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)