22.21 GB | 01:04:27 | mp4 | 1280X720  | 16:9
Genre:eLearning |Language:English

Files Included :
1  Welcome  (21.29 MB)
2  General Introduction  (222.86 MB)
3  Course Content  (77.74 MB)
1  Data Logging  (287.15 MB)
2  Viewing Model Graphs  (21.5 MB)
3  Hyperparameter tuning  (194.95 MB)
4  Profiling and other visualizations with Tensorboard  (69.23 MB)
1  Experiment Tracking  (469.67 MB)
2  Hyperparameter Tuning with Weights and Biases and TensorFlow 2  (186.11 MB)
3  Dataset Versioning with Weights and Biases and TensorFlow 2  (329.36 MB)
4  Data Versioning with Wandb  (329.5 MB)
5  Model Versioning with Weights and Biases and TensorFlow 2  (137.45 MB)
2  Data Preparation  (225.41 MB)
3  Modeling and Training  (371.79 MB)
4  Data augmentation  (142.09 MB)
5  Tensorflow Records  (293.65 MB)
1  Alexnet  (183.52 MB)
2  Vggnet  (116.45 MB)
3  Resnet  (351.48 MB)
4  Coding Resnet  (180.36 MB)
5  Mobilenet  (206.77 MB)
6  Efficientnet  (189.24 MB)
1  Leveraging Pretrained Models  (163.38 MB)
2  Finetuning  (112.17 MB)
1  Visualizing intermediate layers  (157.97 MB)
2  Grad-cam Method  (226.85 MB)
1  Ensembling  (45.22 MB)
2  Class Imbalance  (100.61 MB)
1  Understanding VITs  (421.93 MB)
2  Building VITs from scratch  (398.59 MB)
3  Finetuning Huggingface Transformers  (206.49 MB)
4  Model Evaluation with Wandb  (140.23 MB)
5  Data efficient transformers  (72.86 MB)
6  Swin Transformers  (192.31 MB)
1  Model Conversion from Tensorflow to Onnx  (205.38 MB)
2  Understanding quantization  (159.78 MB)
3  Practical quantization of Onnx model  (64.99 MB)
4  Quantization Aware training  (160.52 MB)
5  Conversion to Tensorflow lite model  (154.58 MB)
6  What is an API  (127.97 MB)
7  Building the Emotions Detection API with Fastapi  (674.71 MB)
8  Deploy the Emotions Detection API to the Cloud  (103.37 MB)
9  Load tesing the Emotions Detection API with Locust  (106.28 MB)
2  Understanding object detection  (52.42 MB)
3  YOLO Paper  (571.91 MB)
4  Dataset Preparation  (401 MB)
5  YOLO Resnet  (53.95 MB)
6  YOLO Loss  (691.12 MB)
7  Data augmentation  (219.2 MB)
8  Testing  (308.94 MB)
9  Data generators  (51.43 MB)
10  String Tensors  (29.49 MB)
11  Tensorflow Variables  (25.62 MB)
2  Tensor Basics  (33.14 MB)
3  Tensor Initialization and Casting  (306.74 MB)
4  Indexing  (157.44 MB)
5  Maths Operations in Tensorflow  (217.22 MB)
6  Linear Algebra Operations in Tensorflow  (381.47 MB)
7  Common Tensorflow Methods  (214.15 MB)
8  Ragged Tensors  (96.8 MB)
9  Sparse Tensors  (19.48 MB)
10  Model Evaluation with FiftyOne  (258.54 MB)
11  Virtual Cloth Try-on with Stable Diffusion Inpainting  (218.75 MB)
12  Building FiftyOne Data Augmentation Plugin with Stable Diffusion Inpainting  (556.54 MB)
2  Problem Understanding  (35.83 MB)
3  Data Downloading  (35.11 MB)
4  Data Splitting  (118.89 MB)
5  Data Processing  (178.72 MB)
6  Data Visualization with Matplotlib  (69.72 MB)
7  Data Visualization with FiftyOne  (184.92 MB)
8  Understanding Segformer  (190.77 MB)
9  Model Creation  (183.3 MB)
2  People Counting - Shangai Tech Dataset  (83.51 MB)
3  Dataset Preparation  (323.88 MB)
4  CSRNET  (75.34 MB)
5  Training and Optimization  (51.85 MB)
6  Data Augmentation  (260.93 MB)
2  Introduction to Image generation  (27.26 MB)
3  Understanding Variational autoencoders  (117.51 MB)
4  VAE training and digit generation  (299.23 MB)
5  Latent space visualizations  (112.88 MB)
6  How GANs work  (232.5 MB)
7  The GAN Loss  (163.18 MB)
8  Improving GAN training  (168.74 MB)
9  Face generation with GANs  (411.8 MB)
1  Python Installation  (18.15 MB)
10  Encapsulation  (11.58 MB)
11  Polymorphism  (13.41 MB)
12  Decorators  (90.77 MB)
13  Generators  (46.59 MB)
14  Numpy Package  (207.96 MB)
15  Matplotlib Introduction  (21.53 MB)
2  Conditional Statements  (89.57 MB)
3  Variables and Basic Operators  (134.99 MB)
4  Loops  (93.23 MB)
5  Methods  (88.44 MB)
6  Objects and Classes  (59.14 MB)
7  Operator Overloading  (52.9 MB)
8  Method Types  (48.35 MB)
9  Inheritance  (57.33 MB)
10  Corrective Measures  (80.25 MB)
11  TensorFlow Datasets  (79.43 MB)
3  Task Understanding  (23.56 MB)
4  Data Preparation  (224.68 MB)
5  Linear Regression Model  (101.06 MB)
6  Error Sanctioning  (107.8 MB)
7  Training and Optimization  (132.36 MB)
8  Performance Measurement  (27.3 MB)
9  Validation and Testing  (178.06 MB)
10  Model Evaluation and Testing  (29.58 MB)
11  Loading and Saving Tensorflow Models to Google Drive  (128.9 MB)
2  Task Understanding  (56.87 MB)
3  Data Preparation  (155.88 MB)
4  Data Visualization  (18.8 MB)
5  Data Processing  (39.52 MB)
6  How and Why Convolutional Neural Networks work  (348.61 MB)
7  Building Convnets in Tensorflow  (44.7 MB)
8  Binary Crossentropy Loss  (57.44 MB)
9  Convnet Training  (64.63 MB)
1  Functional API  (138.4 MB)
2  Model Subclassing  (119.54 MB)
3  Custom Layers  (135.7 MB)
1  Precision,Recall and Accuracy  (211.32 MB)
2  Confusion Matrix  (62.37 MB)
3  ROC Curve  (50.19 MB)
1  Tensorflow Callbacks  (217.44 MB)
2  Learning rate scheduling  (136.87 MB)
3  Model checkpointing  (61.83 MB)
4  Mitigating Overfitting and Underfitting with Dropout, Regularization  (202 MB)
1  Data augmentation with TensorFlow using tf image and Keras Layers  (475.19 MB)
2  Mixup Data augmentation with TensorFlow 2 with intergration in tf data  (161.77 MB)
3  Cutmix Data augmentation with TensorFlow 2 and intergration in tf data  (344.06 MB)
4  Albumentations with TensorFlow 2 and PyTorch for Data augmentation  (197.33 MB)
1  Custom Loss and Metrics  (176.01 MB)
2  Eager and Graph Modes  (88.69 MB)
3  Custom Training Loops  (234.98 MB)
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