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| [Tutorialsplanet.NET].url |
128б |
| [Tutorialsplanet.NET].url |
128б |
| [Tutorialsplanet.NET].url |
128б |
| [Tutorialsplanet.NET].url |
128б |
| [Tutorialsplanet.NET].url |
128б |
| [Tutorialsplanet.NET].url |
128б |
| 1. Autoencoders.mp4 |
42.08Мб |
| 1. Autoencoders-en_US.srt |
11.80Кб |
| 1. Batch Gradient Descent.mp4 |
49.42Мб |
| 1. Batch Gradient Descent-en_US.srt |
8.26Кб |
| 1. CNN Architectures Part 1.mp4 |
43.87Мб |
| 1. CNN Architectures Part 1-en_US.srt |
15.19Кб |
| 1. Code Details.mp4 |
31.94Мб |
| 1. Code Details-en_US.srt |
2.68Кб |
| 1. CODE FOR THIS COURSE.mp4 |
1.78Мб |
| 1. CODE FOR THIS COURSE-en_US.srt |
701б |
| 1. Creating the Dictionary.mp4 |
59.88Мб |
| 1. Creating the Dictionary-en_US.srt |
7.54Кб |
| 1. Data and the Model.mp4 |
74.39Мб |
| 1. Data and the Model-en_US.srt |
10.10Кб |
| 1. Data Augmentation.mp4 |
224.61Мб |
| 1. Data Augmentation-en_US.srt |
15.47Кб |
| 1. Dataset Preprocessing Part 1.mp4 |
83.35Мб |
| 1. Dataset Preprocessing Part 1-en_US.srt |
13.20Кб |
| 1. GPT Part 1.mp4 |
88.85Мб |
| 1. GPT Part 1-en_US.srt |
13.71Кб |
| 1. Implementation Details.mp4 |
50.34Мб |
| 1. Implementation Details-en_US.srt |
15.86Кб |
| 1. Introduction.mp4 |
74.44Мб |
| 1. Introduction-en_US.srt |
7.80Кб |
| 1. Introduction to Hyperparameter Tuning and Learning Rate Recap.mp4 |
17.65Мб |
| 1. Introduction to Hyperparameter Tuning and Learning Rate Recap-en_US.srt |
6.64Кб |
| 1. Introduction to Transformers.mp4 |
46.69Мб |
| 1. Introduction to Transformers-en_US.srt |
15.80Кб |
| 1. Introduction to Transposed Convolutions.mp4 |
30.98Мб |
| 1. Introduction to Transposed Convolutions-en_US.srt |
8.95Кб |
| 1. Loading and Normalizing the Dataset.mp4 |
52.57Мб |
| 1. Loading and Normalizing the Dataset-en_US.srt |
15.96Кб |
| 1. Mean Squared Error (MSE).mp4 |
19.82Мб |
| 1. Mean Squared Error (MSE)-en_US.srt |
9.21Кб |
| 1. Normal Distribution.mp4 |
18.73Мб |
| 1. Normal Distribution-en_US.srt |
8.63Кб |
| 1. NST Practical Part 1.mp4 |
63.78Мб |
| 1. NST Practical Part 1-en_US.srt |
13.93Кб |
| 1. NST Theory Part 1.mp4 |
52.53Мб |
| 1. NST Theory Part 1-en_US.srt |
9.19Кб |
| 1. Overfitting.mp4 |
26.27Мб |
| 1. Overfitting-en_US.srt |
6.41Кб |
| 1. Part 1 Data Preprocessing.mp4 |
123.77Мб |
| 1. Part 1 Data Preprocessing-en_US.srt |
18.58Кб |
| 1. Practical ResNet Part 1.mp4 |
71.51Мб |
| 1. Practical ResNet Part 1-en_US.srt |
15.96Кб |
| 1. Practical VAE Part 1.mp4 |
101.17Мб |
| 1. Practical VAE Part 1-en_US.srt |
25.48Кб |
| 1. Prerequisite Filters.mp4 |
36.41Мб |
| 1. Prerequisite Filters-en_US.srt |
6.30Кб |
| 1. Running your models on Google Colab.mp4 |
33.18Мб |
| 1. Running your models on Google Colab-en_US.srt |
10.41Кб |
| 1. Saving and Loading Part 1.mp4 |
130.61Мб |
| 1. Saving and Loading Part 1-en_US.srt |
19.27Кб |
| 1. Sequence Modeling.mp4 |
81.57Мб |
| 1. Sequence Modeling-en_US.srt |
17.25Кб |
| 1. The Dataset and Hyperparameters.mp4 |
70.53Мб |
| 1. The Dataset and Hyperparameters-en_US.srt |
15.60Кб |
| 1. Universal Transformers.mp4 |
21.83Мб |
| 1. Universal Transformers-en_US.srt |
9.28Кб |
| 1. Vision Transformer Part 1.mp4 |
85.28Мб |
| 1. Vision Transformer Part 1-en_US.srt |
16.97Кб |
| 1. Visualize Learning Part 1.mp4 |
24.38Мб |
| 1. Visualize Learning Part 1-en_US.srt |
12.01Кб |
| 1. What are Word Embeddings.mp4 |
72.71Мб |
| 1. What are Word Embeddings-en_US.srt |
12.19Кб |
| 1. What Can Deep Learning Do.mp4 |
156.25Мб |
| 1. What Can Deep Learning Do-en_US.srt |
18.21Кб |
| 1. What is BERT and its structure.mp4 |
34.67Мб |
| 1. What is BERT and its structure-en_US.srt |
11.23Кб |
| 1. Why do we need RNNs.mp4 |
18.62Мб |
| 1. Why do we need RNNs-en_US.srt |
6.66Кб |
| 1. Why we need activation functions.mp4 |
22.45Мб |
| 1. Why we need activation functions-en_US.srt |
5.08Кб |
| 1. YOLO Theory Part 1.mp4 |
133.82Мб |
| 1. YOLO Theory Part 1-en_US.srt |
6.75Кб |
| 10. Classifying your own Handwritten images.mp4 |
55.66Мб |
| 10. Classifying your own Handwritten images-en_US.srt |
15.29Кб |
| 10. CNN-LSTM.mp4 |
21.45Мб |
| 10. CNN-LSTM-en_US.srt |
6.36Кб |
| 10. Important formulas.mp4 |
13.38Мб |
| 10. Important formulas-en_US.srt |
6.83Кб |
| 10. Masked MultiHead Attention.mp4 |
26.69Мб |
| 10. Masked MultiHead Attention-en_US.srt |
8.69Кб |
| 10. MultiHead Attention Implementation Part 3.mp4 |
123.48Мб |
| 10. MultiHead Attention Implementation Part 3-en_US.srt |
16.09Кб |
| 10. SWATS - Switching from Adam to SGD.mp4 |
9.81Мб |
| 10. SWATS - Switching from Adam to SGD-en_US.srt |
2.09Кб |
| 10. Train Function.mp4 |
158.91Мб |
| 10. Train Function-en_US.srt |
20.55Кб |
| 10. Triplet Ranking Loss.mp4 |
125.70Мб |
| 10. Triplet Ranking Loss-en_US.srt |
16.43Кб |
| 10. Weight Initialization in PyTorch.mp4 |
65.88Мб |
| 10. Weight Initialization in PyTorch-en_US.srt |
16.41Кб |
| 10. YOLO Theory Part 10.mp4 |
25.29Мб |
| 10. YOLO Theory Part 10-en_US.srt |
2.86Кб |
| 11. CNN Characteristics.mp4 |
45.88Мб |
| 11. CNN Characteristics-en_US.srt |
10.78Кб |
| 11. Defining Hyperparameters.mp4 |
104.79Мб |
| 11. Defining Hyperparameters-en_US.srt |
18.59Кб |
| 11. Feed Forward Implementation.mp4 |
42.91Мб |
| 11. Feed Forward Implementation-en_US.srt |
4.43Кб |
| 11. MultiHead Attention in Decoder.mp4 |
11.07Мб |
| 11. MultiHead Attention in Decoder-en_US.srt |
3.47Кб |
| 11. Weight Decay.mp4 |
75.65Мб |
| 11. Weight Decay-en_US.srt |
9.28Кб |
| 11. YOLO Theory Part 11.mp4 |
52.80Мб |
| 11. YOLO Theory Part 11-en_US.srt |
7.44Кб |
| 12. Cross Entropy Loss.mp4 |
32.68Мб |
| 12. Cross Entropy Loss-en_US.srt |
16.15Кб |
| 12. Decoupling Weight Decay.mp4 |
52.25Мб |
| 12. Decoupling Weight Decay-en_US.srt |
5.79Кб |
| 12. Encoder Layer.mp4 |
86.66Мб |
| 12. Encoder Layer-en_US.srt |
9.84Кб |
| 12. Evaluation Function.mp4 |
90.60Мб |
| 12. Evaluation Function-en_US.srt |
21.66Кб |
| 12. Regularization and Batch Normalization in CNNs.mp4 |
18.19Мб |
| 12. Regularization and Batch Normalization in CNNs-en_US.srt |
4.73Кб |
| 12. YOLO Theory Part 12.mp4 |
58.28Мб |
| 12. YOLO Theory Part 12-en_US.srt |
13.31Кб |
| 13. AMSGrad.mp4 |
85.64Мб |
| 13. AMSGrad-en_US.srt |
11.64Кб |
| 13. Decoder Layer.mp4 |
62.27Мб |
| 13. Decoder Layer-en_US.srt |
6.78Кб |
| 13. DropBlock Dropout in CNNs.mp4 |
99.51Мб |
| 13. DropBlock Dropout in CNNs-en_US.srt |
15.43Кб |
| 13. KL Divergence Loss.mp4 |
23.59Мб |
| 13. KL Divergence Loss-en_US.srt |
7.72Кб |
| 13. Training.mp4 |
12.85Мб |
| 13. Training-en_US.srt |
3.33Кб |
| 14. Label Smoothing.mp4 |
13.21Мб |
| 14. Label Smoothing-en_US.srt |
5.96Кб |
| 14. Results.mp4 |
33.86Мб |
| 14. Results-en_US.srt |
3.69Кб |
| 14. Softmax with Temperature.mp4 |
27.35Мб |
| 14. Softmax with Temperature-en_US.srt |
12.56Кб |
| 14. Transformer.mp4 |
117.13Мб |
| 14. Transformer-en_US.srt |
14.72Кб |
| 15. AdamWarmup.mp4 |
75.29Мб |
| 15. AdamWarmup-en_US.srt |
8.75Кб |
| 15. Dropout.mp4 |
75.25Мб |
| 15. Dropout-en_US.srt |
11.98Кб |
| 16. Learning Rate Warmup.mp4 |
29.07Мб |
| 16. Learning Rate Warmup-en_US.srt |
8.61Кб |
| 16. Loss with Label Smoothing.mp4 |
214.69Мб |
| 16. Loss with Label Smoothing-en_US.srt |
24.79Кб |
| 17. Defining the Model.mp4 |
43.71Мб |
| 17. Defining the Model-en_US.srt |
8.35Кб |
| 18. Training Function.mp4 |
100.55Мб |
| 18. Training Function-en_US.srt |
13.91Кб |
| 19. Evaluation Function.mp4 |
109.81Мб |
| 19. Evaluation Function-en_US.srt |
20.63Кб |
| 2. Computation Graphs and Deep Learning Frameworks.mp4 |
55.23Мб |
| 2. Computation Graphs and Deep Learning Frameworks-en_US.srt |
17.33Кб |
| 2. Convolution Operation as Matrix Multiplication.mp4 |
70.98Мб |
| 2. Convolution Operation as Matrix Multiplication-en_US.srt |
11.18Кб |
| 2. Dataset Preprocessing Part 2.mp4 |
134.64Мб |
| 2. Dataset Preprocessing Part 2-en_US.srt |
19.87Кб |
| 2. Denoising Autoencoders.mp4 |
30.00Мб |
| 2. Denoising Autoencoders-en_US.srt |
9.27Кб |
| 2. External URLs.txt |
70б |
| 2. GPT Part 2.mp4 |
45.39Мб |
| 2. GPT Part 2-en_US.srt |
12.18Кб |
| 2. Gradient Accumulation.mp4 |
56.83Мб |
| 2. Gradient Accumulation-en_US.srt |
20.70Кб |
| 2. Image Captioning.mp4 |
34.74Мб |
| 2. Image Captioning-en_US.srt |
6.66Кб |
| 2. Importing and Defining Parameters.mp4 |
142.18Мб |
| 2. Importing and Defining Parameters-en_US.srt |
15.92Кб |
| 2. Input Embeddings.mp4 |
65.76Мб |
| 2. Input Embeddings-en_US.srt |
8.51Кб |
| 2. Introduction to Convolutional Networks and the need for them.mp4 |
25.12Мб |
| 2. Introduction to Convolutional Networks and the need for them-en_US.srt |
9.22Кб |
| 2. L1 and L2 Regularization.mp4 |
33.50Мб |
| 2. L1 and L2 Regularization-en_US.srt |
11.81Кб |
| 2. L1 Loss (MAE).mp4 |
77.21Мб |
| 2. L1 Loss (MAE)-en_US.srt |
10.96Кб |
| 2. Loading the Dataset.mp4 |
177.38Мб |
| 2. Loading the Dataset-en_US.srt |
14.10Кб |
| 2. Masked Language Modelling.mp4 |
23.09Мб |
| 2. Masked Language Modelling-en_US.srt |
7.12Кб |
| 2. NST Practical Part 2.mp4 |
127.87Мб |
| 2. NST Practical Part 2-en_US.srt |
12.48Кб |
| 2. NST Theory Part 2.mp4 |
35.19Мб |
| 2. NST Theory Part 2-en_US.srt |
7.90Кб |
| 2. Part 2 Data Normalization.mp4 |
55.43Мб |
| 2. Part 2 Data Normalization-en_US.srt |
10.21Кб |
| 2. Practical ResNet Part 2.mp4 |
85.73Мб |
| 2. Practical ResNet Part 2-en_US.srt |
16.04Кб |
| 2. Practical Universal Transformers Modifying the Transformers code.mp4 |
161.10Мб |
| 2. Practical Universal Transformers Modifying the Transformers code-en_US.srt |
17.27Кб |
| 2. Practical VAE Part 2.mp4 |
103.79Мб |
| 2. Practical VAE Part 2-en_US.srt |
14.70Кб |
| 2. Processing the Model.mp4 |
142.48Мб |
| 2. Processing the Model-en_US.srt |
17.53Кб |
| 2. Processing the Text.mp4 |
108.66Мб |
| 2. Processing the Text-en_US.srt |
13.42Кб |
| 2. Residual Networks Part 1.mp4 |
122.27Мб |
| 2. Residual Networks Part 1-en_US.srt |
14.13Кб |
| 2. Saving and Loading Part 2.mp4 |
96.57Мб |
| 2. Saving and Loading Part 2-en_US.srt |
10.03Кб |
| 2. Sigmoid Activation.mp4 |
20.16Мб |
| 2. Sigmoid Activation-en_US.srt |
8.23Кб |
| 2. Step Learning Rate Decay.mp4 |
62.86Мб |
| 2. Step Learning Rate Decay-en_US.srt |
16.36Кб |
| 2. Stochastic Gradient Descent.mp4 |
18.11Мб |
| 2. Stochastic Gradient Descent-en_US.srt |
6.51Кб |
| 2. The Rise of Deep Learning.mp4 |
41.80Мб |
| 2. The Rise of Deep Learning-en_US.srt |
8.13Кб |
| 2. Understanding the Encoder.mp4 |
92.74Мб |
| 2. Understanding the Encoder-en_US.srt |
7.73Кб |
| 2. Understanding the Implementation.mp4 |
23.40Мб |
| 2. Understanding the Implementation-en_US.srt |
10.90Кб |
| 2. Utility Functions.mp4 |
41.36Мб |
| 2. Utility Functions-en_US.srt |
18.16Кб |
| 2. Vanilla RNNs.mp4 |
51.57Мб |
| 2. Vanilla RNNs-en_US.srt |
10.83Кб |
| 2. Vision Transformer Part 2.mp4 |
35.31Мб |
| 2. Vision Transformer Part 2-en_US.srt |
11.86Кб |
| 2. Visualize Learning Part 2.mp4 |
12.21Мб |
| 2. Visualize Learning Part 2-en_US.srt |
2.49Кб |
| 2. Visualizing and Loading the Dataset.mp4 |
60.74Мб |
| 2. Visualizing and Loading the Dataset-en_US.srt |
12.37Кб |
| 2. Visualizing Word Embeddings.mp4 |
12.19Мб |
| 2. Visualizing Word Embeddings-en_US.srt |
4.30Кб |
| 2. What happens when all weights are initialized to the same value.mp4 |
59.96Мб |
| 2. What happens when all weights are initialized to the same value-en_US.srt |
12.74Кб |
| 2. YOLO Theory Part 2.mp4 |
80.65Мб |
| 2. YOLO Theory Part 2-en_US.srt |
16.11Кб |
| 20. Main Function and User Evaluation.mp4 |
93.28Мб |
| 20. Main Function and User Evaluation-en_US.srt |
12.47Кб |
| 21. Action.mp4 |
32.24Мб |
| 21. Action-en_US.srt |
3.95Кб |
| 3. Accuracy Calculation.mp4 |
74.06Мб |
| 3. Accuracy Calculation-en_US.srt |
13.71Кб |
| 3. Attention Mechanisms.mp4 |
16.49Мб |
| 3. Attention Mechanisms-en_US.srt |
7.00Кб |
| 3. Building the CNN.mp4 |
251.43Мб |
| 3. Building the CNN-en_US.srt |
31.83Кб |
| 3. Cyclic Learning Rate.mp4 |
69.37Мб |
| 3. Cyclic Learning Rate-en_US.srt |
12.97Кб |
| 3. Dataset Preprocessing Part 3.mp4 |
80.05Мб |
| 3. Dataset Preprocessing Part 3-en_US.srt |
13.91Кб |
| 3. Defining and Visualizing the Parameters.mp4 |
69.54Мб |
| 3. Defining and Visualizing the Parameters-en_US.srt |
9.53Кб |
| 3. Defining the Encoder.mp4 |
404.31Мб |
| 3. Defining the Encoder-en_US.srt |
30.95Кб |
| 3. Defining the Network Class.mp4 |
85.95Мб |
| 3. Defining the Network Class-en_US.srt |
12.03Кб |
| 3. Dropout.mp4 |
75.22Мб |
| 3. Dropout-en_US.srt |
11.98Кб |
| 3. Filters and Features.mp4 |
51.93Мб |
| 3. Filters and Features-en_US.srt |
12.21Кб |
| 3. Forward Propagation.mp4 |
85.20Мб |
| 3. Forward Propagation-en_US.srt |
15.30Кб |
| 3. Huber Loss.mp4 |
28.65Мб |
| 3. Huber Loss-en_US.srt |
8.22Кб |
| 3. Installing PyTorch and an Introduction.mp4 |
99.25Мб |
| 3. Installing PyTorch and an Introduction-en_US.srt |
14.25Кб |
| 3. Measuring Word Embeddings.mp4 |
5.53Мб |
| 3. Measuring Word Embeddings-en_US.srt |
2.57Кб |
| 3. Mini-Batch Gradient Descent.mp4 |
6.94Мб |
| 3. Mini-Batch Gradient Descent-en_US.srt |
3.44Кб |
| 3. Modifying the Network.mp4 |
96.99Мб |
| 3. Modifying the Network-en_US.srt |
10.79Кб |
| 3. Next Sentence Prediction.mp4 |
42.59Мб |
| 3. Next Sentence Prediction-en_US.srt |
11.52Кб |
| 3. NST Practical Part 3.mp4 |
105.89Мб |
| 3. NST Practical Part 3-en_US.srt |
14.56Кб |
| 3. NST Theory Part 3.mp4 |
69.11Мб |
| 3. NST Theory Part 3-en_US.srt |
13.50Кб |
| 3. Part 3 Creating and Loading the Dataset.mp4 |
66.20Мб |
| 3. Part 3 Creating and Loading the Dataset-en_US.srt |
9.46Кб |
| 3. Positional Encoding.mp4 |
95.97Мб |
| 3. Positional Encoding-en_US.srt |
18.06Кб |
| 3. Practical ResNet Part 3.mp4 |
103.17Мб |
| 3. Practical ResNet Part 3-en_US.srt |
15.57Кб |
| 3. Practical VAE Part 3.mp4 |
93.22Мб |
| 3. Practical VAE Part 3-en_US.srt |
15.32Кб |
| 3. Quiz Solution Discussion.mp4 |
15.38Мб |
| 3. Quiz Solution Discussion-en_US.srt |
5.12Кб |
| 3. Residual Networks Part 2.mp4 |
151.37Мб |
| 3. Residual Networks Part 2-en_US.srt |
23.05Кб |
| 3. Saving and Loading Part 3.mp4 |
52.79Мб |
| 3. Saving and Loading Part 3-en_US.srt |
7.53Кб |
| 3. Tanh Activation.mp4 |
13.87Мб |
| 3. Tanh Activation-en_US.srt |
4.10Кб |
| 3. The Essence of Neural Networks.mp4 |
49.99Мб |
| 3. The Essence of Neural Networks-en_US.srt |
12.75Кб |
| 3. The Problem in Autoencoders.mp4 |
13.42Мб |
| 3. The Problem in Autoencoders-en_US.srt |
6.43Кб |
| 3. Transformers for other tasks.mp4 |
112.79Мб |
| 3. Transformers for other tasks-en_US.srt |
11.14Кб |
| 3. Transposed Convolutions.mp4 |
36.09Мб |
| 3. Transposed Convolutions-en_US.srt |
8.33Кб |
| 3. Vision Transformer Part 3.mp4 |
106.39Мб |
| 3. Vision Transformer Part 3-en_US.srt |
15.46Кб |
| 3. Visualize Learning Part 3.mp4 |
27.37Мб |
| 3. Visualize Learning Part 3-en_US.srt |
10.34Кб |
| 3. Visualizing the Feature Maps.mp4 |
133.26Мб |
| 3. Visualizing the Feature Maps-en_US.srt |
16.38Кб |
| 3. Xavier Initialization.mp4 |
109.71Мб |
| 3. Xavier Initialization-en_US.srt |
12.61Кб |
| 3. YOLO Theory Part 3.mp4 |
123.91Мб |
| 3. YOLO Theory Part 3-en_US.srt |
12.26Кб |
| 3. Zero-Shot Predictions with GPT.mp4 |
43.41Мб |
| 3. Zero-Shot Predictions with GPT-en_US.srt |
10.37Кб |
| 4. Backpropagation Through Time.mp4 |
61.56Мб |
| 4. Backpropagation Through Time-en_US.srt |
16.52Кб |
| 4. Binary Cross Entropy Loss.mp4 |
44.94Мб |
| 4. Binary Cross Entropy Loss-en_US.srt |
17.16Кб |
| 4. Byte-Pair Encoding.mp4 |
39.26Мб |
| 4. Byte-Pair Encoding-en_US.srt |
10.44Кб |
| 4. CNN Architectures Part 2.mp4 |
13.38Мб |
| 4. CNN Architectures Part 2-en_US.srt |
4.63Кб |
| 4. Constructing the Dataset Part 1.mp4 |
136.13Мб |
| 4. Constructing the Dataset Part 1-en_US.srt |
18.22Кб |
| 4. Convolution over Volume Animation.mp4 |
21.31Мб |
| 4. Convolution over Volume Animation-en_US.srt |
4.54Кб |
| 4. Cosine Annealing with Warm Restarts.mp4 |
35.21Мб |
| 4. Cosine Annealing with Warm Restarts-en_US.srt |
7.16Кб |
| 4. Creating the Network.mp4 |
112.10Мб |
| 4. Creating the network class and the network functions.mp4 |
56.20Мб |
| 4. Creating the network class and the network functions-en_US.srt |
0б |
| 4. Creating the Network-en_US.srt |
14.15Кб |
| 4. Dataset Preprocessing Part 4.mp4 |
20.34Мб |
| 4. Dataset Preprocessing Part 4-en_US.srt |
5.68Кб |
| 4. Defining the Model.mp4 |
18.68Мб |
| 4. Defining the Model-en_US.srt |
5.57Кб |
| 4. DropConnect.mp4 |
14.18Мб |
| 4. DropConnect-en_US.srt |
2.23Кб |
| 4. Exponentially Weighted Average Intuition.mp4 |
22.92Мб |
| 4. Exponentially Weighted Average Intuition-en_US.srt |
6.87Кб |
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| 4. MultiHead Attention Part 1.mp4 |
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| 4. The Perceptron.mp4 |
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| 5. Constructing the Dataset Part 2.mp4 |
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| 5. Densely Connected Networks.mp4 |
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| 5. Exponentially Linear Units (ELU).mp4 |
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| 5. Exponentially Weighted Average Implementation.mp4 |
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| 5. Fast Neural Style Transfer.mp4 |
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| 5. Finetuning the Network.mp4 |
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| 5. Part 5 Training the Network.mp4 |
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| 5. YOLO Theory Part 5.mp4 |
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| 6. Backpropagation Equations.mp4 |
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| 6. The Forward Propagation.mp4 |
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| 7. Backpropagation.mp4 |
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| 7. Residual Learning.mp4 |
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| 8. Activation, Pooling and FC.mp4 |
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| Before Proceeding with the Backpropagation.html |
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| BEFORE STARTING...PLEASE READ THIS.html |
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| CODE.html |
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| Convolution over Volume Animation Resource.html |
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| dog.jpg |
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| Download the Dataset.html |
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| Download the Dataset.html |
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| imagenet-class-index.json |
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| Implementation.html |
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| Notebook for the following Lecture.html |
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| Note on Residual Networks Implementation.html |
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| Note on Weight Decay.html |
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| Practical Loss Functions Note.html |
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| Practical Weight Initialization Note.html |
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| SANITY CHECK ON PREVIOUS SECTIONS.html |
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| The MNIST Dataset.html |
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| YOLO Code Note.html |
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