Intro to Deep Learning and Generative Models Course
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What you'll learn
This course includes
- 40.3 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 171 lessons • 40.3 hours of video
Intro to Deep Learning and Generative Models Course
171 lessons
• 40.3 hours
Intro to Deep Learning and Generative Models Course
171 lessons
• 40.3 hours
- L1.0 Intro to Deep Learning, Course Introduction 04:27
- L1.1.1 Course Overview Part 1: Motivation and Topics 16:27
- L1.1.2 Course Overview Part 2: Organization (Optional) 17:35
- L1.2 What is Machine Learning? 17:43
- L1.3.1 Broad Categories of ML Part 1: Supervised Learning 10:56
- L1.3.2 Broad Categories of ML Part 2: Unsupervised Learning 07:30
- L1.3.3 Broad Categories of ML Part 3: Reinforcement Learning 03:49
- L1.3.4 Broad Categories of ML Part 4: Special Cases of Supervised Learning 10:46
- L1.4 The Supervised Learning Workflow 17:46
- L1.5 Necessary Machine Learning Notation and Jargon 22:02
- L1.6 About the Practical Aspects and Tools Used in This Course 11:26
- Deep Learning News #1, Jan 27 2021 15:28
- L2.0 A Brief History of Deep Learning -- Lecture Overview 02:57
- L2.1 Artificial Neurons 16:49
- L2.2 Multilayer Networks 15:11
- L2.3 The Origins of Deep Learning 20:12
- L2.4 The Deep Learning Hardware & Software Landscape 07:21
- L2.5 Current Trends in Deep Learning 08:22
- L3.0 Perceptron Lecture Overview 05:02
- L3.1 About Brains and Neurons 12:51
- L3.2 The Perceptron Learning Rule 31:39
- L3.3 Vectorization in Python 14:55
- L3.4 Perceptron in Python using NumPy and PyTorch 28:42
- L3.5 The Geometric Intuition Behind the Perceptron 18:43
- Deep Learning News #2, Feb 6 2021 25:01
- L4.0 Linear Algebra for Deep Learning -- Lecture Overview 02:11
- L4.1 Tensors in Deep Learning 13:03
- L4.2 Tensors in PyTorch 28:34
- L4.3 Vectors, Matrices, and Broadcasting 16:15
- L4.4 Notational Conventions for Neural Networks 11:53
- L4.5 A Fully Connected (Linear) Layer in PyTorch 12:42
- L5.0 Gradient Descent -- Lecture Overview 06:28
- L5.1 Online, Batch, and Minibatch Mode 21:04
- L5.2 Relation Between Perceptron and Linear Regression 05:21
- L5.3 An Iterative Training Algorithm for Linear Regression 11:11
- L5.4 (Optional) Calculus Refresher I: Derivatives 17:37
- L5.5 (Optional) Calculus Refresher II: Gradients 17:34
- L5.6 Understanding Gradient Descent 26:34
- L5.7 Training an Adaptive Linear Neuron (Adaline) 06:43
- L5.8 Adaline Code Example 33:27
- Deep Learning News #3, Feb 13 2021 20:25
- L6.0 Automatic Differentiation in PyTorch -- Lecture Overview 04:09
- L6.1 Learning More About PyTorch 15:47
- L6.2 Understanding Automatic Differentiation via Computation Graphs 22:48
- L6.3 Automatic Differentiation in PyTorch -- Code Example 09:03
- L6.4 Training ADALINE with PyTorch -- Code Example 23:30
- L6.5 A Closer Look at the PyTorch API 25:03
- L7.0 GPU resources & Google Colab 19:17
- Deep Learning News #4, Feb 20 2021 28:09
- L8.0 Logistic Regression -- Lecture Overview 06:28
- L8.1 Logistic Regression as a Single-Layer Neural Network 09:15
- L8.2 Logistic Regression Loss Function 12:57
- L8.3 Logistic Regression Loss Derivative and Training 19:57
- L8.4 Logits and Cross Entropy 06:48
- L8.5 Logistic Regression in PyTorch -- Code Example 19:03
- L8.6 Multinomial Logistic Regression / Softmax Regression 17:32
- L8.7.1 OneHot Encoding and Multi-category Cross Entropy 15:35
- L8.7.2 OneHot Encoding and Multi-category Cross Entropy -- Code Example 15:05
- L8.8 Softmax Regression Derivatives for Gradient Descent 19:40
- L8.9 Softmax Regression -- Code Example Using PyTorch 25:40
- Deep Learning News #5, Feb 27 2021 30:59
- L9.0 Multilayer Perceptrons -- Lecture Overview 03:54
- L9.1 Multilayer Perceptron Architecture 24:24
- L9.2 Nonlinear Activation Functions 22:50
- L9.3.1 Multilayer Perceptron -- Code Example Part 1/3 (Slide Overview) 10:00
- L9.3.2 Multilayer Perceptron in PyTorch -- Code Example Part 2/3 (Jupyter Notebook) 08:31
- L9.3.3 Multilayer Perceptron in PyTorch -- Code Example Part 3/3 (Script Setup) 13:37
- L9.4 Overfitting and Underfitting 31:09
- L9.5.1 Cats & Dogs and Custom Data Loaders 16:48
- L9.5.2 Custom DataLoaders in PyTorch --Code Example 29:29
- Deep Learning News #6, Mar 7 2021 36:13
- L10.0 Regularization Methods for Neural Networks -- Lecture Overview 11:09
- L10.1 Techniques for Reducing Overfitting 12:17
- L10.2 Data Augmentation in PyTorch 14:32
- L10.3 Early Stopping 04:08
- L10.4 L2 Regularization for Neural Nets 15:48
- L10.5.1 The Main Concept Behind Dropout 11:08
- L10.5.2 Dropout Co-Adaptation Interpretation 03:51
- L10.5.3 (Optional) Dropout Ensemble Interpretation 09:11
- L10.5.4 Dropout in PyTorch 12:04
- L11.0 Input Normalization and Weight Initialization -- Lecture Overview 02:53
- L11.1 Input Normalization 08:03
- L11.2 How BatchNorm Works 15:14
- L11.3 BatchNorm in PyTorch -- Code Example 08:45
- L11.4 Why BatchNorm Works 23:38
- L11.5 Weight Initialization -- Why Do We Care? 06:01
- L11.6 Xavier Glorot and Kaiming He Initialization 12:22
- L11.7 Weight Initialization in PyTorch -- Code Example 07:37
- Deep Learning News #7 Mar 13 2021 23:34
- L12.0: Improving Gradient Descent-based Optimization -- Lecture Overview 06:19
- L12.1 Learning Rate Decay 17:07
- L12.2 Learning Rate Schedulers in PyTorch 14:38
- L12.3 SGD with Momentum 09:05
- L12.4 Adam: Combining Adaptive Learning Rates and Momentum 15:33
- L12.5 Choosing Different Optimizers in PyTorch 06:01
- L12.6 Additional Topics and Research on Optimization Algorithms 12:05
- L13.0 Introduction to Convolutional Networks -- Lecture Overview 05:25
- L13.1 Common Applications of CNNs 09:35
- L13.2 Challenges of Image Classification 07:45
- L13.3 Convolutional Neural Network Basics 18:40
- L13.4 Convolutional Filters and Weight-Sharing 20:20
- L13.5 Cross-correlation vs. Convolution (Old) 10:17
- L13.5 What's The Difference Between Cross-Correlation And Convolution? 10:38
- Deep Learning News #8 Mar 20 2021 18:03
- L13.6 CNNs & Backpropagation 05:54
- L13.7 CNN Architectures & AlexNet 20:17
- L13.8 What a CNN Can See 13:43
- L13.9.1 LeNet-5 in PyTorch 13:12
- L13.9.2 Saving and Loading Models in PyTorch 05:45
- L13.9.3 AlexNet in PyTorch 15:16
- Deep Learning News #9, Mar 27 2021 28:10
- L14.0: Convolutional Neural Networks Architectures -- Lecture Overview 06:18
- L14.1: Convolutions and Padding 11:14
- L14.2: Spatial Dropout and BatchNorm 06:46
- L14.3: Architecture Overview 03:24
- L14.3.1.1 VGG16 Overview 06:06
- L14.3.1.2 VGG16 in PyTorch -- Code Example 15:52
- L14.3.2.1 ResNet Overview 14:42
- L14.3.2.2 ResNet-34 in PyTorch -- Code Example 18:48
- Deep Learning News #10, Apr 3 2021 20:55
- L14.4.1 Replacing Max-Pooling with Convolutional Layers 08:19
- L14.4.2 All-Convolutional Network in PyTorch -- Code Example 08:17
- L14.5 Convolutional Instead of Fully Connected Layers 14:33
- L14.6.1 Transfer Learning 07:39
- L14.6.2 Transfer Learning in PyTorch -- Code Example 11:36
- L15.0: Introduction to Recurrent Neural Networks -- Lecture Overview 03:59
- L15.1: Different Methods for Working With Text Data 15:58
- L15.2 Sequence Modeling with RNNs 13:40
- L15.3 Different Types of Sequence Modeling Tasks 04:32
- L15.4 Backpropagation Through Time Overview 09:34
- L15.5 Long Short-Term Memory 16:58
- L15.6 RNNs for Classification: A Many-to-One Word RNN 29:07
- L15.7 An RNN Sentiment Classifier in PyTorch 40:00
- L16.0 Introduction to Autoencoders -- Lecture Overview 04:45
- L16.1 Dimensionality Reduction 09:40
- L16.2 A Fully-Connected Autoencoder 16:35
- L16.3 Convolutional Autoencoders & Transposed Convolutions 16:08
- L16.4 A Convolutional Autoencoder in PyTorch -- Code Example 15:21
- L16.5 Other Types of Autoencoders 05:34
- L17.0 Intro to Variational Autoencoders -- Lecture Overview 03:16
- L17.1 Variational Autoencoder Overview 05:24
- L17.2 Sampling from a Variational Autoencoder 09:27
- L17.3 The Log-Var Trick 07:35
- L17.4 Variational Autoencoder Loss Function 12:16
- L17.5 A Variational Autoencoder for Handwritten Digits in PyTorch -- Code Example 23:13
- L17.6 A Variational Autoencoder for Face Images in PyTorch -- Code Example 10:06
- L17.7 VAE Latent Space Arithmetic in PyTorch -- Making People Smile (Code Example) 11:54
- L18.0: Introduction to Generative Adversarial Networks -- Lecture Overview 05:15
- L18.1: The Main Idea Behind GANs 10:43
- L18.2: The GAN Objective 26:26
- L18.3: Modifying the GAN Loss Function for Practical Use 18:50
- L18.4: A GAN for Generating Handwritten Digits in PyTorch -- Code Example 22:46
- L18.5: Tips and Tricks to Make GANs Work 17:14
- L18.6: A DCGAN for Generating Face Images in PyTorch -- Code Example 12:43
- L19.0 RNNs & Transformers for Sequence-to-Sequence Modeling -- Lecture Overview 03:05
- L19.1 Sequence Generation with Word and Character RNNs 17:44
- L19.2.1 Implementing a Character RNN in PyTorch (Concepts) 09:20
- L19.2.2 Implementing a Character RNN in PyTorch --Code Example 25:57
- L19.3 RNNs with an Attention Mechanism 22:19
- L19.4.1 Using Attention Without the RNN -- A Basic Form of Self-Attention 16:11
- L19.4.2 Self-Attention and Scaled Dot-Product Attention 16:09
- L19.4.3 Multi-Head Attention 07:37
- L19.5.1 The Transformer Architecture 22:36
- L19.5.2.1 Some Popular Transformer Models: BERT, GPT, and BART -- Overview 08:41
- L19.5.2.2 GPT-v1: Generative Pre-Trained Transformer 09:54
- L19.5.2.3 BERT: Bidirectional Encoder Representations from Transformers 18:31
- L19.5.2.4 GPT-v2: Language Models are Unsupervised Multitask Learners 09:03
- L19.5.2.5 GPT-v3: Language Models are Few-Shot Learners 06:41
- L19.5.2.6 BART: Combining Bidirectional and Auto-Regressive Transformers 10:15
- L19.5.2.7: Closing Words -- The Recent Growth of Language Transformers 06:10
- L19.6 DistilBert Movie Review Classifier in PyTorch -- Code Example 17:58
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