Best AI & Deep Learning Course Training in Singapore

Artificial Neural Network, also called MLP or Multilayer Perceptron is used to handle nonlinear problems. Understand the various network architectures along with different layers including input layers, hidden layers, output layers, etc. Also learn about the various activation functions, error functions, optimization algorithms including Batch Gradient Descent, Stochastic Gradient Descent, Mini-batch SGD, etc.

Understand working with videos and images because the amount of data getting generated in this space is outstripping the volume of textual data. Understand the various features to be extracted from images including edges, textures, etc., by applying various kinds of filters such as Sobel, Harris Corner Detector. Also, learn about face detection using Viola-Jones and tracking human faces in videos. Alongside this also learn about a few image-related models such as image segmentation, image recognition, etc.

Understand how to work with images and videos for building predictive models. Learn about convolution layers as well as handling very small datasets. Understand how to improve the accuracy of models by performing data augmentation activities. Also one should be aware of the use of pre-trained models using feature extraction, fine-tuning, etc., in solving business problems. Finally visualizing the activation layers and heat maps for activation will complete the study to the fullest.

Understand working with textual sequence data and how to perform a one-hot encoding of words and characters. Learn about bi-directional RNNs as well as deep bi-directional RNNs. Learn about various RNN topologies and network architectures. Vanishing and exploding gradient problems are very prevalent in the field of recurrent neural networks. Understand Backpropagation Through Time, which is a different but slight variation from the regular backpropagation algorithm.

Advanced techniques in handling textual and sequential data are LSTMs and GRUs. Also, understand about forecasting temperature. Learn about bi-directional LSTMs and deep bi-directional LSTMs . Also, understand the stacking of various recurrent layers. Stacking recurrent layers will improve accuracy and understanding the same is extremely pivotal for the success of AI algorithms. Also, learn about 1D convolution for time series data. Finally combining CNN and RNN models is an art, which is explained in detail.

Learn about the renowned unsupervised deep learning algorithm called Autoencoders. Understand about generating sentences using a combination of LSTM and Autoencoders. Also, learn about variational autoencoders for generating images and editing images. Another most used algorithm in the family of neural network algorithms is GANs. Learn about systematically implementing GAN. Learn about various elements of GANs including Deep Convolutional Generative Adversarial Network. A brief introduction to WaveNet, which is used to produce audio is also explained.

Board games such as Tic-Tac, Go, AlphaGo uses reinforcement learning algorithms to build unbeatable games. Learn how Artificial Intelligence games are built using Neural network algorithms. Maximizing future rewards is the key to building reinforcement learning. Learn how to balance between exploration as well as exploitation in Q-Learning.