Udemy - The Complete Neural Networks Bootcamp Theory, Applications (11.2021)

Creation Time	Sept. 12, 2025, 6:37 p.m.
Last Access Time	Sept. 13, 2025, 10:42 a.m.
File Size	13.4 GB
Keywords	11 Complete Neural - Udemy Applications 2021 The Bootcamp Networks Theory
Total Requests	23
Total Files	599
Magnet Link	Please wait ...
	Download (Magnet Link) Play (Watch Online)

31 - Practical Sequence Modelling in PyTorch Chatbot Application/004 Defining the Encoder.mp4 222.6 MB
22 - Autoencoders and Variational Autoencoders/006 Loss Function Derivation for VAE.mp4 219.3 MB
22 - Autoencoders and Variational Autoencoders/005 Probability Distributions Recap.mp4 187.6 MB
13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/006 Training the Network.mp4 182.7 MB
15 - Practical Convolutional Networks in PyTorch - Image Classification/003 Building the CNN.mp4 171.4 MB
01 - How Neural Networks and Backpropagation Works/002 What Can Deep Learning Do.mp4 156.2 MB
08 - Introduction to PyTorch/009 Loss Functions in PyTorch.mp4 155.0 MB
31 - Practical Sequence Modelling in PyTorch Chatbot Application/006 Designing the Attention Model.mp4 147.4 MB
34 - Build a Chatbot with Transformers/017 Loss with Label Smoothing.mp4 138.5 MB
19 - Transfer Learning in PyTorch - Image Classification/001 Data Augmentation.mp4 120.3 MB
10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/005 Part 4 Building the Network.mp4 109.6 MB
16 - CNN Architectures/003 Residual Networks Part 2.mp4 107.7 MB
38 - Vision Transformers/003 Vision Transformer Part 3.mp4 106.4 MB
32 - Practical Sequence Modelling in PyTorch Image Captioning/010 Train Function.mp4 105.8 MB
35 - Universal Transformers/002 Practical Universal Transformers Modifying the Transformers code.mp4 105.1 MB
10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/006 Part 5 Training the Network.mp4 104.7 MB
31 - Practical Sequence Modelling in PyTorch Chatbot Application/008 Designing the Decoder Part 2.mp4 104.3 MB
12 - Implementing a Neural Network from Scratch with Numpy/008 Backpropagation.mp4 102.1 MB
16 - CNN Architectures/005 Stochastic Depth.mp4 100.9 MB
28 - Practical Recurrent Networks in PyTorch/007 Generating Text.mp4 98.4 MB
19 - Transfer Learning in PyTorch - Image Classification/002 Loading the Dataset.mp4 96.6 MB
39 - GPT/013 (6) GPT Implementation Part 1.mp4 96.6 MB
39 - GPT/012 (5) GPT Implementation Part 1.mp4 95.8 MB
09 - Data Augmentation/003 2_Data Augmentation Techniques Part 2.mp4 95.2 MB
17 - Practical Residual Networks in PyTorch/004 Practical ResNet Part 4.mp4 93.1 MB
34 - Build a Chatbot with Transformers/003 Dataset Preprocessing Part 2.mp4 90.0 MB
08 - Introduction to PyTorch/004 How PyTorch Works.mp4 88.8 MB
25 - Practical Neural Style Transfer in PyTorch/004 NST Practical Part 4.mp4 88.0 MB
32 - Practical Sequence Modelling in PyTorch Image Captioning/004 Constructing the Dataset Part 1.mp4 87.6 MB
37 - BERT/005 Exploring Transformers.mp4 87.1 MB
16 - CNN Architectures/002 Residual Networks Part 1.mp4 86.8 MB
02 - Loss Functions/011 Triplet Ranking Loss.mp4 86.7 MB
38 - Vision Transformers/001 Vision Transformer Part 1.mp4 85.3 MB
26 - Recurrent Neural Networks/007 LSTMs.mp4 84.9 MB
25 - Practical Neural Style Transfer in PyTorch/002 NST Practical Part 2.mp4 84.6 MB
31 - Practical Sequence Modelling in PyTorch Chatbot Application/007 Designing the Decoder Part 1.mp4 84.4 MB
21 - YOLO Object Detection (Theory)/003 YOLO Theory Part 3.mp4 84.4 MB
32 - Practical Sequence Modelling in PyTorch Image Captioning/009 Creating the Decoder Part 3.mp4 84.0 MB
10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/002 Part 1 Data Preprocessing.mp4 82.6 MB
28 - Practical Recurrent Networks in PyTorch/006 Training the Network.mp4 82.5 MB
34 - Build a Chatbot with Transformers/011 MultiHead Attention Implementation Part 3.mp4 82.4 MB
30 - Sequence Modelling/001 Sequence Modeling.mp4 81.6 MB
21 - YOLO Object Detection (Theory)/006 YOLO Theory Part 6.mp4 81.4 MB
09 - Data Augmentation/002 2_Data Augmentation Techniques Part 1.mp4 81.3 MB
20 - Convolutional Networks Visualization/002 Processing the Model.mp4 80.5 MB
01 - How Neural Networks and Backpropagation Works/005 The Perceptron.mp4 80.4 MB
39 - GPT/010 (3) GPT Implementation Part 1.mp4 80.2 MB
13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/003 Importing and Defining Parameters.mp4 79.1 MB
32 - Practical Sequence Modelling in PyTorch Image Captioning/007 Creating the Decoder Part 1.mp4 79.0 MB
15 - Practical Convolutional Networks in PyTorch - Image Classification/006 Training the CNN.mp4 78.2 MB
39 - GPT/009 (2) GPT Implementation Part 1.mp4 77.2 MB
34 - Build a Chatbot with Transformers/015 Transformer.mp4 77.0 MB
09 - Data Augmentation/004 2_Data Augmentation Techniques Part 3.mp4 76.9 MB
20 - Convolutional Networks Visualization/003 Visualizing the Feature Maps.mp4 76.8 MB
29 - Saving and Loading Models/001 Saving and Loading Part 1.mp4 76.2 MB
35 - Universal Transformers/003 Transformers for other tasks.mp4 75.7 MB
34 - Build a Chatbot with Transformers/020 Evaluation Function.mp4 74.0 MB
21 - YOLO Object Detection (Theory)/001 YOLO Theory Part 1.mp4 71.9 MB
23 - Practical Variational Autoencoders in PyTorch/001 Practical VAE Part 1.mp4 71.5 MB
32 - Practical Sequence Modelling in PyTorch Image Captioning/011 Defining Hyperparameters.mp4 70.3 MB
21 - YOLO Object Detection (Theory)/005 YOLO Theory Part 5.mp4 70.1 MB
25 - Practical Neural Style Transfer in PyTorch/003 NST Practical Part 3.mp4 69.9 MB
33 - Transformers/004 Positional Encoding.mp4 69.5 MB
12 - Implementing a Neural Network from Scratch with Numpy/007 Backpropagation Equations.mp4 69.3 MB
04 - Regularization and Normalization/006 Batch Normalization.mp4 68.6 MB
17 - Practical Residual Networks in PyTorch/003 Practical ResNet Part 3.mp4 68.4 MB
08 - Introduction to PyTorch/006 Torch Tensors - Part 2.mp4 67.9 MB
34 - Build a Chatbot with Transformers/019 Training Function.mp4 67.2 MB
23 - Practical Variational Autoencoders in PyTorch/002 Practical VAE Part 2.mp4 66.8 MB
19 - Transfer Learning in PyTorch - Image Classification/006 Testing and Visualizing the results.mp4 66.7 MB
16 - CNN Architectures/007 Densely Connected Networks.mp4 65.9 MB
14 - Convolutional Neural Networks/014 DropBlock Dropout in CNNs.mp4 65.3 MB
07 - Weight Initialization/003 Xavier Initialization.mp4 64.9 MB
28 - Practical Recurrent Networks in PyTorch/005 Creating the Network.mp4 64.5 MB
23 - Practical Variational Autoencoders in PyTorch/003 Practical VAE Part 3.mp4 63.5 MB
32 - Practical Sequence Modelling in PyTorch Image Captioning/008 Creating the Decoder Part 2.mp4 63.1 MB
39 - GPT/001 GPT Part 1.mp4 63.1 MB
34 - Build a Chatbot with Transformers/021 Main Function and User Evaluation.mp4 62.8 MB
28 - Practical Recurrent Networks in PyTorch/003 Processing the Text.mp4 62.8 MB
32 - Practical Sequence Modelling in PyTorch Image Captioning/012 Evaluation Function.mp4 61.7 MB
34 - Build a Chatbot with Transformers/006 Dataset Preprocessing Part 5.mp4 61.2 MB
05 - Optimization/013 AMSGrad.mp4 60.9 MB
12 - Implementing a Neural Network from Scratch with Numpy/003 Forward Propagation.mp4 60.4 MB
32 - Practical Sequence Modelling in PyTorch Image Captioning/006 Creating the Encoder.mp4 60.0 MB
19 - Transfer Learning in PyTorch - Image Classification/004 Understanding the data.mp4 58.9 MB
14 - Convolutional Neural Networks/009 Activation, Pooling and FC.mp4 58.7 MB
17 - Practical Residual Networks in PyTorch/002 Practical ResNet Part 2.mp4 58.4 MB
39 - GPT/014 (7) GPT Implementation Part 1.mp4 56.7 MB
22 - Autoencoders and Variational Autoencoders/007 Deep Fake.mp4 56.6 MB
34 - Build a Chatbot with Transformers/013 Encoder Layer.mp4 56.1 MB
29 - Saving and Loading Models/002 Saving and Loading Part 2.mp4 55.4 MB
34 - Build a Chatbot with Transformers/002 Dataset Preprocessing Part 1.mp4 55.2 MB
34 - Build a Chatbot with Transformers/004 Dataset Preprocessing Part 3.mp4 55.2 MB
08 - Introduction to PyTorch/003 Installing PyTorch and an Introduction.mp4 54.9 MB
05 - Optimization/011 Weight Decay.mp4 54.2 MB
34 - Build a Chatbot with Transformers/007 Data Loading and Masking.mp4 54.0 MB
19 - Transfer Learning in PyTorch - Image Classification/003 Modifying the Network.mp4 53.8 MB
02 - Loss Functions/002 L1 Loss (MAE).mp4 53.7 MB
34 - Build a Chatbot with Transformers/008 Embeddings.mp4 53.1 MB
05 - Optimization/009 Adam Optimization.mp4 53.0 MB
21 - YOLO Object Detection (Theory)/008 YOLO Theory Part 8.mp4 52.1 MB
31 - Practical Sequence Modelling in PyTorch Chatbot Application/003 Understanding the Encoder.mp4 52.0 MB
21 - YOLO Object Detection (Theory)/002 YOLO Theory Part 2.mp4 51.6 MB
08 - Introduction to PyTorch/005 Torch Tensors - Part 1.mp4 51.3 MB
04 - Regularization and Normalization/003 Dropout.mp4 51.3 MB
33 - Transformers/016 Dropout.mp4 51.3 MB
18 - Transposed Convolutions/002 Convolution Operation as Matrix Multiplication.mp4 51.0 MB
32 - Practical Sequence Modelling in PyTorch Image Captioning/003 Accuracy Calculation.mp4 50.8 MB
11 - Visualize the Learning Process/005 Visualize Learning Part 5.mp4 50.8 MB
17 - Practical Residual Networks in PyTorch/001 Practical ResNet Part 1.mp4 50.8 MB
22 - Autoencoders and Variational Autoencoders/004 Variational Autoencoders.mp4 50.5 MB
01 - How Neural Networks and Backpropagation Works/004 The Essence of Neural Networks.mp4 50.0 MB
24 - Neural Style Transfer/003 NST Theory Part 3.mp4 49.8 MB
34 - Build a Chatbot with Transformers/016 AdamWarmup.mp4 49.4 MB
06 - Hyperparameter Tuning and Learning Rate Scheduling/003 Cyclic Learning Rate.mp4 49.3 MB
12 - Implementing a Neural Network from Scratch with Numpy/004 Loss Function.mp4 48.6 MB
02 - Loss Functions/010 Hinge Loss.mp4 48.5 MB
12 - Implementing a Neural Network from Scratch with Numpy/001 The Dataset and Hyperparameters.mp4 48.5 MB
21 - YOLO Object Detection (Theory)/007 YOLO Theory Part 7.mp4 48.4 MB
13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/004 Defining the Network Class.mp4 48.2 MB
26 - Recurrent Neural Networks/006 Vanishing and Exploding Gradient Problem.mp4 47.2 MB
33 - Transformers/003 Input Embeddings.mp4 46.5 MB
08 - Introduction to PyTorch/007 Numpy Bridge, Tensor Concatenation and Adding Dimensions.mp4 45.4 MB
25 - Practical Neural Style Transfer in PyTorch/001 NST Practical Part 1.mp4 45.2 MB
06 - Hyperparameter Tuning and Learning Rate Scheduling/002 Step Learning Rate Decay.mp4 45.2 MB
08 - Introduction to PyTorch/008 Automatic Differentiation.mp4 44.8 MB
16 - CNN Architectures/009 Seperable Convolutions.mp4 44.6 MB
27 - Word Embeddings/001 What are Word Embeddings.mp4 44.2 MB
08 - Introduction to PyTorch/010 Weight Initialization in PyTorch.mp4 44.2 MB
07 - Weight Initialization/002 What happens when all weights are initialized to the same value.mp4 43.9 MB
33 - Transformers/005 MultiHead Attention Part 1.mp4 43.6 MB
10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/004 Part 3 Creating and Loading the Dataset.mp4 43.3 MB
16 - CNN Architectures/011 Is a 1x1 convolutional filter equivalent to a FC layer.mp4 43.2 MB
02 - Loss Functions/009 Contrastive Loss.mp4 42.9 MB
15 - Practical Convolutional Networks in PyTorch - Image Classification/002 Visualizing and Loading the Dataset.mp4 42.2 MB
20 - Convolutional Networks Visualization/001 Data and the Model.mp4 42.0 MB
34 - Build a Chatbot with Transformers/014 Decoder Layer.mp4 41.9 MB
01 - How Neural Networks and Backpropagation Works/003 The Rise of Deep Learning.mp4 41.8 MB
31 - Practical Sequence Modelling in PyTorch Chatbot Application/002 Introduction.mp4 41.6 MB
08 - Introduction to PyTorch/002 Computation Graphs and Deep Learning Frameworks.mp4 41.5 MB
34 - Build a Chatbot with Transformers/009 MultiHead Attention Implementation Part 1.mp4 41.1 MB
32 - Practical Sequence Modelling in PyTorch Image Captioning/005 Constructing the Dataset Part 2.mp4 41.0 MB
36 - Google Colab and Gradient Accumulation/002 Gradient Accumulation.mp4 40.6 MB
28 - Practical Recurrent Networks in PyTorch/004 Defining and Visualizing the Parameters.mp4 40.6 MB
01 - How Neural Networks and Backpropagation Works/007 The Forward Propagation.mp4 40.4 MB
26 - Recurrent Neural Networks/004 Backpropagation Through Time.mp4 39.7 MB
12 - Implementing a Neural Network from Scratch with Numpy/009 Initializing the Network.mp4 39.3 MB
11 - Visualize the Learning Process/006 Visualize Learning Part 6.mp4 39.3 MB
21 - YOLO Object Detection (Theory)/012 YOLO Theory Part 12.mp4 39.1 MB
26 - Recurrent Neural Networks/002 Vanilla RNNs.mp4 38.2 MB
32 - Practical Sequence Modelling in PyTorch Image Captioning/001 Implementation Details.mp4 37.9 MB
10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/003 Part 2 Data Normalization.mp4 37.7 MB
39 - GPT/005 Technical Details of GPT.mp4 37.5 MB
21 - YOLO Object Detection (Theory)/011 YOLO Theory Part 11.mp4 37.0 MB
14 - Convolutional Neural Networks/003 Filters and Features.mp4 36.6 MB
15 - Practical Convolutional Networks in PyTorch - Image Classification/001 Loading and Normalizing the Dataset.mp4 36.6 MB
14 - Convolutional Neural Networks/001 Prerequisite Filters.mp4 36.4 MB
24 - Neural Style Transfer/001 NST Theory Part 1.mp4 35.9 MB
37 - BERT/004 Fine-tuning BERT.mp4 35.9 MB
38 - Vision Transformers/002 Vision Transformer Part 2.mp4 35.3 MB
05 - Optimization/001 Batch Gradient Descent.mp4 35.3 MB
33 - Transformers/002 Introduction to Transformers.mp4 35.0 MB
05 - Optimization/012 Decoupling Weight Decay.mp4 35.0 MB
15 - Practical Convolutional Networks in PyTorch - Image Classification/010 Classifying your own Handwritten images.mp4 35.0 MB
34 - Build a Chatbot with Transformers/010 MultiHead Attention Implementation Part 2.mp4 34.9 MB
33 - Transformers/006 MultiHead Attention Part 2.mp4 34.9 MB
28 - Practical Recurrent Networks in PyTorch/002 Creating the Dictionary.mp4 34.6 MB
29 - Saving and Loading Models/003 Saving and Loading Part 3.mp4 34.2 MB
39 - GPT/003 Zero-Shot Predictions with GPT.mp4 33.5 MB
14 - Convolutional Neural Networks/012 CNN Characteristics.mp4 33.3 MB
04 - Regularization and Normalization/007 Layer Normalization.mp4 33.2 MB
02 - Loss Functions/006 Softmax Function.mp4 32.6 MB
39 - GPT/002 GPT Part 2.mp4 32.5 MB
13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/005 Creating the network class and the network functions.mp4 32.3 MB
27 - Word Embeddings/005 Word Embeddings in PyTorch.mp4 31.2 MB
16 - CNN Architectures/001 CNN Architectures Part 1.mp4 30.8 MB
22 - Autoencoders and Variational Autoencoders/001 Autoencoders.mp4 30.5 MB
09 - Data Augmentation/001 1_Introduction to Data Augmentation.mp4 30.3 MB
02 - Loss Functions/004 Binary Cross Entropy Loss.mp4 29.9 MB
19 - Transfer Learning in PyTorch - Image Classification/005 Finetuning the Network.mp4 29.5 MB
32 - Practical Sequence Modelling in PyTorch Image Captioning/002 Utility Functions.mp4 29.3 MB
15 - Practical Convolutional Networks in PyTorch - Image Classification/008 Plotting and Putting into Action.mp4 29.0 MB
34 - Build a Chatbot with Transformers/018 Defining the Model.mp4 29.0 MB
25 - Practical Neural Style Transfer in PyTorch/005 Fast Neural Style Transfer.mp4 28.9 MB
30 - Sequence Modelling/004 How Attention Mechanisms Work.mp4 28.1 MB
03 - Activation Functions/008 Mish Activation.mp4 27.9 MB
01 - How Neural Networks and Backpropagation Works/006 Gradient Descent.mp4 27.7 MB
37 - BERT/003 Next Sentence Prediction.mp4 27.7 MB
34 - Build a Chatbot with Transformers/012 Feed Forward Implementation.mp4 27.6 MB
05 - Optimization/005 Exponentially Weighted Average Implementation.mp4 27.5 MB
16 - CNN Architectures/008 Squeeze-Excite Networks.mp4 27.5 MB
12 - Implementing a Neural Network from Scratch with Numpy/010 Training the Model.mp4 27.3 MB
18 - Transposed Convolutions/003 Transposed Convolutions.mp4 27.1 MB
13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/007 Testing the Network.mp4 27.0 MB
39 - GPT/004 Byte-Pair Encoding.mp4 27.0 MB
39 - GPT/011 (4) GPT Implementation Part 1.mp4 26.1 MB
05 - Optimization/008 RMSProp.mp4 26.0 MB
06 - Hyperparameter Tuning and Learning Rate Scheduling/004 Cosine Annealing with Warm Restarts.mp4 25.7 MB
15 - Practical Convolutional Networks in PyTorch - Image Classification/007 Testing the CNN.mp4 24.9 MB
37 - BERT/001 What is BERT and its structure.mp4 24.3 MB
24 - Neural Style Transfer/002 NST Theory Part 2.mp4 23.8 MB
33 - Transformers/013 Cross Entropy Loss.mp4 23.3 MB
36 - Google Colab and Gradient Accumulation/001 Running your models on Google Colab.mp4 22.8 MB
04 - Regularization and Normalization/002 L1 and L2 Regularization.mp4 22.6 MB
14 - Convolutional Neural Networks/006 More on Convolutions.mp4 22.5 MB
01 - How Neural Networks and Backpropagation Works/009 Backpropagation Part 1.mp4 21.7 MB
18 - Transposed Convolutions/001 Introduction to Transposed Convolutions.mp4 21.6 MB
33 - Transformers/017 Learning Rate Warmup.mp4 21.3 MB
11 - Visualize the Learning Process/007 Neural Networks Playground.mp4 21.0 MB
11 - Visualize the Learning Process/003 Visualize Learning Part 3.mp4 20.8 MB
39 - GPT/006 Playing with HuggingFace models.mp4 20.8 MB
30 - Sequence Modelling/002 Image Captioning.mp4 20.4 MB
16 - CNN Architectures/010 Transfer Learning.mp4 20.3 MB
34 - Build a Chatbot with Transformers/022 Action.mp4 20.3 MB
14 - Convolutional Neural Networks/015 Softmax with Temperature.mp4 20.3 MB
02 - Loss Functions/003 Huber Loss.mp4 20.2 MB
01 - How Neural Networks and Backpropagation Works/010 Backpropagation Part 2.mp4 20.2 MB
26 - Recurrent Neural Networks/009 GRUs.mp4 20.0 MB
14 - Convolutional Neural Networks/008 A Tool for Convolution Visualization.mp4 19.9 MB
22 - Autoencoders and Variational Autoencoders/002 Denoising Autoencoders.mp4 19.9 MB
05 - Optimization/006 Bias Correction in Exponentially Weighted Averages.mp4 19.8 MB
31 - Practical Sequence Modelling in PyTorch Chatbot Application/005 Understanding Pack Padded Sequence.mp4 19.5 MB
13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/002 Code Details.mp4 19.2 MB
03 - Activation Functions/006 Gated Linear Units (GLU).mp4 19.0 MB
04 - Regularization and Normalization/008 Group Normalization.mp4 19.0 MB
32 - Practical Sequence Modelling in PyTorch Image Captioning/014 Results.mp4 18.8 MB
02 - Loss Functions/008 KL divergence Loss.mp4 18.7 MB
11 - Visualize the Learning Process/001 Visualize Learning Part 1.mp4 18.6 MB
33 - Transformers/008 Residual Learning.mp4 18.5 MB
21 - YOLO Object Detection (Theory)/004 YOLO Theory Part 4.mp4 18.4 MB
05 - Optimization/007 Momentum.mp4 18.4 MB
33 - Transformers/011 Masked MultiHead Attention.mp4 18.3 MB
15 - Practical Convolutional Networks in PyTorch - Image Classification/005 Understanding the Propagation.mp4 18.2 MB
33 - Transformers/014 KL Divergence Loss.mp4 17.6 MB
02 - Loss Functions/005 Cross Entropy Loss.mp4 17.2 MB
21 - YOLO Object Detection (Theory)/010 YOLO Theory Part 10.mp4 17.1 MB
12 - Implementing a Neural Network from Scratch with Numpy/002 Understanding the Implementation.mp4 17.1 MB
14 - Convolutional Neural Networks/002 Introduction to Convolutional Networks and the need for them.mp4 16.8 MB
12 - Implementing a Neural Network from Scratch with Numpy/005 Prediction.mp4 16.8 MB
06 - Hyperparameter Tuning and Learning Rate Scheduling/005 Batch Size vs Learning Rate.mp4 16.7 MB
04 - Regularization and Normalization/001 Overfitting.mp4 16.6 MB
03 - Activation Functions/001 Why we need activation functions.mp4 16.6 MB
39 - GPT/008 (1) GPT Implementation Part 1.mp4 16.1 MB
37 - BERT/002 Masked Language Modelling.mp4 15.7 MB
11 - Visualize the Learning Process/004 Visualize Learning Part 4.mp4 15.0 MB
03 - Activation Functions/004 ReLU and PReLU.mp4 14.9 MB
35 - Universal Transformers/001 Universal Transformers.mp4 14.9 MB
03 - Activation Functions/002 Sigmoid Activation.mp4 14.8 MB
33 - Transformers/009 Layer Normalization.mp4 14.6 MB
05 - Optimization/004 Exponentially Weighted Average Intuition.mp4 14.4 MB
26 - Recurrent Neural Networks/010 CNN-LSTM.mp4 14.3 MB
31 - Practical Sequence Modelling in PyTorch Chatbot Application/009 Teacher Forcing.mp4 13.9 MB
02 - Loss Functions/001 Mean Squared Error (MSE).mp4 13.6 MB
34 - Build a Chatbot with Transformers/005 Dataset Preprocessing Part 4.mp4 13.6 MB
14 - Convolutional Neural Networks/013 Regularization and Batch Normalization in CNNs.mp4 13.4 MB
15 - Practical Convolutional Networks in PyTorch - Image Classification/004 Defining the Model.mp4 13.0 MB
14 - Convolutional Neural Networks/005 Convolution over Volume Animation.mp4 12.7 MB
15 - Practical Convolutional Networks in PyTorch - Image Classification/009 Predicting an image.mp4 12.7 MB
07 - Weight Initialization/001 Normal Distribution.mp4 12.6 MB
26 - Recurrent Neural Networks/001 Why do we need RNNs.mp4 12.3 MB
21 - YOLO Object Detection (Theory)/009 YOLO Theory Part 9.mp4 12.2 MB
05 - Optimization/002 Stochastic Gradient Descent.mp4 12.1 MB
30 - Sequence Modelling/003 Attention Mechanisms.mp4 12.0 MB
06 - Hyperparameter Tuning and Learning Rate Scheduling/001 Introduction to Hyperparameter Tuning and Learning Rate Recap.mp4 11.8 MB
33 - Transformers/010 Feed Forward.mp4 11.1 MB
26 - Recurrent Neural Networks/003 Quiz Solution Discussion.mp4 11.0 MB
03 - Activation Functions/003 Tanh Activation.mp4 10.6 MB
04 - Regularization and Normalization/004 DropConnect.mp4 10.1 MB
14 - Convolutional Neural Networks/011 Important formulas.mp4 9.8 MB
22 - Autoencoders and Variational Autoencoders/003 The Problem in Autoencoders.mp4 9.6 MB
33 - Transformers/015 Label Smoothing.mp4 9.6 MB
26 - Recurrent Neural Networks/008 Bidirectional RNNs.mp4 9.4 MB
04 - Regularization and Normalization/005 Normalization.mp4 9.3 MB
16 - CNN Architectures/006 CNN Architectures Part 2.mp4 9.0 MB
03 - Activation Functions/007 Swish Activation.mp4 9.0 MB
32 - Practical Sequence Modelling in PyTorch Image Captioning/013 Training.mp4 8.9 MB
14 - Convolutional Neural Networks/010 CNN Visualization.mp4 8.9 MB
07 - Weight Initialization/004 He Norm Initialization.mp4 8.8 MB
27 - Word Embeddings/002 Visualizing Word Embeddings.mp4 8.8 MB
11 - Visualize the Learning Process/002 Visualize Learning Part 2.mp4 8.4 MB
03 - Activation Functions/005 Exponentially Linear Units (ELU).mp4 8.2 MB
33 - Transformers/007 Concat and Linear.mp4 7.3 MB
27 - Word Embeddings/004 Word Embeddings Models.mp4 7.2 MB
33 - Transformers/012 MultiHead Attention in Decoder.mp4 7.2 MB
05 - Optimization/010 SWATS - Switching from Adam to SGD.mp4 6.4 MB
26 - Recurrent Neural Networks/005 Stacked RNNs.mp4 5.8 MB
05 - Optimization/003 Mini-Batch Gradient Descent.mp4 5.0 MB
14 - Convolutional Neural Networks/007 Quiz Solution Discussion.mp4 4.5 MB
27 - Word Embeddings/003 Measuring Word Embeddings.mp4 4.0 MB
08 - Introduction to PyTorch/001 CODE FOR THIS COURSE.mp4 1.1 MB
20 - Convolutional Networks Visualization/19339744-dog.jpg 93.3 KB
22 - Autoencoders and Variational Autoencoders/005 Probability Distributions Recap.vtt 38.5 KB
20 - Convolutional Networks Visualization/13787548-imagenet-class-index.json 34.5 KB
22 - Autoencoders and Variational Autoencoders/006 Loss Function Derivation for VAE.vtt 33.7 KB
08 - Introduction to PyTorch/009 Loss Functions in PyTorch.vtt 33.0 KB
13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/006 Training the Network.vtt 28.9 KB
15 - Practical Convolutional Networks in PyTorch - Image Classification/003 Building the CNN.vtt 28.5 KB
31 - Practical Sequence Modelling in PyTorch Chatbot Application/004 Defining the Encoder.vtt 27.9 KB
26 - Recurrent Neural Networks/007 LSTMs.vtt 25.5 KB
12 - Implementing a Neural Network from Scratch with Numpy/008 Backpropagation.vtt 24.7 KB
23 - Practical Variational Autoencoders in PyTorch/001 Practical VAE Part 1.vtt 22.9 KB
34 - Build a Chatbot with Transformers/017 Loss with Label Smoothing.vtt 22.3 KB
08 - Introduction to PyTorch/004 How PyTorch Works.vtt 21.4 KB
10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/006 Part 5 Training the Network.vtt 20.7 KB
16 - CNN Architectures/003 Residual Networks Part 2.vtt 20.7 KB
09 - Data Augmentation/003 2_Data Augmentation Techniques Part 2.vtt 20.6 KB
32 - Practical Sequence Modelling in PyTorch Image Captioning/006 Creating the Encoder.vtt 20.3 KB
31 - Practical Sequence Modelling in PyTorch Chatbot Application/008 Designing the Decoder Part 2.vtt 20.3 KB
10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/005 Part 4 Building the Network.vtt 20.1 KB
32 - Practical Sequence Modelling in PyTorch Image Captioning/007 Creating the Decoder Part 1.vtt 20.0 KB
01 - How Neural Networks and Backpropagation Works/005 The Perceptron.vtt 19.1 KB
32 - Practical Sequence Modelling in PyTorch Image Captioning/012 Evaluation Function.vtt 19.0 KB
12 - Implementing a Neural Network from Scratch with Numpy/004 Loss Function.vtt 18.9 KB
15 - Practical Convolutional Networks in PyTorch - Image Classification/006 Training the CNN.vtt 18.7 KB
34 - Build a Chatbot with Transformers/020 Evaluation Function.vtt 18.6 KB
36 - Google Colab and Gradient Accumulation/002 Gradient Accumulation.vtt 18.5 KB
31 - Practical Sequence Modelling in PyTorch Chatbot Application/006 Designing the Attention Model.vtt 18.3 KB
32 - Practical Sequence Modelling in PyTorch Image Captioning/010 Train Function.vtt 18.1 KB
37 - BERT/005 Exploring Transformers.vtt 17.9 KB
34 - Build a Chatbot with Transformers/003 Dataset Preprocessing Part 2.vtt 17.9 KB
29 - Saving and Loading Models/001 Saving and Loading Part 1.vtt 17.4 KB
39 - GPT/013 (6) GPT Implementation Part 1.vtt 17.2 KB
34 - Build a Chatbot with Transformers/008 Embeddings.vtt 16.9 KB
10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/002 Part 1 Data Preprocessing.vtt 16.7 KB
25 - Practical Neural Style Transfer in PyTorch/004 NST Practical Part 4.vtt 16.5 KB
01 - How Neural Networks and Backpropagation Works/002 What Can Deep Learning Do.vtt 16.5 KB
32 - Practical Sequence Modelling in PyTorch Image Captioning/011 Defining Hyperparameters.vtt 16.4 KB
33 - Transformers/004 Positional Encoding.vtt 16.3 KB
16 - CNN Architectures/007 Densely Connected Networks.vtt 16.3 KB
31 - Practical Sequence Modelling in PyTorch Chatbot Application/007 Designing the Decoder Part 1.vtt 16.3 KB
32 - Practical Sequence Modelling in PyTorch Image Captioning/002 Utility Functions.vtt 16.3 KB
32 - Practical Sequence Modelling in PyTorch Image Captioning/004 Constructing the Dataset Part 1.vtt 16.2 KB
39 - GPT/014 (7) GPT Implementation Part 1.vtt 16.2 KB
16 - CNN Architectures/005 Stochastic Depth.vtt 16.1 KB
39 - GPT/009 (2) GPT Implementation Part 1.vtt 16.0 KB
39 - GPT/010 (3) GPT Implementation Part 1.vtt 16.0 KB
08 - Introduction to PyTorch/002 Computation Graphs and Deep Learning Frameworks.vtt 15.7 KB
20 - Convolutional Networks Visualization/002 Processing the Model.vtt 15.7 KB
30 - Sequence Modelling/001 Sequence Modeling.vtt 15.6 KB
02 - Loss Functions/004 Binary Cross Entropy Loss.vtt 15.5 KB
38 - Vision Transformers/001 Vision Transformer Part 1.vtt 15.3 KB
14 - Convolutional Neural Networks/009 Activation, Pooling and FC.vtt 15.3 KB
17 - Practical Residual Networks in PyTorch/004 Practical ResNet Part 4.vtt 15.3 KB
35 - Universal Transformers/002 Practical Universal Transformers Modifying the Transformers code.vtt 15.3 KB
34 - Build a Chatbot with Transformers/007 Data Loading and Masking.vtt 15.2 KB
32 - Practical Sequence Modelling in PyTorch Image Captioning/009 Creating the Decoder Part 3.vtt 15.1 KB
26 - Recurrent Neural Networks/004 Backpropagation Through Time.vtt 15.1 KB
02 - Loss Functions/010 Hinge Loss.vtt 14.9 KB
02 - Loss Functions/011 Triplet Ranking Loss.vtt 14.9 KB
08 - Introduction to PyTorch/010 Weight Initialization in PyTorch.vtt 14.8 KB
20 - Convolutional Networks Visualization/003 Visualizing the Feature Maps.vtt 14.7 KB
06 - Hyperparameter Tuning and Learning Rate Scheduling/002 Step Learning Rate Decay.vtt 14.7 KB
33 - Transformers/013 Cross Entropy Loss.vtt 14.7 KB
28 - Practical Recurrent Networks in PyTorch/007 Generating Text.vtt 14.6 KB
34 - Build a Chatbot with Transformers/011 MultiHead Attention Implementation Part 3.vtt 14.5 KB
04 - Regularization and Normalization/006 Batch Normalization.vtt 14.5 KB
21 - YOLO Object Detection (Theory)/002 YOLO Theory Part 2.vtt 14.5 KB
12 - Implementing a Neural Network from Scratch with Numpy/007 Backpropagation Equations.vtt 14.4 KB
17 - Practical Residual Networks in PyTorch/001 Practical ResNet Part 1.vtt 14.4 KB
17 - Practical Residual Networks in PyTorch/002 Practical ResNet Part 2.vtt 14.4 KB
02 - Loss Functions/009 Contrastive Loss.vtt 14.4 KB
15 - Practical Convolutional Networks in PyTorch - Image Classification/001 Loading and Normalizing the Dataset.vtt 14.3 KB
33 - Transformers/002 Introduction to Transformers.vtt 14.3 KB
32 - Practical Sequence Modelling in PyTorch Image Captioning/001 Implementation Details.vtt 14.2 KB
13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/003 Importing and Defining Parameters.vtt 14.2 KB
39 - GPT/012 (5) GPT Implementation Part 1.vtt 14.1 KB
17 - Practical Residual Networks in PyTorch/003 Practical ResNet Part 3.vtt 14.0 KB
19 - Transfer Learning in PyTorch - Image Classification/001 Data Augmentation.vtt 14.0 KB
12 - Implementing a Neural Network from Scratch with Numpy/001 The Dataset and Hyperparameters.vtt 14.0 KB
14 - Convolutional Neural Networks/014 DropBlock Dropout in CNNs.vtt 14.0 KB
38 - Vision Transformers/003 Vision Transformer Part 3.vtt 14.0 KB
12 - Implementing a Neural Network from Scratch with Numpy/003 Forward Propagation.vtt 13.8 KB
32 - Practical Sequence Modelling in PyTorch Image Captioning/005 Constructing the Dataset Part 2.vtt 13.8 KB
16 - CNN Architectures/001 CNN Architectures Part 1.vtt 13.8 KB
15 - Practical Convolutional Networks in PyTorch - Image Classification/010 Classifying your own Handwritten images.vtt 13.8 KB
01 - How Neural Networks and Backpropagation Works/006 Gradient Descent.vtt 13.8 KB
23 - Practical Variational Autoencoders in PyTorch/003 Practical VAE Part 3.vtt 13.7 KB
05 - Optimization/008 RMSProp.vtt 13.7 KB
16 - CNN Architectures/009 Seperable Convolutions.vtt 13.4 KB
30 - Sequence Modelling/004 How Attention Mechanisms Work.vtt 13.3 KB
34 - Build a Chatbot with Transformers/015 Transformer.vtt 13.3 KB
08 - Introduction to PyTorch/005 Torch Tensors - Part 1.vtt 13.3 KB
23 - Practical Variational Autoencoders in PyTorch/002 Practical VAE Part 2.vtt 13.2 KB
19 - Transfer Learning in PyTorch - Image Classification/004 Understanding the data.vtt 13.1 KB
25 - Practical Neural Style Transfer in PyTorch/003 NST Practical Part 3.vtt 13.1 KB
32 - Practical Sequence Modelling in PyTorch Image Captioning/008 Creating the Decoder Part 2.vtt 13.1 KB
08 - Introduction to PyTorch/007 Numpy Bridge, Tensor Concatenation and Adding Dimensions.vtt 13.1 KB
01 - How Neural Networks and Backpropagation Works/009 Backpropagation Part 1.vtt 12.8 KB
28 - Practical Recurrent Networks in PyTorch/005 Creating the Network.vtt 12.8 KB
19 - Transfer Learning in PyTorch - Image Classification/002 Loading the Dataset.vtt 12.8 KB
08 - Introduction to PyTorch/003 Installing PyTorch and an Introduction.vtt 12.7 KB
11 - Visualize the Learning Process/005 Visualize Learning Part 5.vtt 12.7 KB
16 - CNN Architectures/002 Residual Networks Part 1.vtt 12.7 KB
22 - Autoencoders and Variational Autoencoders/004 Variational Autoencoders.vtt 12.6 KB
34 - Build a Chatbot with Transformers/019 Training Function.vtt 12.6 KB
34 - Build a Chatbot with Transformers/004 Dataset Preprocessing Part 3.vtt 12.6 KB
01 - How Neural Networks and Backpropagation Works/007 The Forward Propagation.vtt 12.5 KB
25 - Practical Neural Style Transfer in PyTorch/001 NST Practical Part 1.vtt 12.5 KB
39 - GPT/001 GPT Part 1.vtt 12.4 KB
26 - Recurrent Neural Networks/006 Vanishing and Exploding Gradient Problem.vtt 12.3 KB
32 - Practical Sequence Modelling in PyTorch Image Captioning/003 Accuracy Calculation.vtt 12.3 KB
24 - Neural Style Transfer/003 NST Theory Part 3.vtt 12.3 KB
28 - Practical Recurrent Networks in PyTorch/003 Processing the Text.vtt 12.1 KB
21 - YOLO Object Detection (Theory)/012 YOLO Theory Part 12.vtt 12.0 KB
34 - Build a Chatbot with Transformers/002 Dataset Preprocessing Part 1.vtt 12.0 KB
16 - CNN Architectures/008 Squeeze-Excite Networks.vtt 12.0 KB
28 - Practical Recurrent Networks in PyTorch/006 Training the Network.vtt 11.9 KB
33 - Transformers/005 MultiHead Attention Part 1.vtt 11.9 KB
06 - Hyperparameter Tuning and Learning Rate Scheduling/003 Cyclic Learning Rate.vtt 11.8 KB
16 - CNN Architectures/011 Is a 1x1 convolutional filter equivalent to a FC layer.vtt 11.8 KB
19 - Transfer Learning in PyTorch - Image Classification/006 Testing and Visualizing the results.vtt 11.7 KB
08 - Introduction to PyTorch/006 Torch Tensors - Part 2.vtt 11.7 KB
07 - Weight Initialization/002 What happens when all weights are initialized to the same value.vtt 11.5 KB
01 - How Neural Networks and Backpropagation Works/004 The Essence of Neural Networks.vtt 11.5 KB
07 - Weight Initialization/003 Xavier Initialization.vtt 11.5 KB
14 - Convolutional Neural Networks/015 Softmax with Temperature.vtt 11.4 KB
34 - Build a Chatbot with Transformers/006 Dataset Preprocessing Part 5.vtt 11.3 KB
34 - Build a Chatbot with Transformers/021 Main Function and User Evaluation.vtt 11.2 KB
25 - Practical Neural Style Transfer in PyTorch/002 NST Practical Part 2.vtt 11.2 KB
14 - Convolutional Neural Networks/003 Filters and Features.vtt 11.1 KB
15 - Practical Convolutional Networks in PyTorch - Image Classification/002 Visualizing and Loading the Dataset.vtt 11.1 KB
21 - YOLO Object Detection (Theory)/003 YOLO Theory Part 3.vtt 11.0 KB
39 - GPT/002 GPT Part 2.vtt 11.0 KB
21 - YOLO Object Detection (Theory)/006 YOLO Theory Part 6.vtt 10.9 KB
01 - How Neural Networks and Backpropagation Works/010 Backpropagation Part 2.vtt 10.9 KB
27 - Word Embeddings/001 What are Word Embeddings.vtt 10.9 KB
39 - GPT/008 (1) GPT Implementation Part 1.vtt 10.9 KB
04 - Regularization and Normalization/003 Dropout.vtt 10.9 KB
33 - Transformers/016 Dropout.vtt 10.9 KB
11 - Visualize the Learning Process/001 Visualize Learning Part 1.vtt 10.8 KB
04 - Regularization and Normalization/002 L1 and L2 Regularization.vtt 10.7 KB
22 - Autoencoders and Variational Autoencoders/001 Autoencoders.vtt 10.7 KB
09 - Data Augmentation/002 2_Data Augmentation Techniques Part 1.vtt 10.7 KB
13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/004 Defining the Network Class.vtt 10.7 KB
38 - Vision Transformers/002 Vision Transformer Part 2.vtt 10.7 KB
08 - Introduction to PyTorch/008 Automatic Differentiation.vtt 10.7 KB
05 - Optimization/013 AMSGrad.vtt 10.5 KB
16 - CNN Architectures/010 Transfer Learning.vtt 10.5 KB
37 - BERT/003 Next Sentence Prediction.vtt 10.4 KB
05 - Optimization/005 Exponentially Weighted Average Implementation.vtt 10.3 KB
35 - Universal Transformers/003 Transformers for other tasks.vtt 10.2 KB
18 - Transposed Convolutions/002 Convolution Operation as Matrix Multiplication.vtt 10.1 KB
37 - BERT/001 What is BERT and its structure.vtt 10.1 KB
39 - GPT/011 (4) GPT Implementation Part 1.vtt 10.1 KB
02 - Loss Functions/002 L1 Loss (MAE).vtt 10.0 KB
12 - Implementing a Neural Network from Scratch with Numpy/002 Understanding the Implementation.vtt 9.9 KB
14 - Convolutional Neural Networks/012 CNN Characteristics.vtt 9.8 KB
19 - Transfer Learning in PyTorch - Image Classification/003 Modifying the Network.vtt 9.8 KB
26 - Recurrent Neural Networks/002 Vanilla RNNs.vtt 9.8 KB
02 - Loss Functions/005 Cross Entropy Loss.vtt 9.8 KB
33 - Transformers/006 MultiHead Attention Part 2.vtt 9.5 KB
39 - GPT/004 Byte-Pair Encoding.vtt 9.5 KB
39 - GPT/003 Zero-Shot Predictions with GPT.vtt 9.4 KB
34 - Build a Chatbot with Transformers/010 MultiHead Attention Implementation Part 2.vtt 9.3 KB
36 - Google Colab and Gradient Accumulation/001 Running your models on Google Colab.vtt 9.3 KB
10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/003 Part 2 Data Normalization.vtt 9.3 KB
11 - Visualize the Learning Process/003 Visualize Learning Part 3.vtt 9.3 KB
21 - YOLO Object Detection (Theory)/005 YOLO Theory Part 5.vtt 9.3 KB
09 - Data Augmentation/004 2_Data Augmentation Techniques Part 3.vtt 9.2 KB
20 - Convolutional Networks Visualization/001 Data and the Model.vtt 9.1 KB
04 - Regularization and Normalization/007 Layer Normalization.vtt 9.1 KB
22 - Autoencoders and Variational Autoencoders/007 Deep Fake.vtt 9.1 KB
29 - Saving and Loading Models/002 Saving and Loading Part 2.vtt 9.1 KB
02 - Loss Functions/006 Softmax Function.vtt 9.0 KB
34 - Build a Chatbot with Transformers/013 Encoder Layer.vtt 8.9 KB
11 - Visualize the Learning Process/006 Visualize Learning Part 6.vtt 8.9 KB
31 - Practical Sequence Modelling in PyTorch Chatbot Application/005 Understanding Pack Padded Sequence.vtt 8.7 KB
39 - GPT/006 Playing with HuggingFace models.vtt 8.7 KB
02 - Loss Functions/008 KL divergence Loss.vtt 8.7 KB
28 - Practical Recurrent Networks in PyTorch/004 Defining and Visualizing the Parameters.vtt 8.6 KB
10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/004 Part 3 Creating and Loading the Dataset.vtt 8.6 KB
33 - Transformers/009 Layer Normalization.vtt 8.6 KB
22 - Autoencoders and Variational Autoencoders/002 Denoising Autoencoders.vtt 8.4 KB
14 - Convolutional Neural Networks/002 Introduction to Convolutional Networks and the need for them.vtt 8.4 KB
05 - Optimization/011 Weight Decay.vtt 8.4 KB
02 - Loss Functions/001 Mean Squared Error (MSE).vtt 8.4 KB
05 - Optimization/009 Adam Optimization.vtt 8.4 KB
35 - Universal Transformers/001 Universal Transformers.vtt 8.4 KB
03 - Activation Functions/004 ReLU and PReLU.vtt 8.3 KB
24 - Neural Style Transfer/001 NST Theory Part 1.vtt 8.3 KB
37 - BERT/004 Fine-tuning BERT.vtt 8.2 KB
39 - GPT/005 Technical Details of GPT.vtt 8.2 KB
18 - Transposed Convolutions/001 Introduction to Transposed Convolutions.vtt 8.2 KB
26 - Recurrent Neural Networks/009 GRUs.vtt 8.1 KB
15 - Practical Convolutional Networks in PyTorch - Image Classification/007 Testing the CNN.vtt 8.0 KB
09 - Data Augmentation/001 1_Introduction to Data Augmentation.vtt 8.0 KB
34 - Build a Chatbot with Transformers/009 MultiHead Attention Implementation Part 1.vtt 8.0 KB
34 - Build a Chatbot with Transformers/016 AdamWarmup.vtt 8.0 KB
21 - YOLO Object Detection (Theory)/007 YOLO Theory Part 7.vtt 7.9 KB
33 - Transformers/011 Masked MultiHead Attention.vtt 7.9 KB
14 - Convolutional Neural Networks/006 More on Convolutions.vtt 7.9 KB
33 - Transformers/017 Learning Rate Warmup.vtt 7.8 KB
07 - Weight Initialization/001 Normal Distribution.vtt 7.8 KB
21 - YOLO Object Detection (Theory)/004 YOLO Theory Part 4.vtt 7.8 KB
33 - Transformers/003 Input Embeddings.vtt 7.7 KB
33 - Transformers/008 Residual Learning.vtt 7.7 KB
34 - Build a Chatbot with Transformers/018 Defining the Model.vtt 7.6 KB
18 - Transposed Convolutions/003 Transposed Convolutions.vtt 7.5 KB
05 - Optimization/001 Batch Gradient Descent.vtt 7.5 KB
03 - Activation Functions/002 Sigmoid Activation.vtt 7.5 KB
02 - Loss Functions/003 Huber Loss.vtt 7.5 KB
12 - Implementing a Neural Network from Scratch with Numpy/009 Initializing the Network.vtt 7.5 KB
01 - How Neural Networks and Backpropagation Works/003 The Rise of Deep Learning.vtt 7.4 KB
24 - Neural Style Transfer/002 NST Theory Part 2.vtt 7.2 KB
05 - Optimization/006 Bias Correction in Exponentially Weighted Averages.vtt 7.2 KB
04 - Regularization and Normalization/008 Group Normalization.vtt 7.2 KB
33 - Transformers/014 KL Divergence Loss.vtt 7.1 KB
27 - Word Embeddings/005 Word Embeddings in PyTorch.vtt 7.0 KB
13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/005 Creating the network class and the network functions.vtt 7.0 KB
31 - Practical Sequence Modelling in PyTorch Chatbot Application/002 Introduction.vtt 7.0 KB
31 - Practical Sequence Modelling in PyTorch Chatbot Application/003 Understanding the Encoder.vtt 7.0 KB
05 - Optimization/007 Momentum.vtt 7.0 KB
15 - Practical Convolutional Networks in PyTorch - Image Classification/005 Understanding the Propagation.vtt 7.0 KB
03 - Activation Functions/008 Mish Activation.vtt 6.8 KB
29 - Saving and Loading Models/003 Saving and Loading Part 3.vtt 6.8 KB
28 - Practical Recurrent Networks in PyTorch/002 Creating the Dictionary.vtt 6.8 KB
21 - YOLO Object Detection (Theory)/011 YOLO Theory Part 11.vtt 6.7 KB
06 - Hyperparameter Tuning and Learning Rate Scheduling/004 Cosine Annealing with Warm Restarts.vtt 6.5 KB
37 - BERT/002 Masked Language Modelling.vtt 6.4 KB
30 - Sequence Modelling/003 Attention Mechanisms.vtt 6.4 KB
11 - Visualize the Learning Process/004 Visualize Learning Part 4.vtt 6.4 KB
21 - YOLO Object Detection (Theory)/008 YOLO Theory Part 8.vtt 6.4 KB
05 - Optimization/004 Exponentially Weighted Average Intuition.vtt 6.2 KB
12 - Implementing a Neural Network from Scratch with Numpy/005 Prediction.vtt 6.2 KB
14 - Convolutional Neural Networks/011 Important formulas.vtt 6.2 KB
34 - Build a Chatbot with Transformers/014 Decoder Layer.vtt 6.2 KB
11 - Visualize the Learning Process/007 Neural Networks Playground.vtt 6.1 KB
19 - Transfer Learning in PyTorch - Image Classification/005 Finetuning the Network.vtt 6.1 KB
26 - Recurrent Neural Networks/001 Why do we need RNNs.vtt 6.1 KB
21 - YOLO Object Detection (Theory)/001 YOLO Theory Part 1.vtt 6.1 KB
06 - Hyperparameter Tuning and Learning Rate Scheduling/001 Introduction to Hyperparameter Tuning and Learning Rate Recap.vtt 6.1 KB
30 - Sequence Modelling/002 Image Captioning.vtt 6.0 KB
05 - Optimization/002 Stochastic Gradient Descent.vtt 5.9 KB
22 - Autoencoders and Variational Autoencoders/003 The Problem in Autoencoders.vtt 5.9 KB
31 - Practical Sequence Modelling in PyTorch Chatbot Application/009 Teacher Forcing.vtt 5.9 KB
04 - Regularization and Normalization/001 Overfitting.vtt 5.8 KB
26 - Recurrent Neural Networks/010 CNN-LSTM.vtt 5.8 KB
15 - Practical Convolutional Networks in PyTorch - Image Classification/009 Predicting an image.vtt 5.8 KB
14 - Convolutional Neural Networks/001 Prerequisite Filters.vtt 5.7 KB
15 - Practical Convolutional Networks in PyTorch - Image Classification/008 Plotting and Putting into Action.vtt 5.7 KB
04 - Regularization and Normalization/005 Normalization.vtt 5.5 KB
33 - Transformers/015 Label Smoothing.vtt 5.4 KB
14 - Convolutional Neural Networks/008 A Tool for Convolution Visualization.vtt 5.4 KB
05 - Optimization/012 Decoupling Weight Decay.vtt 5.2 KB
34 - Build a Chatbot with Transformers/005 Dataset Preprocessing Part 4.vtt 5.2 KB
13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/007 Testing the Network.vtt 5.1 KB
15 - Practical Convolutional Networks in PyTorch - Image Classification/004 Defining the Model.vtt 5.0 KB
12 - Implementing a Neural Network from Scratch with Numpy/010 Training the Model.vtt 4.8 KB
21 - YOLO Object Detection (Theory)/009 YOLO Theory Part 9.vtt 4.7 KB
26 - Recurrent Neural Networks/008 Bidirectional RNNs.vtt 4.7 KB
03 - Activation Functions/007 Swish Activation.vtt 4.7 KB
03 - Activation Functions/001 Why we need activation functions.vtt 4.7 KB
26 - Recurrent Neural Networks/003 Quiz Solution Discussion.vtt 4.6 KB
25 - Practical Neural Style Transfer in PyTorch/005 Fast Neural Style Transfer.vtt 4.6 KB
07 - Weight Initialization/004 He Norm Initialization.vtt 4.5 KB
03 - Activation Functions/005 Exponentially Linear Units (ELU).vtt 4.4 KB
14 - Convolutional Neural Networks/013 Regularization and Batch Normalization in CNNs.vtt 4.3 KB
16 - CNN Architectures/006 CNN Architectures Part 2.vtt 4.2 KB
14 - Convolutional Neural Networks/007 Quiz Solution Discussion.vtt 4.1 KB
14 - Convolutional Neural Networks/005 Convolution over Volume Animation.vtt 4.1 KB
34 - Build a Chatbot with Transformers/012 Feed Forward Implementation.vtt 4.0 KB
27 - Word Embeddings/002 Visualizing Word Embeddings.vtt 3.9 KB
33 - Transformers/010 Feed Forward.vtt 3.9 KB
27 - Word Embeddings/004 Word Embeddings Models.vtt 3.8 KB
03 - Activation Functions/003 Tanh Activation.vtt 3.8 KB
06 - Hyperparameter Tuning and Learning Rate Scheduling/005 Batch Size vs Learning Rate.vtt 3.7 KB
33 - Transformers/007 Concat and Linear.vtt 3.7 KB
03 - Activation Functions/006 Gated Linear Units (GLU).vtt 3.6 KB
34 - Build a Chatbot with Transformers/022 Action.vtt 3.6 KB
32 - Practical Sequence Modelling in PyTorch Image Captioning/014 Results.vtt 3.3 KB
26 - Recurrent Neural Networks/005 Stacked RNNs.vtt 3.2 KB
33 - Transformers/012 MultiHead Attention in Decoder.vtt 3.2 KB
05 - Optimization/003 Mini-Batch Gradient Descent.vtt 3.1 KB
32 - Practical Sequence Modelling in PyTorch Image Captioning/013 Training.vtt 3.0 KB
21 - YOLO Object Detection (Theory)/010 YOLO Theory Part 10.vtt 2.6 KB
14 - Convolutional Neural Networks/010 CNN Visualization.vtt 2.5 KB
13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/002 Code Details.vtt 2.4 KB
27 - Word Embeddings/003 Measuring Word Embeddings.vtt 2.3 KB
11 - Visualize the Learning Process/002 Visualize Learning Part 2.vtt 2.2 KB
04 - Regularization and Normalization/004 DropConnect.vtt 2.0 KB
05 - Optimization/010 SWATS - Switching from Adam to SGD.vtt 1.9 KB
21 - YOLO Object Detection (Theory)/013 YOLO Code Note.html 1.4 KB
08 - Introduction to PyTorch/001 CODE FOR THIS COURSE.vtt 636 bytes
01 - How Neural Networks and Backpropagation Works/001 BEFORE STARTING...PLEASE READ THIS.html 630 bytes
12 - Implementing a Neural Network from Scratch with Numpy/006 Notebook for the following Lecture.html 532 bytes
13 - Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/001 The MNIST Dataset.html 470 bytes
02 - Loss Functions/007 Softmax with Temperature Controlling your distribution.html 394 bytes
01 - How Neural Networks and Backpropagation Works/008 Before Proceeding with the Backpropagation.html 341 bytes
10 - Practical Neural Networks in PyTorch - Application 1 Diabetes/001 Download the Dataset.html 322 bytes
14 - Convolutional Neural Networks/004 Convolution over Volume Animation Resource.html 321 bytes
28 - Practical Recurrent Networks in PyTorch/001 Download the Dataset.html 312 bytes
33 - Transformers/001 SANITY CHECK ON PREVIOUS SECTIONS.html 272 bytes
34 - Build a Chatbot with Transformers/001 CODE.html 268 bytes
31 - Practical Sequence Modelling in PyTorch Chatbot Application/001 Download the Dataset.html 252 bytes
07 - Weight Initialization/005 Practical Weight Initialization Note.html 186 bytes
02 - Loss Functions/012 Practical Loss Functions Note.html 179 bytes
39 - GPT/007 Implementation.html 128 bytes
16 - CNN Architectures/004 Note on Residual Networks Implementation.html 109 bytes
19 - Transfer Learning in PyTorch - Image Classification/external-assets-links.txt 71 bytes

Udemy - The Complete Neural Networks Bootcamp Theory, Applications (11.2021)

File List

Download Torrent

Related Resources

Copyright Infringement