2.98 GB | 00:22:10 | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English

Files Included :
1 - Introduction (11.34 MB)
27 - Different ways to create ANN using Keras (8.67 MB)
28 - Building the Neural Network using Keras (77.49 MB)
29 - Compiling and Training the Neural Network model (107.17 MB)
30 - Evaluating performance and Predicting using Keras (90.7 MB)
31 - Building Neural Network for Regression Problem (250.53 MB)
32 - Using Functional API for complex architectures (119.79 MB)
33 - Saving Restoring Models and Using Callbacks (245.61 MB)
34 - Hyperparameter Tuning (44.45 MB)
35 - CNN Introduction (32.01 MB)
36 - Stride (10.58 MB)
37 - Padding (20.03 MB)
38 - Filters and Feature maps (40.26 MB)
39 - Channels (113.57 MB)
40 - PoolingLayer (25.28 MB)
41 - CNN model in Python Preprocessing (51.82 MB)
42 - CNN model in Python structure and Compile (33.81 MB)
43 - CNN model in Python Training and results (70.18 MB)
44 - Comparison Pooling vs Without Pooling in Python (95.63 MB)
45 - Project Introduction (31.44 MB)
47 - Project Data Preprocessing in Python (93.75 MB)
48 - Project Training CNN model in Python (60.42 MB)
49 - Project in Python model results (16.62 MB)
50 - Project Data Augmentation Preprocessing (33.49 MB)
51 - Project Data Augmentation Training and Results (47.36 MB)
10 - Working with Numpy Library of Python (43.55 MB)
11 - Working with Pandas Library of Python (55.88 MB)
12 - Working with Seaborn Library of Python (37.76 MB)
3 - Installing Python and Anaconda (13.28 MB)
4 - This is a milestone (26.16 MB)
5 - Opening Jupyter Notebook (88.41 MB)
6 - Introduction to Jupyter (31.98 MB)
7 - Arithmetic operators in Python Python Basics (10.41 MB)
8 - Strings in Python Python Basics (81.09 MB)
9 - Lists Tuples and Directories Python Basics (48.61 MB)
52 - ILSVRC (13.66 MB)
53 - LeNET (4.8 MB)
54 - VGG16NET (6.79 MB)
55 - GoogLeNet (11.96 MB)
56 - Transfer Learning (19.7 MB)
57 - Project Transfer Learning VGG16 (204.52 MB)
59 - The final milestone (8.36 MB)
13 - Integrating ChatGPT with Jupyter notebook (52.36 MB)
14 - Perceptron (30.65 MB)
15 - Activation Functions (22.9 MB)
16 - Python Creating Perceptron model (118.39 MB)
17 - Basic Terminologies (26.87 MB)
18 - Gradient Descent (41.51 MB)
19 - Back Propagation (79.14 MB)
20 - Some Important Concepts (49.19 MB)
21 - Hyperparameters (30.75 MB)
22 - Keras and Tensorflow (10.19 MB)
23 - Installing Tensorflow and Keras (20.09 MB)
24 - Dataset for classification (70.38 MB)
25 - Normalization and TestTrain split (60.27 MB)