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[Tutorialsplanet.NET] Udemy - The Complete Neural Networks Bootcamp Theory, Applications
magnet:?xt=urn:btih:43f148d4ac20cc5bf65b0efeac89ffd575208065&dn=[Tutorialsplanet.NET] Udemy - The Complete Neural Networks Bootcamp Theory, Applications
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Hash值:
43f148d4ac20cc5bf65b0efeac89ffd575208065
点击数:
152
文件大小:
18.78 GB
文件数量:
277
创建日期:
2023-2-12 00:36
最后访问:
2024-6-2 21:50
访问标签:
Tutorialsplanet
NET
Udemy
-
The
Complete
Neural
Networks
Bootcamp
Theory
Applications
文件列表详情
1. How Neural Networks and Backpropagation Works/1. What Can Deep Learning Do.mp4 156.25 MB
1. How Neural Networks and Backpropagation Works/2. The Rise of Deep Learning.mp4 41.8 MB
1. How Neural Networks and Backpropagation Works/3. The Essence of Neural Networks.mp4 49.99 MB
1. How Neural Networks and Backpropagation Works/4. The Perceptron.mp4 110.88 MB
1. How Neural Networks and Backpropagation Works/5. Gradient Descent.mp4 40.6 MB
1. How Neural Networks and Backpropagation Works/6. The Forward Propagation.mp4 52.23 MB
1. How Neural Networks and Backpropagation Works/7. Backpropagation Part 1.mp4 29.37 MB
1. How Neural Networks and Backpropagation Works/8. Backpropagation Part 2.mp4 27.82 MB
10. Visualize the Learning Process/1. Visualize Learning Part 1.mp4 24.38 MB
10. Visualize the Learning Process/2. Visualize Learning Part 2.mp4 12.21 MB
10. Visualize the Learning Process/3. Visualize Learning Part 3.mp4 27.37 MB
10. Visualize the Learning Process/4. Visualize Learning Part 4.mp4 20.1 MB
10. Visualize the Learning Process/5. Visualize Learning Part 5.mp4 71.66 MB
10. Visualize the Learning Process/6. Visualize Learning Part 6.mp4 64.39 MB
10. Visualize the Learning Process/7. Neural Networks Playground.mp4 32.52 MB
11. Implementing a Neural Network from Scratch with Numpy/1. The Dataset and Hyperparameters.mp4 70.53 MB
11. Implementing a Neural Network from Scratch with Numpy/2. Understanding the Implementation.mp4 23.4 MB
11. Implementing a Neural Network from Scratch with Numpy/3. Forward Propagation.mp4 85.2 MB
11. Implementing a Neural Network from Scratch with Numpy/4. Loss Function.mp4 68.48 MB
11. Implementing a Neural Network from Scratch with Numpy/5. Prediction.mp4 27.71 MB
11. Implementing a Neural Network from Scratch with Numpy/6. Backpropagation Equations.mp4 98.77 MB
11. Implementing a Neural Network from Scratch with Numpy/7. Backpropagation.mp4 148.09 MB
11. Implementing a Neural Network from Scratch with Numpy/8. Initializing the Network.mp4 58.9 MB
11. Implementing a Neural Network from Scratch with Numpy/9. Training the Model.mp4 47.19 MB
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/1. Code Details.mp4 31.94 MB
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/2. Importing and Defining Parameters.mp4 142.18 MB
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/3. Defining the Network Class.mp4 85.95 MB
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/4. Creating the network class and the network functions.mp4 56.2 MB
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/5. Training the Network.mp4 333.24 MB
12. Practical Neural Networks in PyTorch - Application 2 Handwritten Digits/6. Testing the Network.mp4 47.1 MB
13. Convolutional Neural Networks/1. Prerequisite Filters.mp4 36.41 MB
13. Convolutional Neural Networks/10. Important formulas.mp4 13.38 MB
13. Convolutional Neural Networks/11. CNN Characteristics.mp4 45.88 MB
13. Convolutional Neural Networks/12. Regularization and Batch Normalization in CNNs.mp4 18.19 MB
13. Convolutional Neural Networks/13. DropBlock Dropout in CNNs.mp4 99.51 MB
13. Convolutional Neural Networks/14. Softmax with Temperature.mp4 27.35 MB
13. Convolutional Neural Networks/2. Introduction to Convolutional Networks and the need for them.mp4 25.12 MB
13. Convolutional Neural Networks/3. Filters and Features.mp4 51.93 MB
13. Convolutional Neural Networks/4. Convolution over Volume Animation.mp4 21.31 MB
13. Convolutional Neural Networks/5. More on Convolutions.mp4 29.98 MB
13. Convolutional Neural Networks/6. Quiz Solution Discussion.mp4 5.87 MB
13. Convolutional Neural Networks/7. A Tool for Convolution Visualization.mp4 27.97 MB
13. Convolutional Neural Networks/8. Activation, Pooling and FC.mp4 80.68 MB
13. Convolutional Neural Networks/9. CNN Visualization.mp4 15.41 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/1. Loading and Normalizing the Dataset.mp4 52.57 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/10. Classifying your own Handwritten images.mp4 55.66 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/2. Visualizing and Loading the Dataset.mp4 60.74 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/3. Building the CNN.mp4 251.43 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/4. Defining the Model.mp4 18.68 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/5. Understanding the Propagation.mp4 26.19 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/6. Training the CNN.mp4 131.06 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/7. Testing the CNN.mp4 35.82 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/8. Plotting and Putting into Action.mp4 45.32 MB
14. Practical Convolutional Networks in PyTorch - Image Classification/9. Predicting an image.mp4 17.46 MB
15. CNN Architectures/1. CNN Architectures Part 1.mp4 43.87 MB
15. CNN Architectures/2. Residual Networks Part 1.mp4 122.27 MB
15. CNN Architectures/3. Residual Networks Part 2.mp4 151.37 MB
15. CNN Architectures/4. CNN Architectures Part 2.mp4 13.38 MB
15. CNN Architectures/5. Densely Connected Networks.mp4 95.14 MB
15. CNN Architectures/6. Squeeze-Excite Networks.mp4 39.6 MB
15. CNN Architectures/7. Seperable Convolutions.mp4 60.51 MB
15. CNN Architectures/8. Transfer Learning.mp4 29.24 MB
16. Practical Residual Networks in PyTorch/1. Practical ResNet Part 1.mp4 71.51 MB
16. Practical Residual Networks in PyTorch/2. Practical ResNet Part 2.mp4 85.73 MB
16. Practical Residual Networks in PyTorch/3. Practical ResNet Part 3.mp4 103.17 MB
16. Practical Residual Networks in PyTorch/4. Practical ResNet Part 4.mp4 143.28 MB
17. Transposed Convolutions/1. Introduction to Transposed Convolutions.mp4 30.98 MB
17. Transposed Convolutions/2. Convolution Operation as Matrix Multiplication.mp4 70.98 MB
17. Transposed Convolutions/3. Transposed Convolutions.mp4 36.09 MB
18. Transfer Learning in PyTorch - Image Classification/1. Data Augmentation.mp4 224.61 MB
18. Transfer Learning in PyTorch - Image Classification/2. Loading the Dataset.mp4 177.38 MB
18. Transfer Learning in PyTorch - Image Classification/3. Modifying the Network.mp4 96.99 MB
18. Transfer Learning in PyTorch - Image Classification/4. Understanding the data.mp4 101.76 MB
18. Transfer Learning in PyTorch - Image Classification/5. Finetuning the Network.mp4 50.02 MB
18. Transfer Learning in PyTorch - Image Classification/6. Testing and Visualizing the results.mp4 118.43 MB
19. Convolutional Networks Visualization/1. Data and the Model.mp4 74.39 MB
19. Convolutional Networks Visualization/2. Processing the Model.mp4 142.48 MB
19. Convolutional Networks Visualization/3. Visualizing the Feature Maps.mp4 133.26 MB
2. Loss Functions/1. Mean Squared Error (MSE).mp4 19.82 MB
2. Loss Functions/10. Triplet Ranking Loss.mp4 125.7 MB
2. Loss Functions/2. L1 Loss (MAE).mp4 77.21 MB
2. Loss Functions/3. Huber Loss.mp4 28.65 MB
2. Loss Functions/4. Binary Cross Entropy Loss.mp4 44.94 MB
2. Loss Functions/5. Cross Entropy Loss.mp4 24.66 MB
2. Loss Functions/6. Softmax Function.mp4 44.73 MB
2. Loss Functions/7. KL divergence Loss.mp4 25.4 MB
2. Loss Functions/8. Contrastive Loss.mp4 62.66 MB
2. Loss Functions/9. Hinge Loss.mp4 67.43 MB
20. YOLO Object Detection (Theory)/1. YOLO Theory Part 1.mp4 133.82 MB
20. YOLO Object Detection (Theory)/10. YOLO Theory Part 10.mp4 25.29 MB
20. YOLO Object Detection (Theory)/11. YOLO Theory Part 11.mp4 52.8 MB
20. YOLO Object Detection (Theory)/12. YOLO Theory Part 12.mp4 58.28 MB
20. YOLO Object Detection (Theory)/2. YOLO Theory Part 2.mp4 80.65 MB
20. YOLO Object Detection (Theory)/3. YOLO Theory Part 3.mp4 123.91 MB
20. YOLO Object Detection (Theory)/4. YOLO Theory Part 4.mp4 25.77 MB
20. YOLO Object Detection (Theory)/5. YOLO Theory Part 5.mp4 104.97 MB
20. YOLO Object Detection (Theory)/6. YOLO Theory Part 6.mp4 123.77 MB
20. YOLO Object Detection (Theory)/7. YOLO Theory Part 7.mp4 69.72 MB
20. YOLO Object Detection (Theory)/8. YOLO Theory Part 8.mp4 77.19 MB
20. YOLO Object Detection (Theory)/9. YOLO Theory Part 9.mp4 17.69 MB
21. Autoencoders and Variational Autoencoders/1. Autoencoders.mp4 42.08 MB
21. Autoencoders and Variational Autoencoders/2. Denoising Autoencoders.mp4 30 MB
21. Autoencoders and Variational Autoencoders/3. The Problem in Autoencoders.mp4 13.42 MB
21. Autoencoders and Variational Autoencoders/4. Variational Autoencoders.mp4 70.2 MB
21. Autoencoders and Variational Autoencoders/5. Probability Distributions Recap.mp4 259.26 MB
21. Autoencoders and Variational Autoencoders/6. Loss Function Derivation for VAE.mp4 319.16 MB
21. Autoencoders and Variational Autoencoders/7. Deep Fake.mp4 85.25 MB
22. Practical Variational Autoencoders in PyTorch/1. Practical VAE Part 1.mp4 101.17 MB
22. Practical Variational Autoencoders in PyTorch/2. Practical VAE Part 2.mp4 103.79 MB
22. Practical Variational Autoencoders in PyTorch/3. Practical VAE Part 3.mp4 93.22 MB
23. Neural Style Transfer/1. NST Theory Part 1.mp4 52.53 MB
23. Neural Style Transfer/2. NST Theory Part 2.mp4 35.19 MB
23. Neural Style Transfer/3. NST Theory Part 3.mp4 69.11 MB
24. Practical Neural Style Transfer in PyTorch/1. NST Practical Part 1.mp4 63.78 MB
24. Practical Neural Style Transfer in PyTorch/2. NST Practical Part 2.mp4 127.87 MB
24. Practical Neural Style Transfer in PyTorch/3. NST Practical Part 3.mp4 105.89 MB
24. Practical Neural Style Transfer in PyTorch/4. NST Practical Part 4.mp4 130.96 MB
24. Practical Neural Style Transfer in PyTorch/5. Fast Neural Style Transfer.mp4 44.83 MB
25. Recurrent Neural Networks/1. Why do we need RNNs.mp4 18.62 MB
25. Recurrent Neural Networks/10. CNN-LSTM.mp4 21.45 MB
25. Recurrent Neural Networks/2. Vanilla RNNs.mp4 51.57 MB
25. Recurrent Neural Networks/3. Quiz Solution Discussion.mp4 15.38 MB
25. Recurrent Neural Networks/4. Backpropagation Through Time.mp4 61.56 MB
25. Recurrent Neural Networks/5. Stacked RNNs.mp4 7.77 MB
25. Recurrent Neural Networks/6. Vanishing and Exploding Gradient Problem.mp4 66.86 MB
25. Recurrent Neural Networks/7. LSTMs.mp4 111.65 MB
25. Recurrent Neural Networks/8. Bidirectional RNNs.mp4 15.03 MB
25. Recurrent Neural Networks/9. GRUs.mp4 26.15 MB
26. Word Embeddings/1. What are Word Embeddings.mp4 72.71 MB
26. Word Embeddings/2. Visualizing Word Embeddings.mp4 12.19 MB
26. Word Embeddings/3. Measuring Word Embeddings.mp4 5.53 MB
26. Word Embeddings/4. Word Embeddings Models.mp4 10.65 MB
26. Word Embeddings/5. Word Embeddings in PyTorch.mp4 53.24 MB
27. Practical Recurrent Networks in PyTorch/1. Creating the Dictionary.mp4 59.88 MB
27. Practical Recurrent Networks in PyTorch/2. Processing the Text.mp4 108.66 MB
27. Practical Recurrent Networks in PyTorch/3. Defining and Visualizing the Parameters.mp4 69.54 MB
27. Practical Recurrent Networks in PyTorch/4. Creating the Network.mp4 112.1 MB
27. Practical Recurrent Networks in PyTorch/5. Training the Network.mp4 151.65 MB
27. Practical Recurrent Networks in PyTorch/6. Generating Text.mp4 177.83 MB
28. Saving and Loading Models/1. Saving and Loading Part 1.mp4 130.61 MB
28. Saving and Loading Models/2. Saving and Loading Part 2.mp4 96.57 MB
28. Saving and Loading Models/3. Saving and Loading Part 3.mp4 52.79 MB
29. Sequence Modelling/1. Sequence Modeling.mp4 81.57 MB
29. Sequence Modelling/2. Image Captioning.mp4 34.74 MB
29. Sequence Modelling/3. Attention Mechanisms.mp4 16.49 MB
29. Sequence Modelling/4. How Attention Mechanisms Work.mp4 40.15 MB
3. Activation Functions/1. Why we need activation functions.mp4 22.45 MB
3. Activation Functions/2. Sigmoid Activation.mp4 20.16 MB
3. Activation Functions/3. Tanh Activation.mp4 13.87 MB
3. Activation Functions/4. ReLU and PReLU.mp4 20.77 MB
3. Activation Functions/5. Exponentially Linear Units (ELU).mp4 10.64 MB
3. Activation Functions/6. Gated Linear Units (GLU).mp4 26.52 MB
3. Activation Functions/7. Swish Activation.mp4 12.87 MB
3. Activation Functions/8. Mish Activation.mp4 38.14 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/1. Introduction.mp4 74.44 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/2. Understanding the Encoder.mp4 92.74 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/3. Defining the Encoder.mp4 404.31 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/4. Understanding Pack Padded Sequence.mp4 29.21 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/5. Designing the Attention Model.mp4 260.29 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/6. Designing the Decoder Part 1.mp4 139.29 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/7. Designing the Decoder Part 2.mp4 176.14 MB
30. Practical Sequence Modelling in PyTorch Chatbot Application/8. Teacher Forcing.mp4 21.72 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/1. Implementation Details.mp4 50.34 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/10. Train Function.mp4 158.91 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/11. Defining Hyperparameters.mp4 104.79 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/12. Evaluation Function.mp4 90.6 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/13. Training.mp4 12.85 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/14. Results.mp4 33.86 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/2. Utility Functions.mp4 41.36 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/3. Accuracy Calculation.mp4 74.06 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/4. Constructing the Dataset Part 1.mp4 136.13 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/5. Constructing the Dataset Part 2.mp4 56.91 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/6. Creating the Encoder.mp4 84.85 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/7. Creating the Decoder Part 1.mp4 118.19 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/8. Creating the Decoder Part 2.mp4 97.47 MB
31. Practical Sequence Modelling in PyTorch Image Captioning/9. Creating the Decoder Part 3.mp4 131.05 MB
32. Transformers/1. Introduction to Transformers.mp4 46.69 MB
32. Transformers/10. Masked MultiHead Attention.mp4 26.69 MB
32. Transformers/11. MultiHead Attention in Decoder.mp4 11.07 MB
32. Transformers/12. Cross Entropy Loss.mp4 32.68 MB
32. Transformers/13. KL Divergence Loss.mp4 23.59 MB
32. Transformers/14. Label Smoothing.mp4 13.21 MB
32. Transformers/15. Dropout.mp4 75.25 MB
32. Transformers/16. Learning Rate Warmup.mp4 29.07 MB
32. Transformers/2. Input Embeddings.mp4 65.76 MB
32. Transformers/3. Positional Encoding.mp4 95.97 MB
32. Transformers/4. MultiHead Attention Part 1.mp4 58.32 MB
32. Transformers/5. MultiHead Attention Part 2.mp4 45.85 MB
32. Transformers/6. Concat and Linear.mp4 9.77 MB
32. Transformers/7. Residual Learning.mp4 28.02 MB
32. Transformers/8. Layer Normalization.mp4 21.79 MB
32. Transformers/9. Feed Forward.mp4 15.53 MB
33. Build a Chatbot with Transformers/1. Dataset Preprocessing Part 1.mp4 83.35 MB
33. Build a Chatbot with Transformers/10. MultiHead Attention Implementation Part 3.mp4 123.48 MB
33. Build a Chatbot with Transformers/11. Feed Forward Implementation.mp4 42.91 MB
33. Build a Chatbot with Transformers/12. Encoder Layer.mp4 86.66 MB
33. Build a Chatbot with Transformers/13. Decoder Layer.mp4 62.27 MB
33. Build a Chatbot with Transformers/14. Transformer.mp4 117.13 MB
33. Build a Chatbot with Transformers/15. AdamWarmup.mp4 75.29 MB
33. Build a Chatbot with Transformers/16. Loss with Label Smoothing.mp4 214.69 MB
33. Build a Chatbot with Transformers/17. Defining the Model.mp4 43.71 MB
33. Build a Chatbot with Transformers/18. Training Function.mp4 100.55 MB
33. Build a Chatbot with Transformers/19. Evaluation Function.mp4 109.81 MB
33. Build a Chatbot with Transformers/2. Dataset Preprocessing Part 2.mp4 134.64 MB
33. Build a Chatbot with Transformers/20. Main Function and User Evaluation.mp4 93.28 MB
33. Build a Chatbot with Transformers/21. Action.mp4 32.24 MB
33. Build a Chatbot with Transformers/3. Dataset Preprocessing Part 3.mp4 80.05 MB
33. Build a Chatbot with Transformers/4. Dataset Preprocessing Part 4.mp4 20.34 MB
33. Build a Chatbot with Transformers/5. Dataset Preprocessing Part 5.mp4 92.39 MB
33. Build a Chatbot with Transformers/6. Data Loading and Masking.mp4 75.82 MB
33. Build a Chatbot with Transformers/7. Embeddings.mp4 81.22 MB
33. Build a Chatbot with Transformers/8. MultiHead Attention Implementation Part 1.mp4 60.43 MB
33. Build a Chatbot with Transformers/9. MultiHead Attention Implementation Part 2.mp4 51.41 MB
34. Universal Transformers/1. Universal Transformers.mp4 21.83 MB
34. Universal Transformers/2. Practical Universal Transformers Modifying the Transformers code.mp4 161.1 MB
34. Universal Transformers/3. Transformers for other tasks.mp4 112.79 MB
35. Google Colab and Gradient Accumulation/1. Running your models on Google Colab.mp4 33.18 MB
35. Google Colab and Gradient Accumulation/2. Gradient Accumulation.mp4 56.83 MB
36. BERT/1. What is BERT and its structure.mp4 34.67 MB
36. BERT/2. Masked Language Modelling.mp4 23.09 MB
36. BERT/3. Next Sentence Prediction.mp4 42.59 MB
36. BERT/4. Fine-tuning BERT.mp4 50.66 MB
36. BERT/5. Exploring Transformers.mp4 136.61 MB
37. Vision Transformers/1. Vision Transformer Part 1.mp4 85.28 MB
37. Vision Transformers/2. Vision Transformer Part 2.mp4 35.31 MB
37. Vision Transformers/3. Vision Transformer Part 3.mp4 106.39 MB
38. GPT/1. GPT Part 1.mp4 88.85 MB
38. GPT/2. GPT Part 2.mp4 45.39 MB
38. GPT/3. Zero-Shot Predictions with GPT.mp4 43.41 MB
38. GPT/4. Byte-Pair Encoding.mp4 39.26 MB
38. GPT/5. Technical Details of GPT.mp4 51.4 MB
38. GPT/6. Playing with HuggingFace models.mp4 30.23 MB
4. Regularization and Normalization/1. Overfitting.mp4 26.27 MB
4. Regularization and Normalization/2. L1 and L2 Regularization.mp4 33.5 MB
4. Regularization and Normalization/3. Dropout.mp4 75.22 MB
4. Regularization and Normalization/4. DropConnect.mp4 14.18 MB
4. Regularization and Normalization/5. Normalization.mp4 13.54 MB
4. Regularization and Normalization/6. Batch Normalization.mp4 100.34 MB
4. Regularization and Normalization/7. Layer Normalization.mp4 45.48 MB
4. Regularization and Normalization/8. Group Normalization.mp4 26.46 MB
5. Optimization/1. Batch Gradient Descent.mp4 49.42 MB
5. Optimization/10. SWATS - Switching from Adam to SGD.mp4 9.81 MB
5. Optimization/11. Weight Decay.mp4 75.65 MB
5. Optimization/12. Decoupling Weight Decay.mp4 52.25 MB
5. Optimization/13. AMSGrad.mp4 85.64 MB
5. Optimization/2. Stochastic Gradient Descent.mp4 18.11 MB
5. Optimization/3. Mini-Batch Gradient Descent.mp4 6.94 MB
5. Optimization/4. Exponentially Weighted Average Intuition.mp4 22.92 MB
5. Optimization/5. Exponentially Weighted Average Implementation.mp4 43.15 MB
5. Optimization/6. Bias Correction in Exponentially Weighted Averages.mp4 30.92 MB
5. Optimization/7. Momentum.mp4 27.32 MB
5. Optimization/8. RMSProp.mp4 38.96 MB
5. Optimization/9. Adam Optimization.mp4 77.77 MB
6. Hyperparameter Tuning and Learning Rate Scheduling/1. Introduction to Hyperparameter Tuning and Learning Rate Recap.mp4 17.65 MB
6. Hyperparameter Tuning and Learning Rate Scheduling/2. Step Learning Rate Decay.mp4 62.86 MB
6. Hyperparameter Tuning and Learning Rate Scheduling/3. Cyclic Learning Rate.mp4 69.37 MB
6. Hyperparameter Tuning and Learning Rate Scheduling/4. Cosine Annealing with Warm Restarts.mp4 35.21 MB
6. Hyperparameter Tuning and Learning Rate Scheduling/5. Batch Size vs Learning Rate.mp4 24.72 MB
7. Weight Initialization/1. Normal Distribution.mp4 18.73 MB
7. Weight Initialization/2. What happens when all weights are initialized to the same value.mp4 59.96 MB
7. Weight Initialization/3. Xavier Initialization.mp4 109.71 MB
7. Weight Initialization/4. He Norm Initialization.mp4 13.32 MB
8. Introduction to PyTorch/1. CODE FOR THIS COURSE.mp4 1.78 MB
8. Introduction to PyTorch/10. Weight Initialization in PyTorch.mp4 65.88 MB
8. Introduction to PyTorch/2. Computation Graphs and Deep Learning Frameworks.mp4 55.23 MB
8. Introduction to PyTorch/3. Installing PyTorch and an Introduction.mp4 99.25 MB
8. Introduction to PyTorch/4. How PyTorch Works.mp4 147.44 MB
8. Introduction to PyTorch/5. Torch Tensors - Part 1.mp4 87.09 MB
8. Introduction to PyTorch/6. Torch Tensors - Part 2.mp4 67.94 MB
8. Introduction to PyTorch/7. Numpy Bridge, Tensor Concatenation and Adding Dimensions.mp4 75.07 MB
8. Introduction to PyTorch/8. Automatic Differentiation.mp4 76.4 MB
8. Introduction to PyTorch/9. Loss Functions in PyTorch.mp4 222.75 MB
9. Practical Neural Networks in PyTorch - Application 1 Diabetes/1. Part 1 Data Preprocessing.mp4 123.77 MB
9. Practical Neural Networks in PyTorch - Application 1 Diabetes/2. Part 2 Data Normalization.mp4 55.43 MB
9. Practical Neural Networks in PyTorch - Application 1 Diabetes/3. Part 3 Creating and Loading the Dataset.mp4 66.2 MB
9. Practical Neural Networks in PyTorch - Application 1 Diabetes/4. Part 4 Building the Network.mp4 170.51 MB
9. Practical Neural Networks in PyTorch - Application 1 Diabetes/5. Part 5 Training the Network.mp4 156.22 MB
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