Crash Course
Training Neural Networks
Today we’re going to talk about how neurons in a neural network learn by getting their math adjusted, called backpropagation, and how we can optimize networks by finding the best combinations of weights to minimize error. Then we’ll send...
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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 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 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 - 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 - Gradient Descent
This video explains the optimization problem using the gradient descent algorithm. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This section explains artificial neural...
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Fundamentals of Neural Networks - Cross-Entropy Loss Function
This video explains the cross-entropy function, which is designed under the assumption that the variable you are trying to predict is binary. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in...
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Fundamentals of Neural Networks - Backward Propagation
This video explains backward propagation, which is defined by the optimization problem called the gradient descent algorithm. 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 - Linear Regression
This video explains statistical machine learning, where you will start with the linear regression model. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This section explains...
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Deep Learning - Crash Course 2023 - Common Network Configuration
In this video, we will look at the common network configuration that we will be using whenever performing the common deep learning task. This clip is from the chapter "Activation Functions in Deep Learning Neural Networks" of the series...
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Deep Learning - Crash Course 2023 - Training the Neural Network
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. This clip is from the chapter "Deep Learning...
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Deep Learning - Crash Course 2023 - Create First Neural Network with TensorFlow
In this video, we will create our first neural network using TensorFlow, and also understand the various steps involved in creating the neural network. This clip is from the chapter "Deep Learning with TensorFlow 2.x" of the series "Deep...
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Deep Learning - Crash Course 2023 - Building a Neural Network with TensorFlow
In this video, we will start building a neural network with TensorFlow by defining the layers, activation functions, and optimization techniques. This clip is from the chapter "Deep Learning with TensorFlow 2.x" of the series "Deep...
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Deep Learning - Crash Course 2023 - Challenges in Creating Deep Neural Networks from Scratch
In this video, we will discuss the challenges involved in creating deep neural networks from scratch. We will explore the common issues faced during the training process and learn how to overcome them. This clip is from the chapter...
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Deep Learning - Crash Course 2023 - Why Do We Require Entropy Loss
In this video, we will talk about certain events and understand why we require entropy loss. This clip is from the chapter "Basic Probability" of the series "Deep Learning - Crash Course 2023".In this section, we will talk about...
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Deep Learning - Crash Course 2023 - Probability Distribution Table
In this video, you will learn about the probability distribution table. This clip is from the chapter "Basic Probability" of the series "Deep Learning - Crash Course 2023".In this section, we will talk about probability.
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Deep Learning - Crash Course 2023 - Gradient Descent
In this video, you will learn about gradient descent. 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.
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Deep Learning - Crash Course 2023 - Loss Function and Parameter Update
In this video, you will learn about the loss function and parameter update. This clip is from the chapter "Perceptron" of the series "Deep Learning - Crash Course 2023".In this section, you will learn about Perceptron.
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Deep Learning - Crash Course 2023 - MP Neuron Introduction
In this video, you will learn about the MP Neuron model, also called McCulloch Pitts model. 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...
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Deep Learning - Crash Course 2023 - Loss Functions
In this video, you will learn about loss functions. 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 learning.
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Deep Learning - Crash Course 2023 - Learning Algorithms and Model Performance
In this video, you will learn two more terms related to deep learning called learning algorithms and model performance. This clip is from the chapter "Getting the Basics Right" of the series "Deep Learning - Crash Course 2023".In this...
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Recommender Systems Complete Course Beginner to Advanced - Project Amazon Product Recommendation System: Compute Loss
This is the next step of training our model, which entails the compute loss model.
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Deep Learning - Artificial Neural Networks with Tensorflow - Gradient Descent
In this video, we will get introduced to gradient descent. This clip is from the chapter "In-Depth: Gradient Descent" of the series "Deep Learning - Artificial Neural Networks with TensorFlow".In this optional section, we will dive...
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Deep Learning - Artificial Neural Networks with Tensorflow - Code Preparation (Artificial Neural Networks)
In this video, we will work with the MNIST dataset and ANN code preparation. This clip is from the chapter "Feedforward Artificial Neural Networks" of the series "Deep Learning - Artificial Neural Networks with TensorFlow".In this...