20.49 GB | 00:10:50 | mp4 | 1920X1080  | 16:9
Genre:eLearning |Language:English

Files Included :
1  Introduction  (38.68 MB)
2  What is An Artificial Neuron  (45.23 MB)
3  What is Deep Learning  (48.02 MB)
4  Neural Network Basics  (82.17 MB)
5  Convolutional Neural Networks (CNNs)  (107.19 MB)
6  Natural Language Processing (NLP)  (83.67 MB)
7  Challenge  (26.75 MB)
1  Introduction  (150.04 MB)
2  Binary to Multi-Class Classification  EDA  (85.44 MB)
3  Build, Compile, and Fit Model  (54.25 MB)
4  Evaluate for Overfitting (plot curves)  (106.46 MB)
5  Adjust HyperparametersData Augmentation  (70.52 MB)
6  Repeat Until Happy with Results  (159.24 MB)
1  Introduction  (218.29 MB)
2  Kaggle Milestone Challenge Overview  (176.68 MB)
3  Load Dataset with ImageDataGenerator  (130.7 MB)
4  Challenge  (33.57 MB)
5  Solution Video  (107.86 MB)
1  Introduction  (102.74 MB)
2  Explore Dataset and Clone Repo from CBTN GitHub  (57.85 MB)
3  Prepare Train Dataset for Reduction  (147.91 MB)
4  Reduce Train Dataset by 90% and Only Keep 10%  (81.17 MB)
5  CHALLENGE  (34.94 MB)
6  Solution  (93.66 MB)
1  Introduction  (144.92 MB)
10  Improved Learning Rate  (9.28 MB)
2  Review Reduced Food 10 Dataset (10%)  (41.27 MB)
3  Custom Callbacks  (67.96 MB)
4  TensorBoard Callbacks  (45.75 MB)
5  Checkpoint Callbacks  (29.68 MB)
6  Early Stopping Callbacks  (48.35 MB)
7  Callback Lists  (63.39 MB)
8  Challenge Introduction  (24.8 MB)
9  Challenge Solution  (66.8 MB)
1  Introduction  (107.53 MB)
2  Review Transfer Learning, Feature Extraction, and Fine-Tuning  (74.74 MB)
3  Review Food 10 Dataset  (59.85 MB)
4  Exploring Pre-Trained Models  (184.27 MB)
5  Apply Transfer Learning, Feature Extraction, and Fine-tuning  (64.17 MB)
6  Challenge  (49.09 MB)
7  Solution video  (65.45 MB)
1  Introduction  (127.07 MB)
2  Clone and Create Reduced Food 10 Dataset  (133.83 MB)
3  Apply Data Augmentation  (91.92 MB)
4  Create a custom function to build Keras models simply using URLs ��  (65.12 MB)
5  Build, compile, and train resnet model  (68.56 MB)
6  Challenge  (19.54 MB)
7  Solution video  (49.51 MB)
1  Introduction  (117 MB)
2  Explore Reduced Food 10 Dataset for Transfer Learning  (170.52 MB)
3  Create re-useable custom functions for rapid testing  (62.35 MB)
4  Import custom functions from CBT Nuggets GitHub repo  (65.37 MB)
5  Double Challenge ����  (81.84 MB)
6  Build a tensorflowresnet model from scratch  (94.77 MB)
7  Build a tensorflowefficientnet model from scratch  (65.42 MB)
1  Introduction  (141.26 MB)
2  Explore Fine-Tuning  (63.13 MB)
3  Explore Food 10 Dataset  (88.84 MB)
4  Import custom functions with !wget or !clone  (42.85 MB)
5  Create ResNet50 Model  (113.35 MB)
6  Train the Fine-Tuning layers of the model  (111.45 MB)
7  Challenge  (31.41 MB)
8  Solution Video  (154.08 MB)
1  Introduction  (76.93 MB)
2  Explore Fine-Tuning  (37.76 MB)
3  Explore Food 10 Dataset in three sizes  (70.49 MB)
4  Explore a new TensorFlow methods  (129.34 MB)
5  Explore Keras Applications Vs TensorFlow Hub  (127.32 MB)
6  Fine-Tune the Top 10 Unfrozen Layers  (94.7 MB)
7  Challenge  (26.97 MB)
8  Challenge Solution Video  (87.49 MB)
9  Update Completed ResNet50 Training & EfficientNetB0 Code  (159.02 MB)
1  Introduction  (73.46 MB)
2  From Pixels and CNN to Characters with NLP  (54.51 MB)
3  What is ASCII and Why Isn't Great for Encoding in NLP  (43.45 MB)
4  Using Basic Sequences to Understand Basic Encoding Principles  (49.56 MB)
5  What are Tokens and Tokenizers  (62.81 MB)
6  CHALLENGE ��  (25.49 MB)
7  Challenge Solution Video  (54.66 MB)
1  Introduction  (103.26 MB)
2  Google Colab  (121.55 MB)
3  Anaconda and Conda  (178.39 MB)
4  Jupyter Notebook  (84.31 MB)
5  PyCharm  (65.41 MB)
6  Visual Studio Code  (49.41 MB)
7  Challenge  (31.86 MB)
8  Solution Video  (39.96 MB)
1  Introduction  (110.22 MB)
2  Explore Similarities in Reading for Humans and Machines  (48.21 MB)
3  Apply Token Sequences Aiming for Coherent Outputs  (53.45 MB)
4  Handling Out-of Vocabulary words with OOV Tokens  (76.59 MB)
5  Adding Uniformity to Sentences with Padding  (58.14 MB)
6  CHALLENGE  (37.45 MB)
7  Challenge Solution  (76.32 MB)
1  Introduction  (116.64 MB)
2  Review Limitations of Song Lyrics Generator Bot  (113.16 MB)
3  Explore News Category Dataset and Preprocessing  (124.72 MB)
4  Apply Sequencing, OOV, and Padding  (83.85 MB)
5  Test User Article Title Input on Our Vocabulary with OOV in Mind  (83.1 MB)
6  CHALLENGE  (17.64 MB)
7  Challenge Solution Video  (103.04 MB)
1  Introduction  (90.62 MB)
2  Review Previous NLP Neural Network Classifier  (50.79 MB)
3  Explore TensorFlow Datasets and New Dataset  (64.19 MB)
4  Load IMDB Dataset and Convert to DataFrames  (49.8 MB)
5  Convert Data to Numpy Arrays and review sentences and labels  (60.44 MB)
6  Tokenizer, Sequences, OOV, and Padding and Embeddings  (137.65 MB)
7  CHALLENGE  (37.44 MB)
8  Challenge Solution Video  (46.98 MB)
1  Introduction  (64.39 MB)
2  Use Numpy instead of Pandas to Load Dataset  (65.72 MB)
3  Deep Dive Into Hyperparameters Tuning  (89.57 MB)
4  Deep Dive Into Text Embeddings  (101.95 MB)
5  Plot Loss and Accuracy Curves  (159.03 MB)
6  Analyze Sentiment with Embedding Projector  (63.06 MB)
7  CHALLENGE  (45.3 MB)
8  Solution Video  (88.22 MB)
1  Introduction  (98.16 MB)
2  Review TensorFlow Datasets & Explore Subwords and BSE  (96.4 MB)
3  CHALLENGE  (50.8 MB)
4  Solution Video A  (164.69 MB)
5, Solution Video B  (109.42 MB)
6  Solution Video C  (67.84 MB)
1  Introduction  (80.98 MB)
2  From Token Semantics to Sequential Coherence  (91.18 MB)
3  What is RNN and LSTM  (125.07 MB)
4  The Heart of Sequence Models Sequence Problems  (56.53 MB)
5  TensorFlow Modeling Action Steps  (104.1 MB)
6  Delve Deeper Into RNN and LSTM  (65.7 MB)
7  CHALLENGE  (19.3 MB)
8  Solution Video  (66.37 MB)
1  Introduction  (104.63 MB)
2  Static Token Vs Dynamic Embeddings  (40.93 MB)
3  NLP with Kaggle's Disaster Tweets Contest  (78.25 MB)
4  Exploratory Data Analysis with Pandas  (104.91 MB)
5  Data Visualization with Matplotlib and Seaborn  (74.28 MB)
6  CHALLENGE  (59.01 MB)
7  Solution Video  (60.08 MB)
1  Introduction  (53.97 MB)
2  Review Baseline TensorFlow Binary Disaster Classifier  (134.66 MB)
3  Load and Preprocess Dataset  (38.03 MB)
4  Clean Data Before Improving Model Architecture  (52.39 MB)
5  Clean Data Part 2  (92.06 MB)
6  CHALLENGE  (20.17 MB)
7  Solution Video  (86.45 MB)
1  Introduction  (134.23 MB)
2  Review Baseline Model Accuracy & Loss  (291.14 MB)
3   Add Random Dataset Shuffle & Hyperparameters  (61.81 MB)
4  Leverage Custom Functions  (24.09 MB)
5  Prepare Competition Output File submission csv  (25.34 MB)
6  CHALLENGE  (110.24 MB)
7  Solution Video  (68.19 MB)
1  Introduction  (81.22 MB)
2  Explore RNNs Models and the Vanishing Gradients Problem  (105.08 MB)
3  Explore Long Short-Term Memory (LSTM) Models  (41.75 MB)
4  Build a Single Layer Bidirectional LSTM Model  (62.89 MB)
5  Build a Multiple Layer Bidirectional LSTM Model  (45.74 MB)
6  Add Convolutions to LSTM Models to Capture Sequences of Words  (21.79 MB)
7  CHALLENGE  (14.89 MB)
8  Solution Video & Code  (96.93 MB)
1  Introduction  (94.4 MB)
2  What is Computer Vision(CV)  (121.29 MB)
3  Explore CV with the fashion MNIST dataset  (88.43 MB)
4  How does SoftMax work  (50.88 MB)
5  Normalizing and Standardization  (44.75 MB)
6  Challenge  (48 MB)
7  Solution Video  (86.53 MB)
1  Introduction  (70.48 MB)
2  Contrast LSTM, GRU, and Convolutional LSTM  (86.95 MB)
3  Build LSTM model  (101.64 MB)
4  Build GRU model  (13.57 MB)
5  Build Convolutional LSTM model  (51.15 MB)
6  CHALLENGE ��  (25.17 MB)
7  Solution Video  (70.61 MB)
1  Introduction  (59.91 MB)
2  Review Transfer Learning  (125.25 MB)
3  Searching for Models on TensorFlow Hub  (100.01 MB)
4  CHALLENGE  (72.66 MB)
5  Challenge Solution Video Part 1  (57.64 MB)
6  Challenge Solution Video Part 2  (76.27 MB)
1  Introduction  (126.71 MB)
2  Explore Generative Text Prediction  (92.11 MB)
3  Initialize and Fit Tokenizer  (21.83 MB)
4  Convert to Numerical Representation of the Corpus  (36.8 MB)
5  Generate and Return N-Gram Sequences  (43.15 MB)
6  Convert Padding Sequences to X and y  (82.4 MB)
7  Explore Tokenized Word Index  (44.25 MB)
8  CHALLENGE ��  (4.48 MB)
9  Solution Video  (15.28 MB)
1  Introduction  (98.66 MB)
2  Build, Compile and Train Model  (65.17 MB)
3  Plot the Accuracy and Loss Curves  (61.6 MB)
4  Add Bidirectional(LSTM) and Plot Curves  (64.36 MB)
5  Create a Text Prediction Sequence Model  (55.58 MB)
6  CHALLENGE ��  (50.65 MB)
7  Solution Video  (56.43 MB)
1  Introduction  (73.07 MB)
2  What is Univariate Time Series  (79.95 MB)
3  What is Multivariate Time Series  (44.34 MB)
4  Trends  (20.68 MB)
5  Seasonality  (41.45 MB)
6  Autocorrelation  (26.08 MB)
7  Noise  (43.09 MB)
1  Introduction  (37.93 MB)
2  Explore Time Series Forecasting Basics  (32.81 MB)
3  Create and Visualize Synthetic Dataset  (15.07 MB)
4  Prepare Data for Training using a windowed dataset  (75.49 MB)
5  Review Model Architecture  (61.22 MB)
6  CHALLENGE ��  (52.95 MB)
7  Challenge Solution Video  (91.85 MB)
1  Introduction  (28.22 MB)
2  Review DNN Forecasting  (19.01 MB)
3  CHALLENGE 1 ��  (73.5 MB)
4  Complete code challenge  (43.68 MB)
5  Build DNN model  (66.49 MB)
6  Review Recurrent Neural Networks  (25.4 MB)
7  CHALLENGE 2 ��  (24.71 MB)
8  Complete code challenge  (45.23 MB)
9  Build RNN model  (96.35 MB)
1  Introduction  (35.53 MB)
10  Solution Video  (34.97 MB)
2  Review DNN Model Architecture  (14.07 MB)
3  �� CHALLENGE 1 Build DNN Forecasting Model  (33.23 MB)
4  Solution Video  (94.46 MB)
5  Review RNN Model Architecture  (13.88 MB)
6  �� CHALLENGE 2 Build RNN Forecasting Model  (10.39 MB)
7  Solution Video  (21.6 MB)
8  Review LSTM Model Architecture  (9.11 MB)
9  �� CHALLENGE 3 Build LSTM Forecasting Model  (9.01 MB)
1  Introduction  (26.13 MB)
2  Review Kaggle Sunspot Dataset & CBT Nuggets GitHub Repo  (66.63 MB)
3  Build Baseline DNN Forecasting Model Part 1  (67.75 MB)
4  Build Baseline DNN Forecasting Model Part 2  (67.42 MB)
5  Review LSTMCNN Model Architecture  (35.98 MB)
6  �� CHALLENGE Build LSTMCNN Forecasting Model  (15.35 MB)
7  Solution Video  (99.53 MB)
1  Introduction  (22.99 MB)
2  Load & EDA MNIST Dataset  (93.23 MB)
3  Callbacks Part 1  (66.23 MB)
4  Callbacks Part 2  (93.96 MB)
5  Convolution & Pooling  (142.31 MB)
6  Challenge  (42.96 MB)
7  Solution Video  (111.45 MB)
1  Introduction  (70.97 MB)
2  Explore the Food 101 dataset on Kaggle  (70.96 MB)
3  Explore the modified ramen sushi dataset  (129.97 MB)
4  Load dataset using ImageDataGenerator  (83.09 MB)
5  Visualize random images with the labels  (82.38 MB)
6  Challenge  (73.79 MB)
7  Solution Video Part 1  (91.6 MB)
8  Solution Video Part 2  (113.58 MB)
1  Introduction  (72.07 MB)
2  Explore CNNs in a Browser  (132.34 MB)
3  What is a baseline model  (79.49 MB)
4  Deep Neural Networks (DNNs)  (108.75 MB)
5  Convolutional Neural Networks (CNNs)  (191.66 MB)
6  Challenge  (60.07 MB)
7  Solution Video  (128.44 MB)
1  Introduction  (69.51 MB)
2  Real-world scenario Teachable Machine Proof of Concept  (118.03 MB)
3  Real-world scenario Teachable Machine Proof of Concept Part 2  (103.88 MB)
4  Real-world scenario Acquire and Upload Images Part 1  (90.01 MB)
5  Real-world scenario Acquire and Upload Images Part 2  (109.96 MB)
6  Challenge  (28.28 MB)
7  Solution Video  (51.2 MB)
1  Introduction  (130.31 MB)
2  Baseline Model  (136.35 MB)
3  Part 2  (119.45 MB)
4  CNN Model  (86.96 MB)
5  Improvements  (127.94 MB)
6  Challenge  (30.97 MB)
7  Challenge Solution  (123.88 MB)
1  Introduction  (77.21 MB)
2  Explore Overfitting  (85.52 MB)
3  Load Dataset  (107.13 MB)
4  Challenge 1 Build and Train a Baseline Model from Pseudocode  (104.21 MB)
5  Plot Training Curves  (37.01 MB)
6  Reducing Overfitting  (28.55 MB)
7  Challenge  (17.89 MB)
8  Solution Video  (100.8 MB)
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