The Complete Neural Networks Bootcamp- Theory, Applications

The Complete Neural Networks Bootcamp: Theory, Applications, available at $84.99, has an average rating of 4.47, with 306 lectures, 2 quizzes, based on 2389 reviews, and has 21102 subscribers.

You will learn about Understand How Neural Networks Work (Theory and Applications) Understand How Convolutional Networks Work (Theory and Applications) Understand How Recurrent Networks and LSTMs work (Theory and Applications) Learn how to use PyTorch in depth Understand how the Backpropagation algorithm works Understand Loss Functions in Neural Networks Understand Weight Initialization and Regularization Techniques Code-up a Neural Network from Scratch using Numpy Apply Transfer Learning to CNNs CNN Visualization Learn the CNN Architectures that are widely used nowadays Understand Residual Networks in Depth Understand YOLO Object Detection in Depth Visualize the Learning Process of Neural Networks Learn how to Save and Load trained models Learn Sequence Modeling with Attention Mechanisms Build a Chatbot with Attention Transformers Build a Chatbot with Transformers BERT Build an Image Captioning Model This course is ideal for individuals who are Anyone who in interested in learning about Neural Networks and Deep Learning It is particularly useful for Anyone who in interested in learning about Neural Networks and Deep Learning.

Enroll now: The Complete Neural Networks Bootcamp: Theory, Applications

Summary

Title: The Complete Neural Networks Bootcamp: Theory, Applications

Price: $84.99

Average Rating: 4.47

Number of Lectures: 306

Number of Quizzes: 2

Number of Published Lectures: 306

Number of Published Quizzes: 2

Number of Curriculum Items: 308

Number of Published Curriculum Objects: 308

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Understand How Neural Networks Work (Theory and Applications)

Understand How Convolutional Networks Work (Theory and Applications)

Understand How Recurrent Networks and LSTMs work (Theory and Applications)

Learn how to use PyTorch in depth

Understand how the Backpropagation algorithm works

Understand Loss Functions in Neural Networks

Understand Weight Initialization and Regularization Techniques

Code-up a Neural Network from Scratch using Numpy

Apply Transfer Learning to CNNs

CNN Visualization

Learn the CNN Architectures that are widely used nowadays

Understand Residual Networks in Depth

Understand YOLO Object Detection in Depth

Visualize the Learning Process of Neural Networks

Learn how to Save and Load trained models

Learn Sequence Modeling with Attention Mechanisms

Build a Chatbot with Attention

Transformers

Build a Chatbot with Transformers

BERT

Build an Image Captioning Model

Who Should Attend

Anyone who in interested in learning about Neural Networks and Deep Learning

Target Audiences

Anyone who in interested in learning about Neural Networks and Deep Learning

This course is a comprehensive guide to Deep Learning and Neural Networks. The theories are explained in depth and in a friendly manner. After that, we’ll have the hands-on session, where we will be learning how to code Neural Networks in PyTorch, a very advanced and powerful deep learning framework!

The course includes the following Sections:

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Section 1 – How Neural Networks and Backpropagation Works

In this section, you will deeply understand the theories of how neural networks  and the backpropagation algorithm works, in a friendly manner. We will walk through an example and do the calculations step-by-step. We will also discuss the activation functions used in Neural Networks, with their advantages and disadvantages!

Section 2 – Loss Functions

In this section, we will introduce the famous loss functions that are used in Deep Learning and Neural Networks. We will walk through when to use them and how they work.

Section 3 – Optimization

In this section, we will discuss the optimization techniques used in Neural Networks, to reach the optimal Point, including Gradient Descent, Stochastic Gradient Descent, Momentum, RMSProp, Adam, AMSGrad, Weight Decay and Decoupling Weight Decay, LR Scheduler and others.

Section 4 – Weight Initialization

In this section,we will introduce you to the concepts of weight initialization in neural networks, and we will discuss some techniques of weights initialization including Xavier initialization and He norm initialization.

Section 5 – Regularization Techniques

In this section, we will introduce you to the regularization techniques in neural networks. We will first introduce overfitting and then introduce how to prevent overfitting by using regularization techniques, inclusing L1, L2 and Dropout. We’ll also talk about normalization as well as batch normalization and Layer Normalization.

Section 6- Introduction to PyTorch

In this section, we will introduce the deep learning framework we’ll be using through this course, which is PyTorch. We will show you how to install it, how it works and why it’s special, and then we will code some PyTorch tensors and show you some operations on tensors, as well as show you Autograd in code!

Section 7 – Practical Neural Networks in PyTorch – Application 1

In this section, you will apply what you’ve learned to build a Feed Forward Neural Network to classify handwritten digits. This is the first application of Feed Forward Networks we will be showing.

Section 8 – Practical Neural Networks in PyTorch – Application 2

In this section, we will build a feed forward Neural Network to classify weather a person has diabetes or not. We will train the network on a large dataset of diabetes!

Section 9 – Visualize the Learning Process

In this section, we will visualize how neural networks are learning, and how good they are at separating non-linear data!

Section 10 – Implementing a Neural Network from Scratch with Python and Numpy

In this section, we will understand and code up a neural network without using any deep learning library (from scratch using only python and numpy). This is necessary to understand how the underlying structure works.

Section 11 – Convolutional Neural Networks

In this section, we will introduce you to Convolutional Networks that are used for images. We will show you first the relationship to Feed Forward Networks, and then we will introduce you the concepts of Convolutional Networks one by one!

Section 12 – Practical Convolutional Networks in PyTorch

In this section, we will apply Convolutional Networks to classify handwritten digits. This is the first application of CNNs we will do.

Section 13- Deeper into CNN: Improving and Plotting

In this section, we will improve the CNN that we built in the previous section, as well show you how to plot the results of training and testing! Moreover, we will show you how to classify your own handwritten images through the network!

Section 14 – CNN Architectures

In this section, we will introduce the CNN architectures that are widely used in all deep learning applications. These architectures are: AlexNet, VGG net, Inception Net, Residual Networks and Densely Connected Networks. We will also discuss some object detection architectures.

Section 15- Residual Networks

In this section, we will dive deep into the details and theory of Residual Networks, and then we’ll build a Residual Network in PyTorch from scratch!

Section 16 – Transfer Learning in PyTorch – Image Classification

In this section, we will apply transfer learning on a Residual Network, to classify ants and bees. We will also show you how to use your own dataset and apply image augmentation. After completing this section, you will be able to classify any images you want!

Section 17- Convolutional Networks Visualization

In this section, we will visualize what the neural networks output, and what they are really learning. We will observe the feature maps of the network of every layer!

Section 18 – YOLO Object Detection (Theory)

In this section, we will learn one of the most famous Object Detection Frameworks: YOLO!! This section covers the theory of YOLO in depth.

Section 19 – Autoencoders and Variational Autoencoders

In this section, we will cover Autoencoders and Denoising Autoencoders. We will then see the problem they face and learn how to mitigate it with Variational Autoencoders.

Section 20 – Recurrent Neural Networks

In this section, we will introduce you to Recurrent Neural Networks and all their concepts. We will then discuss the Backpropagation through  time, the vanishing gradient problem, and finally about Long Short Term Memory (LSTM) that solved the problems RNN suffered from.

Section 21 – Word Embeddings

In this section, we will discuss how words are represented as features. We will then show you some Word Embedding models.  We will also show you how to implement word embedding in PyTorch!

Section 22 – Practical Recurrent Networks in PyTorch

In this section, we will apply Recurrent Neural Networks using LSTMs in PyTorch to generate text similar to the story of Alice in Wonderland! You can just replace the story with any other text you want, and the RNN will be able to generate text similar to it!

Section 23 – Sequence Modelling

In this section, we will learn about Sequence-to-Sequence Modelling. We will see how Seq2Seq models work and where they are applied. We’ll also talk about Attention mechanisms and see how they work.

Section 24 – Practical Sequence Modelling in PyTorch – Build a Chatbot

In this section, we will apply what we learned about sequence modeling and build a Chatbot with Attention Mechanism.

Section 25 – Saving and Loading Models

In this section, we will show you how to save and load models in PyTorch, so you can use these models either for later testing, or for resuming training!

Section 26 – Transformers

In this section, we will cover the Transformer, which is the current state-of-art model for NLP and language modeling tasks. We will go through each component of a transformer.

Section 27 – Build a Chatbot with Transformers

In this section, we will implement all what we learned in the previous section to build a Chatbot using Transformers.

Course Curriculum

Chapter 1: How Neural Networks and Backpropagation Works

Lecture 1: BEFORE STARTINGPLEASE READ THIS

Lecture 2: What Can Deep Learning Do?

Lecture 3: The Rise of Deep Learning

Lecture 4: The Essence of Neural Networks

Lecture 5: The Perceptron

Lecture 6: Gradient Descent

Lecture 7: The Forward Propagation

Lecture 8: Before Proceeding with the Backpropagation

Lecture 9: Backpropagation Part 1

Lecture 10: Backpropagation Part 2

Chapter 2: Loss Functions

Lecture 1: Mean Squared Error (MSE)

Lecture 2: L1 Loss (MAE)

Lecture 3: Huber Loss

Lecture 4: Binary Cross Entropy Loss

Lecture 5: Cross Entropy Loss

Lecture 6: Softmax Function

Lecture 7: Softmax with Temperature: Controlling your distribution

Lecture 8: KL divergence Loss

Lecture 9: Contrastive Loss

Lecture 10: Hinge Loss

Lecture 11: Triplet Ranking Loss

Lecture 12: Practical Loss Functions Note

Chapter 3: Activation Functions

Lecture 1: Why we need activation functions

Lecture 2: Sigmoid Activation

Lecture 3: Tanh Activation

Lecture 4: ReLU and PReLU

Lecture 5: Exponentially Linear Units (ELU)

Lecture 6: Gated Linear Units (GLU)

Lecture 7: Swish Activation

Lecture 8: Mish Activation

Chapter 4: Regularization and Normalization

Lecture 1: Overfitting

Lecture 2: L1 and L2 Regularization

Lecture 3: Dropout

Lecture 4: DropConnect

Lecture 5: Normalization

Lecture 6: Batch Normalization

Lecture 7: Layer Normalization

Lecture 8: Group Normalization

Chapter 5: Optimization

Lecture 1: Batch Gradient Descent

Lecture 2: Stochastic Gradient Descent

Lecture 3: Mini-Batch Gradient Descent

Lecture 4: Exponentially Weighted Average Intuition

Lecture 5: Exponentially Weighted Average Implementation

Lecture 6: Bias Correction in Exponentially Weighted Averages

Lecture 7: Momentum

Lecture 8: RMSProp

Lecture 9: Adam Optimization

Lecture 10: SWATS – Switching from Adam to SGD

Lecture 11: Weight Decay

Lecture 12: Decoupling Weight Decay

Lecture 13: AMSGrad

Chapter 6: Hyperparameter Tuning and Learning Rate Scheduling

Lecture 1: Introduction to Hyperparameter Tuning and Learning Rate Recap

Lecture 2: Step Learning Rate Decay

Lecture 3: Cyclic Learning Rate

Lecture 4: Cosine Annealing with Warm Restarts

Lecture 5: Batch Size vs Learning Rate

Chapter 7: Weight Initialization

Lecture 1: Normal Distribution

Lecture 2: What happens when all weights are initialized to the same value?

Lecture 3: Xavier Initialization

Lecture 4: He Norm Initialization

Lecture 5: Practical Weight Initialization Note

Chapter 8: Introduction to PyTorch

Lecture 1: CODE FOR THIS COURSE

Lecture 2: Computation Graphs and Deep Learning Frameworks

Lecture 3: Installing PyTorch and an Introduction

Lecture 4: How PyTorch Works

Lecture 5: Torch Tensors – Part 1

Lecture 6: Torch Tensors – Part 2

Lecture 7: Numpy Bridge, Tensor Concatenation and Adding Dimensions

Lecture 8: Automatic Differentiation

Lecture 9: Loss Functions in PyTorch

Lecture 10: Weight Initialization in PyTorch

Chapter 9: Data Augmentation

Lecture 1: 1_Introduction to Data Augmentation

Lecture 2: 2_Data Augmentation Techniques Part 1

Lecture 3: 2_Data Augmentation Techniques Part 2

Lecture 4: 2_Data Augmentation Techniques Part 3

Chapter 10: Practical Neural Networks in PyTorch – Application 1: Diabetes

Lecture 1: Download the Dataset

Lecture 2: Part 1: Data Preprocessing

Lecture 3: Part 2: Data Normalization

Lecture 4: Part 3: Creating and Loading the Dataset

Lecture 5: Part 4: Building the Network

Lecture 6: Part 5: Training the Network

Chapter 11: Visualize the Learning Process

Lecture 1: Visualize Learning Part 1

Lecture 2: Visualize Learning Part 2

Lecture 3: Visualize Learning Part 3

Lecture 4: Visualize Learning Part 4

Lecture 5: Visualize Learning Part 5

Lecture 6: Visualize Learning Part 6

Lecture 7: Neural Networks Playground

Chapter 12: Implementing a Neural Network from Scratch with Numpy

Instructors

Fawaz Sammani
Computer Vision Researcher

Rating Distribution

1 stars: 45 votes

2 stars: 57 votes

3 stars: 232 votes

4 stars: 742 votes

5 stars: 1313 votes

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