Tutorialsplanet NET Udemy The Complete Neural Networks Bootcamp Theory Applications

Tutorialsplanet NET Udemy The Complete Neural Networks Bootcamp Theory Applications

Name	[Tutorialsplanet.NET] Udemy - The Complete Neural Networks Bootcamp Theory, Applications	DOWNLOAD Copy Link
Total Size	20.2 GB
Total Files	584
Last Seen	2024-11-20 00:09
Hash	43F148D4AC20CC5BF65B0EFEAC89FFD575208065

/1. How Neural Networks and Backpropagation Works/
1. What Can Deep Learning Do-en_US.srt	18.7 KB
1. What Can Deep Learning Do.mp4	163.8 MB
2. The Rise of Deep Learning-en_US.srt	8.3 KB
2. The Rise of Deep Learning.mp4	43.8 MB
3. The Essence of Neural Networks-en_US.srt	13.1 KB
3. The Essence of Neural Networks.mp4	52.4 MB
4. The Perceptron-en_US.srt	21.7 KB
4. The Perceptron.mp4	116.3 MB
5. Gradient Descent-en_US.srt	15.6 KB
5. Gradient Descent.mp4	42.6 MB
6. The Forward Propagation-en_US.srt	14.1 KB
6. The Forward Propagation.mp4	54.8 MB
7. Backpropagation Part 1-en_US.srt	14.5 KB
7. Backpropagation Part 1.mp4	30.8 MB
8. Backpropagation Part 2-en_US.srt	12.3 KB
8. Backpropagation Part 2.mp4	29.2 MB
BEFORE STARTING...PLEASE READ THIS.html	0.6 KB
Before Proceeding with the Backpropagation.html	0.3 KB
/10. Visualize the Learning Process/
1. Visualize Learning Part 1-en_US.srt	12.3 KB
1. Visualize Learning Part 1.mp4	25.6 MB
2. Visualize Learning Part 2-en_US.srt	2.6 KB
2. Visualize Learning Part 2.mp4	12.8 MB
3. Visualize Learning Part 3-en_US.srt	10.6 KB
3. Visualize Learning Part 3.mp4	28.7 MB
4. Visualize Learning Part 4-en_US.srt	7.2 KB
4. Visualize Learning Part 4.mp4	21.1 MB
5. Visualize Learning Part 5-en_US.srt	14.5 KB
5. Visualize Learning Part 5.mp4	75.1 MB
6. Visualize Learning Part 6-en_US.srt	10.2 KB
6. Visualize Learning Part 6.mp4	67.5 MB
7. Neural Networks Playground-en_US.srt	6.9 KB
7. Neural Networks Playground.mp4	34.1 MB
/11. Implementing a Neural Network from Scratch with Numpy/
1. The Dataset and Hyperparameters-en_US.srt	16.0 KB
1. The Dataset and Hyperparameters.mp4	74.0 MB
2. Understanding the Implementation-en_US.srt	11.2 KB
2. Understanding the Implementation.mp4	24.5 MB
3. Forward Propagation-en_US.srt	15.7 KB
3. Forward Propagation.mp4	89.3 MB
4. Loss Function-en_US.srt	21.6 KB
4. Loss Function.mp4	71.8 MB
5. Prediction-en_US.srt	7.1 KB
5. Prediction.mp4	29.1 MB
6. Backpropagation Equations-en_US.srt	16.3 KB
6. Backpropagation Equations.mp4	103.6 MB
7. Backpropagation-en_US.srt	28.2 KB
7. Backpropagation.mp4	155.3 MB
8. Initializing the Network-en_US.srt	8.5 KB
8. Initializing the Network.mp4	61.8 MB
9. Training the Model-en_US.srt	5.4 KB
9. Training the Model.mp4	49.5 MB
Notebook for the following Lecture.html	0.5 KB
/12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/
1. Code Details-en_US.srt	2.7 KB
1. Code Details.mp4	33.5 MB
2. Importing and Defining Parameters-en_US.srt	16.3 KB
2. Importing and Defining Parameters.mp4	149.1 MB
3. Defining the Network Class-en_US.srt	12.3 KB
3. Defining the Network Class.mp4	90.1 MB
4. Creating the network class and the network functions-en_US.srt	0.0 KB
4. Creating the network class and the network functions.mp4	58.9 MB
5. Training the Network-en_US.srt	33.3 KB
5. Training the Network.mp4	349.4 MB
6. Testing the Network-en_US.srt	5.8 KB
6. Testing the Network.mp4	49.4 MB
The MNIST Dataset.html	0.4 KB
/13. Convolutional Neural Networks/
1. Prerequisite Filters-en_US.srt	6.5 KB
1. Prerequisite Filters.mp4	38.2 MB
10. Important formulas-en_US.srt	7.0 KB
10. Important formulas.mp4	14.0 MB
11. CNN Characteristics-en_US.srt	11.0 KB
11. CNN Characteristics.mp4	48.1 MB
12. Regularization and Batch Normalization in CNNs-en_US.srt	4.8 KB
12. Regularization and Batch Normalization in CNNs.mp4	19.1 MB
13. DropBlock Dropout in CNNs-en_US.srt	15.8 KB
13. DropBlock Dropout in CNNs.mp4	104.3 MB
14. Softmax with Temperature-en_US.srt	12.9 KB
14. Softmax with Temperature.mp4	28.7 MB
2. Introduction to Convolutional Networks and the need for them-en_US.srt	9.4 KB
2. Introduction to Convolutional Networks and the need for them.mp4	26.3 MB
3. Filters and Features-en_US.srt	12.5 KB
3. Filters and Features.mp4	54.5 MB
4. Convolution over Volume Animation-en_US.srt	4.7 KB
4. Convolution over Volume Animation.mp4	22.3 MB
5. More on Convolutions-en_US.srt	8.9 KB
5. More on Convolutions.mp4	31.4 MB
6. Quiz Solution Discussion-en_US.srt	4.6 KB
6. Quiz Solution Discussion.mp4	6.2 MB
7. A Tool for Convolution Visualization-en_US.srt	6.1 KB
7. A Tool for Convolution Visualization.mp4	29.3 MB
8. Activation, Pooling and FC-en_US.srt	17.3 KB
8. Activation, Pooling and FC.mp4	84.6 MB
9. CNN Visualization-en_US.srt	2.8 KB
9. CNN Visualization.mp4	16.2 MB
Convolution over Volume Animation Resource.html	0.3 KB
/14. Practical Convolutional Networks in PyTorch - Image Classification/
1. Loading and Normalizing the Dataset-en_US.srt	16.3 KB
1. Loading and Normalizing the Dataset.mp4	55.1 MB
10. Classifying your own Handwritten images-en_US.srt	15.7 KB
10. Classifying your own Handwritten images.mp4	58.4 MB
2. Visualizing and Loading the Dataset-en_US.srt	12.7 KB
2. Visualizing and Loading the Dataset.mp4	63.7 MB
3. Building the CNN-en_US.srt	32.6 KB
3. Building the CNN.mp4	263.6 MB
4. Defining the Model-en_US.srt	5.7 KB
4. Defining the Model.mp4	19.6 MB
5. Understanding the Propagation-en_US.srt	7.8 KB
5. Understanding the Propagation.mp4	27.5 MB
6. Training the CNN-en_US.srt	21.3 KB
6. Training the CNN.mp4	137.4 MB
7. Testing the CNN-en_US.srt	9.1 KB
7. Testing the CNN.mp4	37.6 MB
8. Plotting and Putting into Action-en_US.srt	6.5 KB
8. Plotting and Putting into Action.mp4	47.5 MB
9. Predicting an image-en_US.srt	6.5 KB
9. Predicting an image.mp4	18.3 MB
/15. CNN Architectures/
1. CNN Architectures Part 1-en_US.srt	15.6 KB
1. CNN Architectures Part 1.mp4	46.0 MB
2. Residual Networks Part 1-en_US.srt	14.5 KB
2. Residual Networks Part 1.mp4	128.2 MB
3. Residual Networks Part 2-en_US.srt	23.6 KB
3. Residual Networks Part 2.mp4	158.7 MB
4. CNN Architectures Part 2-en_US.srt	4.7 KB
4. CNN Architectures Part 2.mp4	14.0 MB
5. Densely Connected Networks-en_US.srt	18.3 KB
5. Densely Connected Networks.mp4	99.8 MB
6. Squeeze-Excite Networks-en_US.srt	13.5 KB
6. Squeeze-Excite Networks.mp4	41.5 MB
7. Seperable Convolutions-en_US.srt	15.2 KB
7. Seperable Convolutions.mp4	63.4 MB
8. Transfer Learning-en_US.srt	11.8 KB
8. Transfer Learning.mp4	30.7 MB
Note on Residual Networks Implementation.html	0.1 KB
/16. Practical Residual Networks in PyTorch/
1. Practical ResNet Part 1-en_US.srt	16.3 KB
1. Practical ResNet Part 1.mp4	75.0 MB
2. Practical ResNet Part 2-en_US.srt	16.4 KB
2. Practical ResNet Part 2.mp4	89.9 MB
3. Practical ResNet Part 3-en_US.srt	15.9 KB
3. Practical ResNet Part 3.mp4	108.2 MB
4. Practical ResNet Part 4-en_US.srt	17.4 KB
4. Practical ResNet Part 4.mp4	150.2 MB
/17. Transposed Convolutions/
1. Introduction to Transposed Convolutions-en_US.srt	9.2 KB
1. Introduction to Transposed Convolutions.mp4	32.5 MB
2. Convolution Operation as Matrix Multiplication-en_US.srt	11.5 KB
2. Convolution Operation as Matrix Multiplication.mp4	74.4 MB
3. Transposed Convolutions-en_US.srt	8.5 KB
3. Transposed Convolutions.mp4	37.8 MB
/18. Transfer Learning in PyTorch - Image Classification/
1. Data Augmentation-en_US.srt	15.8 KB
1. Data Augmentation.mp4	235.5 MB
2. External URLs.txt	0.1 KB
2. Loading the Dataset-en_US.srt	14.4 KB
2. Loading the Dataset.mp4	186.0 MB
3. Modifying the Network-en_US.srt	11.0 KB
3. Modifying the Network.mp4	101.7 MB
4. Understanding the data-en_US.srt	14.9 KB
4. Understanding the data.mp4	106.7 MB
5. Finetuning the Network-en_US.srt	7.0 KB
5. Finetuning the Network.mp4	52.5 MB
6. Testing and Visualizing the results-en_US.srt	13.3 KB
6. Testing and Visualizing the results.mp4	124.2 MB
[Tutorialsplanet.NET].url	0.1 KB
/19. Convolutional Networks Visualization/
1. Data and the Model-en_US.srt	10.3 KB
1. Data and the Model.mp4	78.0 MB
2. Processing the Model-en_US.srt	18.0 KB
2. Processing the Model.mp4	149.4 MB
3. Visualizing the Feature Maps-en_US.srt	16.8 KB
3. Visualizing the Feature Maps.mp4	139.7 MB
dog.jpg	95.5 KB
imagenet-class-index.json	35.4 KB
/2. Loss Functions/
1. Mean Squared Error (MSE)-en_US.srt	9.4 KB
1. Mean Squared Error (MSE).mp4	20.8 MB
10. Triplet Ranking Loss-en_US.srt	16.8 KB
10. Triplet Ranking Loss.mp4	131.8 MB
2. L1 Loss (MAE)-en_US.srt	11.2 KB
2. L1 Loss (MAE).mp4	81.0 MB
3. Huber Loss-en_US.srt	8.4 KB
3. Huber Loss.mp4	30.0 MB
4. Binary Cross Entropy Loss-en_US.srt	17.6 KB
4. Binary Cross Entropy Loss.mp4	47.1 MB
5. Cross Entropy Loss-en_US.srt	11.0 KB
5. Cross Entropy Loss.mp4	25.9 MB
6. Softmax Function-en_US.srt	10.1 KB
6. Softmax Function.mp4	46.9 MB
7. KL divergence Loss-en_US.srt	9.8 KB
7. KL divergence Loss.mp4	26.6 MB
8. Contrastive Loss-en_US.srt	16.3 KB
8. Contrastive Loss.mp4	65.7 MB
9. Hinge Loss-en_US.srt	16.9 KB
9. Hinge Loss.mp4	70.7 MB
Practical Loss Functions Note.html	0.2 KB
Softmax with Temperature Controlling your distribution.html	0.4 KB
[Tutorialsplanet.NET].url	0.1 KB
/20. YOLO Object Detection (Theory)/
1. YOLO Theory Part 1-en_US.srt	6.9 KB
1. YOLO Theory Part 1.mp4	140.3 MB
10. YOLO Theory Part 10-en_US.srt	2.9 KB
10. YOLO Theory Part 10.mp4	26.5 MB
11. YOLO Theory Part 11-en_US.srt	7.6 KB
11. YOLO Theory Part 11.mp4	55.4 MB
12. YOLO Theory Part 12-en_US.srt	13.6 KB
12. YOLO Theory Part 12.mp4	61.1 MB
2. YOLO Theory Part 2-en_US.srt	16.5 KB
2. YOLO Theory Part 2.mp4	84.6 MB
3. YOLO Theory Part 3-en_US.srt	12.6 KB
3. YOLO Theory Part 3.mp4	129.9 MB
4. YOLO Theory Part 4-en_US.srt	8.9 KB
4. YOLO Theory Part 4.mp4	27.0 MB
5. YOLO Theory Part 5-en_US.srt	10.5 KB
5. YOLO Theory Part 5.mp4	110.1 MB
6. YOLO Theory Part 6-en_US.srt	12.5 KB
6. YOLO Theory Part 6.mp4	129.8 MB
7. YOLO Theory Part 7-en_US.srt	9.0 KB
7. YOLO Theory Part 7.mp4	73.1 MB
8. YOLO Theory Part 8-en_US.srt	7.2 KB
8. YOLO Theory Part 8.mp4	80.9 MB
9. YOLO Theory Part 9-en_US.srt	5.4 KB
9. YOLO Theory Part 9.mp4	18.6 MB
YOLO Code Note.html	1.4 KB
/21. Autoencoders and Variational Autoencoders/
1. Autoencoders-en_US.srt	12.1 KB
1. Autoencoders.mp4	44.1 MB
2. Denoising Autoencoders-en_US.srt	9.5 KB
2. Denoising Autoencoders.mp4	31.5 MB
3. The Problem in Autoencoders-en_US.srt	6.6 KB
3. The Problem in Autoencoders.mp4	14.1 MB
4. Variational Autoencoders-en_US.srt	14.2 KB
4. Variational Autoencoders.mp4	73.6 MB
5. Probability Distributions Recap-en_US.srt	43.5 KB
5. Probability Distributions Recap.mp4	271.9 MB
6. Loss Function Derivation for VAE-en_US.srt	38.2 KB
6. Loss Function Derivation for VAE.mp4	334.7 MB
7. Deep Fake-en_US.srt	10.3 KB
7. Deep Fake.mp4	89.4 MB
/22. Practical Variational Autoencoders in PyTorch/
1. Practical VAE Part 1-en_US.srt	26.1 KB
1. Practical VAE Part 1.mp4	106.1 MB
2. Practical VAE Part 2-en_US.srt	15.0 KB
2. Practical VAE Part 2.mp4	108.8 MB
3. Practical VAE Part 3-en_US.srt	15.7 KB
3. Practical VAE Part 3.mp4	97.7 MB
/23. Neural Style Transfer/
1. NST Theory Part 1-en_US.srt	9.4 KB
1. NST Theory Part 1.mp4	55.1 MB
2. NST Theory Part 2-en_US.srt	8.1 KB
2. NST Theory Part 2.mp4	36.9 MB
3. NST Theory Part 3-en_US.srt	13.8 KB
3. NST Theory Part 3.mp4	72.5 MB
/24. Practical Neural Style Transfer in PyTorch/
1. NST Practical Part 1-en_US.srt	14.3 KB
1. NST Practical Part 1.mp4	66.9 MB
2. NST Practical Part 2-en_US.srt	12.8 KB
2. NST Practical Part 2.mp4	134.1 MB
3. NST Practical Part 3-en_US.srt	14.9 KB
3. NST Practical Part 3.mp4	111.0 MB
4. NST Practical Part 4-en_US.srt	18.9 KB
4. NST Practical Part 4.mp4	137.3 MB
5. Fast Neural Style Transfer-en_US.srt	5.3 KB
5. Fast Neural Style Transfer.mp4	47.0 MB
/25. Recurrent Neural Networks/
1. Why do we need RNNs-en_US.srt	6.8 KB
1. Why do we need RNNs.mp4	19.5 MB
10. CNN-LSTM-en_US.srt	6.5 KB
10. CNN-LSTM.mp4	22.5 MB
2. Vanilla RNNs-en_US.srt	11.1 KB
2. Vanilla RNNs.mp4	54.1 MB
3. Quiz Solution Discussion-en_US.srt	5.2 KB
3. Quiz Solution Discussion.mp4	16.1 MB
4. Backpropagation Through Time-en_US.srt	16.9 KB
4. Backpropagation Through Time.mp4	64.6 MB
5. Stacked RNNs-en_US.srt	3.6 KB
5. Stacked RNNs.mp4	8.1 MB
6. Vanishing and Exploding Gradient Problem-en_US.srt	13.9 KB
6. Vanishing and Exploding Gradient Problem.mp4	70.1 MB
7. LSTMs-en_US.srt	29.1 KB
7. LSTMs.mp4	117.1 MB
8. Bidirectional RNNs-en_US.srt	5.3 KB
8. Bidirectional RNNs.mp4	15.8 MB
9. GRUs-en_US.srt	9.1 KB
9. GRUs.mp4	27.4 MB
[Tutorialsplanet.NET].url	0.1 KB
/26. Word Embeddings/
1. What are Word Embeddings-en_US.srt	12.5 KB
1. What are Word Embeddings.mp4	76.2 MB
2. Visualizing Word Embeddings-en_US.srt	4.4 KB
2. Visualizing Word Embeddings.mp4	12.8 MB
3. Measuring Word Embeddings-en_US.srt	2.6 KB
3. Measuring Word Embeddings.mp4	5.8 MB
4. Word Embeddings Models-en_US.srt	4.3 KB
4. Word Embeddings Models.mp4	11.2 MB
5. Word Embeddings in PyTorch-en_US.srt	8.0 KB
5. Word Embeddings in PyTorch.mp4	55.8 MB
/27. Practical Recurrent Networks in PyTorch/
1. Creating the Dictionary-en_US.srt	7.7 KB
1. Creating the Dictionary.mp4	62.8 MB
2. Processing the Text-en_US.srt	13.7 KB
2. Processing the Text.mp4	113.9 MB
3. Defining and Visualizing the Parameters-en_US.srt	9.8 KB
3. Defining and Visualizing the Parameters.mp4	72.9 MB
4. Creating the Network-en_US.srt	14.5 KB
4. Creating the Network.mp4	117.5 MB
5. Training the Network-en_US.srt	13.6 KB
5. Training the Network.mp4	159.0 MB
6. Generating Text-en_US.srt	16.7 KB
6. Generating Text.mp4	186.5 MB
Download the Dataset.html	0.3 KB
/28. Saving and Loading Models/
1. Saving and Loading Part 1-en_US.srt	19.7 KB
1. Saving and Loading Part 1.mp4	137.0 MB
2. Saving and Loading Part 2-en_US.srt	10.3 KB
2. Saving and Loading Part 2.mp4	101.3 MB
3. Saving and Loading Part 3-en_US.srt	7.7 KB
3. Saving and Loading Part 3.mp4	55.4 MB
/29. Sequence Modelling/
1. Sequence Modeling-en_US.srt	17.7 KB
1. Sequence Modeling.mp4	85.5 MB
2. Image Captioning-en_US.srt	6.8 KB
2. Image Captioning.mp4	36.4 MB
3. Attention Mechanisms-en_US.srt	7.2 KB
3. Attention Mechanisms.mp4	17.3 MB
4. How Attention Mechanisms Work-en_US.srt	15.0 KB
4. How Attention Mechanisms Work.mp4	42.1 MB
/3. Activation Functions/
1. Why we need activation functions-en_US.srt	5.2 KB
1. Why we need activation functions.mp4	23.5 MB
2. Sigmoid Activation-en_US.srt	8.4 KB
2. Sigmoid Activation.mp4	21.1 MB
3. Tanh Activation-en_US.srt	4.2 KB
3. Tanh Activation.mp4	14.5 MB
4. ReLU and PReLU-en_US.srt	9.4 KB
4. ReLU and PReLU.mp4	21.8 MB
5. Exponentially Linear Units (ELU)-en_US.srt	5.0 KB
5. Exponentially Linear Units (ELU).mp4	11.2 MB
6. Gated Linear Units (GLU)-en_US.srt	4.1 KB
6. Gated Linear Units (GLU).mp4	27.8 MB
7. Swish Activation-en_US.srt	5.3 KB
7. Swish Activation.mp4	13.5 MB
8. Mish Activation-en_US.srt	7.7 KB
8. Mish Activation.mp4	40.0 MB
/30. Practical Sequence Modelling in PyTorch Chatbot Application/
1. Introduction-en_US.srt	8.0 KB
1. Introduction.mp4	78.1 MB
2. Understanding the Encoder-en_US.srt	7.9 KB
2. Understanding the Encoder.mp4	97.2 MB
3. Defining the Encoder-en_US.srt	31.7 KB
3. Defining the Encoder.mp4	424.0 MB
4. Understanding Pack Padded Sequence-en_US.srt	10.0 KB
4. Understanding Pack Padded Sequence.mp4	30.6 MB
5. Designing the Attention Model-en_US.srt	20.9 KB
5. Designing the Attention Model.mp4	272.9 MB
6. Designing the Decoder Part 1-en_US.srt	18.5 KB
6. Designing the Decoder Part 1.mp4	146.1 MB
7. Designing the Decoder Part 2-en_US.srt	23.1 KB
7. Designing the Decoder Part 2.mp4	184.7 MB
8. Teacher Forcing-en_US.srt	6.6 KB
8. Teacher Forcing.mp4	22.8 MB
Download the Dataset.html	0.3 KB
/31. Practical Sequence Modelling in PyTorch Image Captioning/
1. Implementation Details-en_US.srt	16.2 KB
1. Implementation Details.mp4	52.8 MB
10. Train Function-en_US.srt	21.0 KB
10. Train Function.mp4	166.6 MB
11. Defining Hyperparameters-en_US.srt	19.0 KB
11. Defining Hyperparameters.mp4	109.9 MB
12. Evaluation Function-en_US.srt	22.2 KB
12. Evaluation Function.mp4	95.0 MB
13. Training-en_US.srt	3.4 KB
13. Training.mp4	13.5 MB
14. Results-en_US.srt	3.8 KB
14. Results.mp4	35.5 MB
2. Utility Functions-en_US.srt	18.6 KB
2. Utility Functions.mp4	43.4 MB
3. Accuracy Calculation-en_US.srt	14.0 KB
3. Accuracy Calculation.mp4	77.7 MB
4. Constructing the Dataset Part 1-en_US.srt	18.7 KB
4. Constructing the Dataset Part 1.mp4	142.7 MB
5. Constructing the Dataset Part 2-en_US.srt	15.9 KB
5. Constructing the Dataset Part 2.mp4	59.7 MB
6. Creating the Encoder-en_US.srt	23.4 KB
6. Creating the Encoder.mp4	89.0 MB
7. Creating the Decoder Part 1-en_US.srt	23.0 KB
7. Creating the Decoder Part 1.mp4	123.9 MB
8. Creating the Decoder Part 2-en_US.srt	15.1 KB
8. Creating the Decoder Part 2.mp4	102.2 MB
9. Creating the Decoder Part 3-en_US.srt	17.5 KB
9. Creating the Decoder Part 3.mp4	137.4 MB
/32. Transformers/
1. Introduction to Transformers-en_US.srt	16.2 KB
1. Introduction to Transformers.mp4	49.0 MB
10. Masked MultiHead Attention-en_US.srt	8.9 KB
10. Masked MultiHead Attention.mp4	28.0 MB
11. MultiHead Attention in Decoder-en_US.srt	3.5 KB
11. MultiHead Attention in Decoder.mp4	11.6 MB
12. Cross Entropy Loss-en_US.srt	16.5 KB
12. Cross Entropy Loss.mp4	34.3 MB
13. KL Divergence Loss-en_US.srt	7.9 KB
13. KL Divergence Loss.mp4	24.7 MB
14. Label Smoothing-en_US.srt	6.1 KB
14. Label Smoothing.mp4	13.9 MB
15. Dropout-en_US.srt	12.3 KB
15. Dropout.mp4	78.9 MB
16. Learning Rate Warmup-en_US.srt	8.8 KB
16. Learning Rate Warmup.mp4	30.5 MB
2. Input Embeddings-en_US.srt	8.7 KB
2. Input Embeddings.mp4	69.0 MB
3. Positional Encoding-en_US.srt	18.5 KB
3. Positional Encoding.mp4	100.6 MB
4. MultiHead Attention Part 1-en_US.srt	13.3 KB
4. MultiHead Attention Part 1.mp4	61.2 MB
5. MultiHead Attention Part 2-en_US.srt	10.7 KB
5. MultiHead Attention Part 2.mp4	48.1 MB
6. Concat and Linear-en_US.srt	4.1 KB
6. Concat and Linear.mp4	10.2 MB
7. Residual Learning-en_US.srt	8.6 KB
7. Residual Learning.mp4	29.4 MB
8. Layer Normalization-en_US.srt	9.6 KB
8. Layer Normalization.mp4	22.8 MB
9. Feed Forward-en_US.srt	4.4 KB
9. Feed Forward.mp4	16.3 MB
SANITY CHECK ON PREVIOUS SECTIONS.html	0.3 KB
[Tutorialsplanet.NET].url	0.1 KB
/33. Build a Chatbot with Transformers/
1. Dataset Preprocessing Part 1-en_US.srt	13.5 KB
1. Dataset Preprocessing Part 1.mp4	87.4 MB
10. MultiHead Attention Implementation Part 3-en_US.srt	16.5 KB
10. MultiHead Attention Implementation Part 3.mp4	129.5 MB
11. Feed Forward Implementation-en_US.srt	4.5 KB
11. Feed Forward Implementation.mp4	45.0 MB
12. Encoder Layer-en_US.srt	10.1 KB
12. Encoder Layer.mp4	90.9 MB
13. Decoder Layer-en_US.srt	6.9 KB
13. Decoder Layer.mp4	65.3 MB
14. Transformer-en_US.srt	15.1 KB
14. Transformer.mp4	122.8 MB
15. AdamWarmup-en_US.srt	9.0 KB
15. AdamWarmup.mp4	78.9 MB
16. Loss with Label Smoothing-en_US.srt	25.4 KB
16. Loss with Label Smoothing.mp4	225.1 MB
17. Defining the Model-en_US.srt	8.6 KB
17. Defining the Model.mp4	45.8 MB
18. Training Function-en_US.srt	14.2 KB
18. Training Function.mp4	105.4 MB
19. Evaluation Function-en_US.srt	21.1 KB
19. Evaluation Function.mp4	115.1 MB
2. Dataset Preprocessing Part 2-en_US.srt	20.4 KB
2. Dataset Preprocessing Part 2.mp4	141.2 MB
20. Main Function and User Evaluation-en_US.srt	12.8 KB
20. Main Function and User Evaluation.mp4	97.8 MB
21. Action-en_US.srt	4.0 KB
21. Action.mp4	33.8 MB
3. Dataset Preprocessing Part 3-en_US.srt	14.2 KB
3. Dataset Preprocessing Part 3.mp4	83.9 MB
4. Dataset Preprocessing Part 4-en_US.srt	5.8 KB
4. Dataset Preprocessing Part 4.mp4	21.3 MB
5. Dataset Preprocessing Part 5-en_US.srt	12.9 KB
5. Dataset Preprocessing Part 5.mp4	96.9 MB
6. Data Loading and Masking-en_US.srt	17.2 KB
6. Data Loading and Masking.mp4	79.5 MB
7. Embeddings-en_US.srt	19.2 KB
7. Embeddings.mp4	85.2 MB
8. MultiHead Attention Implementation Part 1-en_US.srt	8.9 KB
8. MultiHead Attention Implementation Part 1.mp4	63.4 MB
9. MultiHead Attention Implementation Part 2-en_US.srt	10.6 KB
9. MultiHead Attention Implementation Part 2.mp4	53.9 MB
CODE.html	0.3 KB
SANITY CHECK ON PREVIOUS SECTIONS.html	0.3 KB
/34. Universal Transformers/
1. Universal Transformers-en_US.srt	9.5 KB
1. Universal Transformers.mp4	22.9 MB
2. Practical Universal Transformers Modifying the Transformers code-en_US.srt	17.7 KB
2. Practical Universal Transformers Modifying the Transformers code.mp4	168.9 MB
3. Transformers for other tasks-en_US.srt	11.4 KB
3. Transformers for other tasks.mp4	118.3 MB
SANITY CHECK ON PREVIOUS SECTIONS.html	0.3 KB
/35. Google Colab and Gradient Accumulation/
1. Running your models on Google Colab-en_US.srt	10.7 KB
1. Running your models on Google Colab.mp4	34.8 MB
2. Gradient Accumulation-en_US.srt	21.2 KB
2. Gradient Accumulation.mp4	59.6 MB
/36. BERT/
1. What is BERT and its structure-en_US.srt	11.5 KB
1. What is BERT and its structure.mp4	36.4 MB
2. Masked Language Modelling-en_US.srt	7.3 KB
2. Masked Language Modelling.mp4	24.2 MB
3. Next Sentence Prediction-en_US.srt	11.8 KB
3. Next Sentence Prediction.mp4	44.7 MB
4. Fine-tuning BERT-en_US.srt	9.4 KB
4. Fine-tuning BERT.mp4	53.1 MB
5. Exploring Transformers-en_US.srt	20.6 KB
5. Exploring Transformers.mp4	143.2 MB
/37. Vision Transformers/
1. Vision Transformer Part 1-en_US.srt	17.4 KB
1. Vision Transformer Part 1.mp4	89.4 MB
2. Vision Transformer Part 2-en_US.srt	12.1 KB
2. Vision Transformer Part 2.mp4	37.0 MB
3. Vision Transformer Part 3-en_US.srt	15.8 KB
3. Vision Transformer Part 3.mp4	111.6 MB
SANITY CHECK ON PREVIOUS SECTIONS.html	0.3 KB
/38. GPT/
1. GPT Part 1-en_US.srt	14.0 KB
1. GPT Part 1.mp4	93.2 MB
2. GPT Part 2-en_US.srt	12.5 KB
2. GPT Part 2.mp4	47.6 MB
3. Zero-Shot Predictions with GPT-en_US.srt	10.6 KB
3. Zero-Shot Predictions with GPT.mp4	45.5 MB
4. Byte-Pair Encoding-en_US.srt	10.7 KB
4. Byte-Pair Encoding.mp4	41.2 MB
5. Technical Details of GPT-en_US.srt	9.2 KB
5. Technical Details of GPT.mp4	53.9 MB
6. Playing with HuggingFace models-en_US.srt	10.0 KB
6. Playing with HuggingFace models.mp4	31.7 MB
Implementation.html	0.1 KB
/4. Regularization and Normalization/
1. Overfitting-en_US.srt	6.6 KB
1. Overfitting.mp4	27.5 MB
2. L1 and L2 Regularization-en_US.srt	12.1 KB
2. L1 and L2 Regularization.mp4	35.1 MB
3. Dropout-en_US.srt	12.3 KB
3. Dropout.mp4	78.9 MB
4. DropConnect-en_US.srt	2.3 KB
4. DropConnect.mp4	14.9 MB
5. Normalization-en_US.srt	6.2 KB
5. Normalization.mp4	14.2 MB
6. Batch Normalization-en_US.srt	16.3 KB
6. Batch Normalization.mp4	105.2 MB
7. Layer Normalization-en_US.srt	10.3 KB
7. Layer Normalization.mp4	47.7 MB
8. Group Normalization-en_US.srt	8.0 KB
8. Group Normalization.mp4	27.7 MB
DropBlock in CNNs.html	0.3 KB
Note on Weight Decay.html	0.4 KB
/5. Optimization/
1. Batch Gradient Descent-en_US.srt	8.5 KB
1. Batch Gradient Descent.mp4	51.8 MB
10. SWATS - Switching from Adam to SGD-en_US.srt	2.1 KB
10. SWATS - Switching from Adam to SGD.mp4	10.3 MB
11. Weight Decay-en_US.srt	9.5 KB
11. Weight Decay.mp4	79.3 MB
12. Decoupling Weight Decay-en_US.srt	5.9 KB
12. Decoupling Weight Decay.mp4	54.8 MB
13. AMSGrad-en_US.srt	11.9 KB
13. AMSGrad.mp4	89.8 MB
2. Stochastic Gradient Descent-en_US.srt	6.7 KB
2. Stochastic Gradient Descent.mp4	19.0 MB
3. Mini-Batch Gradient Descent-en_US.srt	3.5 KB
3. Mini-Batch Gradient Descent.mp4	7.3 MB
4. Exponentially Weighted Average Intuition-en_US.srt	7.0 KB
4. Exponentially Weighted Average Intuition.mp4	24.0 MB
5. Exponentially Weighted Average Implementation-en_US.srt	11.6 KB
5. Exponentially Weighted Average Implementation.mp4	45.2 MB
6. Bias Correction in Exponentially Weighted Averages-en_US.srt	8.1 KB
6. Bias Correction in Exponentially Weighted Averages.mp4	32.4 MB
7. Momentum-en_US.srt	7.8 KB
7. Momentum.mp4	28.7 MB
8. RMSProp-en_US.srt	15.5 KB
8. RMSProp.mp4	40.9 MB
9. Adam Optimization-en_US.srt	9.5 KB
9. Adam Optimization.mp4	81.5 MB
/6. Hyperparameter Tuning and Learning Rate Scheduling/
1. Introduction to Hyperparameter Tuning and Learning Rate Recap-en_US.srt	6.8 KB
1. Introduction to Hyperparameter Tuning and Learning Rate Recap.mp4	18.5 MB
2. Step Learning Rate Decay-en_US.srt	16.8 KB
2. Step Learning Rate Decay.mp4	65.9 MB
3. Cyclic Learning Rate-en_US.srt	13.3 KB
3. Cyclic Learning Rate.mp4	72.7 MB
4. Cosine Annealing with Warm Restarts-en_US.srt	7.3 KB
4. Cosine Annealing with Warm Restarts.mp4	36.9 MB
5. Batch Size vs Learning Rate-en_US.srt	4.2 KB
5. Batch Size vs Learning Rate.mp4	25.9 MB
/7. Weight Initialization/
1. Normal Distribution-en_US.srt	8.8 KB
1. Normal Distribution.mp4	19.6 MB
2. What happens when all weights are initialized to the same value-en_US.srt	13.0 KB
2. What happens when all weights are initialized to the same value.mp4	62.9 MB
3. Xavier Initialization-en_US.srt	12.9 KB
3. Xavier Initialization.mp4	115.0 MB
4. He Norm Initialization-en_US.srt	5.1 KB
4. He Norm Initialization.mp4	14.0 MB
Practical Weight Initialization Note.html	0.2 KB
/8. Introduction to PyTorch/
1. CODE FOR THIS COURSE-en_US.srt	0.7 KB
1. CODE FOR THIS COURSE.mp4	1.9 MB
10. Weight Initialization in PyTorch-en_US.srt	16.8 KB
10. Weight Initialization in PyTorch.mp4	69.1 MB
2. Computation Graphs and Deep Learning Frameworks-en_US.srt	17.8 KB
2. Computation Graphs and Deep Learning Frameworks.mp4	57.9 MB
3. Installing PyTorch and an Introduction-en_US.srt	14.6 KB
3. Installing PyTorch and an Introduction.mp4	104.1 MB
4. How PyTorch Works-en_US.srt	24.6 KB
4. How PyTorch Works.mp4	154.6 MB
5. Torch Tensors - Part 1-en_US.srt	15.4 KB
5. Torch Tensors - Part 1.mp4	91.3 MB
6. Torch Tensors - Part 2-en_US.srt	13.4 KB
6. Torch Tensors - Part 2.mp4	71.2 MB
7. Numpy Bridge, Tensor Concatenation and Adding Dimensions-en_US.srt	15.2 KB
7. Numpy Bridge, Tensor Concatenation and Adding Dimensions.mp4	78.7 MB
8. Automatic Differentiation-en_US.srt	12.2 KB
8. Automatic Differentiation.mp4	80.1 MB
9. Loss Functions in PyTorch-en_US.srt	37.7 KB
9. Loss Functions in PyTorch.mp4	233.6 MB
[Tutorialsplanet.NET].url	0.1 KB
/9. Practical Neural Networks in PyTorch - Application 1 Diabetes/
1. Part 1 Data Preprocessing-en_US.srt	19.0 KB
1. Part 1 Data Preprocessing.mp4	129.8 MB
2. Part 2 Data Normalization-en_US.srt	10.5 KB
2. Part 2 Data Normalization.mp4	58.1 MB
3. Part 3 Creating and Loading the Dataset-en_US.srt	9.7 KB
3. Part 3 Creating and Loading the Dataset.mp4	69.4 MB
4. Part 4 Building the Network-en_US.srt	23.0 KB
4. Part 4 Building the Network.mp4	178.8 MB
5. Part 5 Training the Network-en_US.srt	23.7 KB
5. Part 5 Training the Network.mp4	163.8 MB
Download the Dataset.html	0.3 KB
/
[Tutorialsplanet.NET].url	0.1 KB
Total files 584