Deep Learning Training Provided by learntek
Deep Learning free videos and free material uploaded by learntek staff .
Introduction to Deep learning
AI and Deep learning
Advantage of DL
Deep Learning Primitives
Deep Learning Architecture
The Neural viewpoint
The Representation Viewpoint
TensorFlow Fundamentals
Introduction of Tensors
Installation of Tensors
Scalars, Vectors, and Matrices
Matrix Mathematics
Initializing Constant Tensors
Basic Computation using TensorFlow
Sampling Random Tensors
Tensor Addition and Scaling
Matrix Operation
Tensor Shape Manipulation
Tensor Types
TensorFlow Graphs
TensorFlow Sessions
TensorFlow Variable
Logistic Regression Model Building and Training
Introduction to Neural Network
Basic Neural Network
The Neurons
Single Hidden Layer Model
Multiple Hidden Layer Model
Input, Output, Hidden Layers
Details of Activation Functions: Sigmoid Function Hyperbolic
Tangent
Function, SoftMax
Selection of Right Activation Functions
Network learning technique
Weight initialization
Forward Propagation
Backpropagation
Optimization Algorithms
Regularization
Linear and Logistic Regression with TensorFlow
Overview of Linear and Logistic Regression
Loss Functions
Gradient Descent
Automatic Differentiation Systems
Learning with TensorFlow
Training Linear and Logistic Regression model
Evaluating Model Accuracy
Convolutional Neural Networks
Visual Cortex Architecture
Convolutional Layer
Filters
Stacking Multiple Feature Maps
TensorFlow Implementation
Pooling/Subsampling
Fully Connected Layer
MNIST digit classification example
Recurrent Neural Networks
Recurrent Neurons
Memory cells
Input and Output Sequences
Basic RNNs in TensorFlow
Static Unrolling through Time
Dynamic Unrolling through Time
Handling Variable Length Input/Output Sequence
Training RNNs
Creative RNNs
Deep RNNs
Distributing a Deep RNN Across Multiple GPUs
The Difficulty of Training over many Time Steps
Reinforcement Learning
Policy Search
Introduction to OpenAI Gym
Neural Network Policies
The Credit Assignment Problem
Policy Gradients
Markov Decision Process
Temporal Difference Learning and Q-Learning
Approximate Q-Learning and Deep Q-Learning
Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. It is intersection of statistics, artificial intelligence, and data to build accurate models. TensorFlow is one of the newest and most comprehensive libraries for implementing deep learning. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible.
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