Artificial Intelligence Bundle - Computer Vision Special

Duration 88 h 7 m 19 s

Available 360 days

Price

₹ 250 ~~₹ 750~~ Buy now

Artificial Intelligence Bundle - Computer Vision Special

About Course

Learn basic of Filters to applying ML and then applying Deep Learning on Images.

Learn various approaches applied by our student on a project in “Student Practice Session”

For Theory - Watch Appendix.

Support :-

Join this whatsapp group for any issue https://chat.whatsapp.com/FeFvYqGUafi788iuoXJOnH or contact +91-7009247270 .

*COURSE VALIDITY- 1 YEAR.

*Course has multiple independent sections that can be skipped as per user expertise and choice.

Course content

Image processing and extraction56 m 2 s Start

Day 11 h 34 m 3 s Start

Day 21 h 24 m 43 s Start

Day 31 h 16 m 19 s Start

Day 41 h 28 m 49 s Start

Day 51 h 28 m 4 s Start

Deep Learning for Computer Vision0 s Start

Introduction0 sFree

Blog Link0 sFree

Introduction19 m 1 sFree

Introduction - 211 m 32 sFree

PCA - 13 m 28 s Start

PCA - 213 m 48 s Start

PCA - 312 m 13 s Start

PCA - 49 m 49 s Start

Eigen value and vector22 m 59 s Start

Dimensionality Reduction9 m 58 s Start

Visualize MNIST Dataset4 m 47 s Start

PCA Code0 s Start

PCA for dimensionality reduction14 m 44 s Start

t-SNE6 m 25 s Start

Embedding6 m 25 s Start

t-SNE (continue)8 m 8 s Start

Crowding Problem7 m 50 s Start

Apply t-SNE37 m 56 s Start

t-SNE on MNIST6 m 14 s Start

Code Example8 m 2 s Start

PROJECT - Face Recognition (Code )0 s Start

History24 m 23 sFree

Biological Neuron8 mFree

Biological Neuron - 216 m 7 s Start

Logistic Regression16 m 40 s Start

MLP0 s Start

Notation17 m 51 s Start

Single neuron training27 m 53 s Start

MLP Chain Rule39 m 57 s Start

MLP Memoization13 m 26 s Start

Backpropagation25 m 21 s Start

Activation Functions16 m 58 s Start

Vanishing Gradient22 m 17 s Start

Bias Variance9 m 52 s Start

Decision surfaces Playground14 m 17 s Start

MLP 1980s15 m 40 s Start

Dropout and Regularization20 m 8 s Start

ReLU27 m 17 s Start

Weight Intialization23 m 44 s Start

Batch Normalization20 m 7 s Start

Optimizer -10 s Start

Optimizer -212 m 49 s Start

SGD - 10 s Start

SGD - 224 m 51 s Start

NAS7 m 35 s Start

AdaGrad14 m 58 s Start

AdaDelta and RMSProp9 m 16 s Start

Adam10 m 29 s Start

Which one to choose5 m 2 s Start

Gradient Checking9 m 40 s Start

Softmax and Cross-Entropy24 m 22 s Start

Deep MLP Training7 m 38 s Start

Auto Encoders26 m 21 s Start

Word2Vec CBOW18 m 16 s Start

Word2Vec Skip gram13 m 48 s Start

Word2Vec Algorithmic Optimization11 m 27 s Start

Tensorflow and Keras - Introduction22 m 28 s Start

GPU vs CPU23 m 4 s Start

Softmax classifier31 m 49 sFree

MLP Initialization10 m 20 sFree

Sigmoid Activation0 sFree

Relu Activation5 m 59 sFree

Batch Normilization7 m 22 s Start

Dropout4 m 8 s Start

MNIST in Keras17 m 33 s Start

Hyperparamater Tuning10 m 18 s Start

Optimizer Choice0 s Start

Introduction17 m 4 s Start

Edge Detection27 m 48 sFree

Padding and Stride18 m 34 s Start

RGB Convolution10 m 46 s Start

Layers23 m 2 s Start

Max Pool11 m 49 s Start

Optimization8 m 35 s Start

LeNet9 m 42 s Start

ImageNet Dataset5 m 53 s Start

Augementation7 m 6 s Start

Convolution Layers16 m 43 s Start

AlexNet12 m 56 s Start

VGGNet10 m 32 s Start

Residual Network21 m 18 s Start

Inception Network18 m 7 s Start

Transfer Learning22 m 47 s Start

Cat Vs Dog14 m 15 s Start

MNIST5 m 9 s Start

Why RNN22 m 26 sFree

RNN28 m 58 sFree

RNN Backprop15 m 22 sFree

RNN Types13 m 49 s Start

LSTM GRU9 m 51 s Start

LSTM34 m 4 s Start

GRU6 m 26 s Start

Deep RNN6 m 16 s Start

Bidirectional RNN11 m 26 s Start

IMDB Sentiment Classification32 m 12 s Start

PROJECT - IMDB Sentiment Analysis (Code)0 s Start

References for resources0 s Start

PROJECT - Github Repo Link (CODE)0 s Start

Problem Definition13 m 34 sFree

Dataset8 m 30 sFree

Data Analysis4 m 17 s Start

Dash-cam images and steering angles5 m 22 s Start

Split the dataset Train vs Test2 m 53 s Start

EDA Steering angles6 m 4 s Start

Mean Baseline model simple4 m 25 s Start

Deep Learning for regression CNN, CNN+RNN9 m 18 s Start

Batch load the dataset5 m 53 s Start

NVIDIA’s end to end CNN model17 m 48 s Start

Train the model12 m 51 s Start

Test and visualize the output10 m 57 sFree

References for resources0 s Start

Problem statement14 m 23 s Start

Music representation17 m 6 s Start

Char-RNN - 122 m 34 s Start

Char-RNN - 240 m 12 s Start

Char-RNN -30 s Start

Char-RNN -411 m 9 s Start

Char-RNN -513 m Start

Char-RNN -610 m 50 s Start

Char-RNN -72 m 42 s Start

MIDI music generation3 m 48 s Start

Survey blog4 m 19 s Start

References for Resources0 s Start

Problem statement8 m 40 s Start

Dataset understanding21 m 35 s Start

Data cleaning & preprocessing3 m 36 s Start

EDA Univariate analysis4 m 44 s Start

EDA Data visualization using t-SNE4 m 45 s Start

Classical ML models12 m 45 s Start

Deep-learning Model14 m 12 s Start

Introduction0 sFree

Drive link for PPT and codes0 s Start

Part 12 h 8 m 14 s Start

Part 22 h 9 m 52 s Start

Part 12 h 28 m 15 sFree

Part 21 h 59 m 31 s Start

Part 32 h 1 m 37 s Start

Part 41 h 50 m 45 s Start

Image features2 h 52 m 59 s Start

ML on medical image1 h 27 m 36 s Start

Part 11 h 14 m 51 s Start

Part 21 h 7 m 52 s Start

Part 31 h 36 m 51 s Start

Part 41 h 5 m 27 s Start

Part 10 s Start

Part 20 s Start

Part 2 (continue)41 m 19 s Start

Part 31 h 41 s Start

Part 450 m 39 s Start

Part 51 h 35 m 22 s Start

Introduction0 sFree

Lecture Material0 s Start

Forecasting using Machine Learning Tools3 h 21 m 15 s Start

Introduction0 sFree

Teaching Material0 s Start

NLP Part - 16 h 42 m 1 s Start

NLP Part - 27 h 31 m 35 s Start

Resources0 s Start

Part 14 h 16 m 37 s Start

Part 24 h 28 m 2 s Start

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Course Instructor

Uteach Academy

UTeach Academy