "Getting the Basics Right" of the series "Deep Learning - Crash Course 2023".In this section, you will learn some basic terms used in deep...

In this video, we will be introduced to Sigmoid Neuron.<br<br/>/>

This clip is from the chapter "Sigmoid Neuron" of the series "Deep Learning - Crash Course 2023".In this section, we will begin our journey with Sigmoid Neuron.

In this video, we will have a quick recap of Perceptron limitations and learn how Sigmoid Neuron is useful.
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This clip is from the chapter "Sigmoid Neuron" of the series "Deep Learning - Crash Course 2023".In this section, we...

In this video, we will get introduced to the Perceptron model.<br<br/>/>

This clip is from the chapter "Perceptron" of the series "Deep Learning - Crash Course 2023".In this section, you will learn about Perceptron.

In this video, you will learn about loss and finding parameters.
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This clip is from the chapter "MP Neuron Model" of the series "Deep Learning - Crash Course 2023".In this section, you will learn how to build neural networks...